index.xml

<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MLO Lab</title><link>https://mlo-lab.github.io/</link><atom:link href="https://mlo-lab.github.io/index.xml" rel="self" type="application/rss+xml"/><description>MLO Lab</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><copyright>© 2026 MLO Lab</copyright><lastBuildDate>Mon, 01 Jan 2024 00:00:00 +0000</lastBuildDate><image><url>https://mlo-lab.github.io/media/logo_hu_9ab0b13d2686246a.png</url><title>MLO Lab</title><link>https://mlo-lab.github.io/</link></image><item><title>FedAgree: Label-Free Performance Estimation under Distribution Shift for Federated Medical Analysis</title><link>https://mlo-lab.github.io/publication/serra-2026-fedagree/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/serra-2026-fedagree/</guid><description/></item><item><title>Fine-grained uncertainty decomposition in large language models: A spectral approach</title><link>https://mlo-lab.github.io/publication/walha-2026-fine/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/walha-2026-fine/</guid><description/></item><item><title>From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty</title><link>https://mlo-lab.github.io/publication/jenane-2026-entropy/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/jenane-2026-entropy/</guid><description/></item><item><title>Improving perturbation-based explanations by understanding the role of uncertainty calibration</title><link>https://mlo-lab.github.io/publication/decker-2026-improving/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/decker-2026-improving/</guid><description/></item><item><title>Interpretable multi-omics integration across mixed-order tensors with MANTRA</title><link>https://mlo-lab.github.io/publication/deazevedooruc-2026-mantra/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/deazevedooruc-2026-mantra/</guid><description/></item><item><title>LVLM-Aided Alignment of Task-Specific Vision Models</title><link>https://mlo-lab.github.io/publication/koebler-2026-lvlm/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/koebler-2026-lvlm/</guid><description/></item><item><title>Non-Contrastive Vision-Language Learning with Predictive Embedding Alignment</title><link>https://mlo-lab.github.io/publication/kuhn-2026-noncontrastive/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/kuhn-2026-noncontrastive/</guid><description/></item><item><title>PACMON: Pathway-guided Multi-Omics data integration for interpreting large-scale perturbation screens</title><link>https://mlo-lab.github.io/publication/qoku-2026-pacmon/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/qoku-2026-pacmon/</guid><description/></item><item><title>Pathogenesis of diffuse large B cell lymphoma proteogenotypes</title><link>https://mlo-lab.github.io/publication/enssle-2026-pathogenesis/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/enssle-2026-pathogenesis/</guid><description/></item><item><title>Toward trustworthy healthcare AI: designing academic research for translation readiness</title><link>https://mlo-lab.github.io/publication/hekler-2026-toward/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/hekler-2026-toward/</guid><description/></item><item><title>Tumor-associated epilepsy and high expression of xCT shape the proteome of IDH-wildtype glioblastoma</title><link>https://mlo-lab.github.io/publication/dive-2026-tumor/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/dive-2026-tumor/</guid><description/></item><item><title>USP22 is a novel vulnerability regulating MEIS1 protein abundance and gene transcription in KMT2Ar acute leukemia</title><link>https://mlo-lab.github.io/publication/brochtel-2026-usp-22/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/brochtel-2026-usp-22/</guid><description/></item><item><title>Quantitative Imaging in Oncology</title><link>https://mlo-lab.github.io/project/medical-imaging/</link><pubDate>Wed, 24 Sep 2025 15:55:09 +0200</pubDate><guid>https://mlo-lab.github.io/project/medical-imaging/</guid><description>&lt;p&gt;Quantitative imaging in oncology combines advanced microscopy, image analysis and machine learning to study cancer in unprecedented detail. By extracting rich spatial and molecular information from tissues, we aim to better understand how tumors grow, respond to treatments and interact with their surroundings. In our lab, we have developed colocatome frameworks to map and quantify in situ cellular organization, revealing how microenvironments regulate cell behavior. In parallel, we extract reproducible radiomic features such as texture, intensity and shape, together with quantitative MRI metrics like relaxation times, and apply interpretable machine-learning models to link these imaging biomarkers to clinical outcomes. This enables precise tumor localization and non-invasive monitoring of disease progression.&lt;/p&gt;
&lt;h3 id="quantitative-imaging-and-spatial-analysis"&gt;Quantitative Imaging and Spatial Analysis&lt;/h3&gt;
&lt;p&gt;Our work focuses on developing computational frameworks to analyze high-resolution multiplex microscopy images of tissues such as bone marrow, generated by &lt;a href="https://www.kokkaliarislab.com/" target="_blank" rel="noopener"&gt;Quantitative Spatial Cancer Biology - Kokkaliaris lab&lt;/a&gt;. We specialize in extracting spatial and morphological features from complex, multi-modal image data.&lt;/p&gt;
&lt;p&gt;We have developed a framework that enables the integration and analysis of multiple biological replicates with complementary information in a shared spatial reference space. Building on this, we established a pipeline to extract and quantify spatial remodeling of the cellular neighborhood during the aging process. These tools allow us to investigate how hematopoietic stem cells, blood vessels, megakaryocytes, adipocytes, and stromal components are spatially organized and how these patterns evolve with age or in response to treatment.&lt;/p&gt;
&lt;p&gt;
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="Bone Marrow Tissue" srcset="
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src="https://mlo-lab.github.io/project/medical-imaging/bone_marrow_hu_21b436252acdc00b.webp"
width="760"
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loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;/p&gt;
&lt;h3 id="radiomics-and-quantitative-mri"&gt;Radiomics and Quantitative MRI&lt;/h3&gt;
&lt;p&gt;Our imaging research also includes radiological data analysis, with a focus on transitioning from qualitative interpretation to quantitative, reproducible metrics. In radiomics, we extract features such as texture, intensity, and shape from defined regions of interest within MRI scans, and use machine learning models to link these features to clinical outcomes. We emphasize interpretability and robustness, testing models on controlled environments to avoid confounding due to real-patient variability and ethical exposure limitations.&lt;/p&gt;
&lt;p&gt;In parallel, we employ quantitative MRI (qMRI) techniques to move beyond traditional contrast-based imaging. By measuring intrinsic physical properties of tissues, e.g. relaxation times, we obtain microstructural insights into tissue composition, particularly in brain imaging. Combining qMRI with machine learning enables precise localization of tumors and assessment of disease progression, further enhancing the clinical utility of radiological data.&lt;/p&gt;</description></item><item><title>Learning interpretable representations of single-cell multi-omics data with multi-output Gaussian processes</title><link>https://mlo-lab.github.io/publication/10-1093-nargkaf-630/</link><pubDate>Tue, 01 Jul 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/10-1093-nargkaf-630/</guid><description/></item><item><title>Trustworthy Machine Learning in Biomedical Research</title><link>https://mlo-lab.github.io/project/trustworthy-ml/</link><pubDate>Tue, 10 Jun 2025 22:46:01 +0200</pubDate><guid>https://mlo-lab.github.io/project/trustworthy-ml/</guid><description>&lt;p&gt;As machine learning becomes increasingly central to biomedical discovery and clinical decision-making, ensuring the reliability, fairness, and interpretability of these models is critical. In our lab, we are committed to developing and applying machine learning methods that are not only accurate but also &lt;strong&gt;trustworthy&lt;/strong&gt;, meaning they are robust to noise, generalizable across datasets, transparent in their decision-making, and aligned with ethical and clinical standards.&lt;/p&gt;
&lt;p&gt;Our work spans multiple aspects of trustworthy ML, including uncertainty quantification, model calibration, interpretability, fairness in predictive models, and robustness to distributional shifts. These components are especially important in healthcare, where decisions influenced by models can have direct consequences for patients.&lt;/p&gt;
&lt;p&gt;In the context of multi-omics data, single-cell analysis, and quantitative imaging, we embed trustworthiness principles throughout the model development pipeline, from data preprocessing and integration to prediction and interpretation. This ensures that our computational outputs can be confidently used to guide biological insight and translational applications.&lt;/p&gt;
&lt;h3 id="model-calibration-under-distribution-shift"&gt;Model Calibration Under Distribution Shift&lt;/h3&gt;
&lt;p&gt;Current-generation neural networks exhibit systematic underconfidence rather than the overconfidence reported in earlier models, and demonstrate improved calibration robustness under distribution shift. However, post-hoc calibration methods become less effective or even detrimental under severe shifts. Our analysis across ImageNet and biomedical datasets reveals that calibration insights from web-scraped benchmarks have limited transferability to specialized domains, where convolutional architectures consistently outperform transformers regardless of model generation. This work challenges established calibration paradigms and emphasizes the need for domain-specific architectural evaluation beyond standard benchmarks.
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="Distribution Shift Calibration" srcset="
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src="https://mlo-lab.github.io/project/trustworthy-ml/distribution_shift_post-hoc_hu_c1d3926264e3302e.webp"
width="760"
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loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;/p&gt;
&lt;p&gt;[&lt;a href="https://arxiv.org/abs/2506.09593" target="_blank" rel="noopener"&gt;pdf&lt;/a&gt;, &lt;a href="https://github.com/MLO-lab/ModelTransformer" target="_blank" rel="noopener"&gt;repo&lt;/a&gt;]&lt;/p&gt;
&lt;h3 id="uncertainty-quantification-for-classification-and-applications"&gt;Uncertainty Quantification for Classification and Applications&lt;/h3&gt;
&lt;p&gt;Reliably estimating the uncertainty of a prediction throughout the model lifecycle is crucial in many safety-critical applications. Since ML-based decision models are increasingly deployed in dynamic environments, understanding when and why a model might fail becomes as important as achieving accurate predictive performance. In our group, we focus on developing theoretically sounded methods for uncertainty quantification that remain robust across different applications, enabling more trustworthy and transparent AI systems.
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="BI" srcset="
/project/trustworthy-ml/BI_plot_hu_9a5bd689a08163f6.webp 400w,
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src="https://mlo-lab.github.io/project/trustworthy-ml/BI_plot_hu_9a5bd689a08163f6.webp"
width="760"
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loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;/p&gt;
&lt;h4 id="uncertainty-estimates-of-predictions-via-a-general-bias-variance-decomposition-aistats-2023"&gt;Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition (AISTATS 2023)&lt;/h4&gt;
&lt;p&gt;Proper scoring rules (e.g., Brier score or negative log-likelihood) are commonly used as loss functions in machine learning, as they are designed to assign optimal predictions to the target distribution. However, it remains unclear how to decompose these scores in a way that a component capturing the model’s predictive uncertainty arises. To address this, we derive a general bias-variance decomposition for proper scoring rules, where the Bregman Information (BI) naturally emerges as the variance term. This new theoretical insight has practical implications for classification tasks: since the decomposition applies to the cross-entropy loss, it allows us to quantify predictive uncertainty directly in the logit space (the standard output of neural networks) without requiring a normalisation step. Extensive empirical results demonstrate the effectiveness and robustness of this method, particularly in out-of-distribution settings. [&lt;a href="https://proceedings.mlr.press/v206/gruber23a/gruber23a.pdf" target="_blank" rel="noopener"&gt;pdf&lt;/a&gt;, &lt;a href="https://github.com/MLO-lab/Uncertainty_Estimates_via_BVD" target="_blank" rel="noopener"&gt;repo&lt;/a&gt;]&lt;/p&gt;
&lt;h4 id="how-to-leverage-predictive-uncertainty-estimates-for-reducing-catastrophic-forgetting-in-online-continual-learning-tmlr-2025"&gt;How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning (TMLR 2025)&lt;/h4&gt;
&lt;p&gt;In many real-world scenarios, we want models to continuously learn new information without forgetting what they already know. In memory-based online continual learning, a key challenge is managing a limited memory buffer to mitigate catastrophic forgetting (CF) — but what is the best strategy for selecting samples to store in the memory? Under an uncertainty lens, we investigate what characteristics make samples effective in alleviating CF. Starting from the examination of the properties and behaviours of popular uncertainty estimates, we identify that they mostly capture the irreducible aleatoric uncertainty and hypothesise that a better strategy should focus on the epistemic uncertainty instead. To this end, we propose using Bregman Information – derived from our general bias-variance decomposition of strictly proper scores – as an effective estimator of epistemic uncertainty, leading to improved memory population strategy and reduced forgetting. [&lt;a href="https://openreview.net/pdf?id=dczXe0S1oL" target="_blank" rel="noopener"&gt;pdf&lt;/a&gt;, &lt;a href="https://github.com/MLO-lab/uncertainty_estimates_for_CF" target="_blank" rel="noopener"&gt;repo&lt;/a&gt;]&lt;/p&gt;
&lt;h4 id="federated-continual-learning-goes-online-uncertainty-aware-memory-management-for-vision-tasks-and-beyond--iclr-2025"&gt;Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond (ICLR 2025)&lt;/h4&gt;
&lt;p&gt;Federated Continual Learning (FCL) is a powerful paradigm that combines the privacy-preserving benefits of Federated Learning (FL) with the ability to learn sequentially over time, as in Continual Learning (CL). However, catastrophic forgetting still remains a major challenge. Most existing FCL methods rely on generative models, assuming an offline setting where all task data are available beforehand. But in real-world applications, data often arrives sequentially in small chunks — a challenge that remains largely unaddressed. To address this, we introduce a novel framework for online federated continual learning. To address scenarios where storing the full dataset locally is impractical, we propose an effective memory-based baseline that integrates uncertainty-aware updates — based on Bregman Information — with random replay to reduce catastrophic forgetting. Unlike generative approaches, our uncertainty-based solution is simple to implement and adaptable across different data modalities. [&lt;a href="https://openreview.net/pdf?id=f65RuQgVlp" target="_blank" rel="noopener"&gt;pdf&lt;/a&gt;, &lt;a href="https://github.com/MLO-lab/online-FCL" target="_blank" rel="noopener"&gt;repo&lt;/a&gt;]&lt;/p&gt;
&lt;h3 id="uncertainty-quantification-for-generative-ai"&gt;Uncertainty Quantification for Generative AI&lt;/h3&gt;
&lt;p&gt;Generative AI models in general and large language models in particular have emerged as a disruptive technology that has been rapidly democratized. Their use in critical domains such as medicine, scientific research, and politics has raised serious concerns about reliability. Consequently, robust estimation of their uncertainty is essential to build trust and to prevent the potentially severe consequences of failures. We develop methods to quantify and calibrate their uncertainty across different modalities, while accounting for the specific characteristics of each modality.
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="UQ_LLM" srcset="
/project/trustworthy-ml/UQ_LLM_plot_hu_297efdc9d9ab26be.webp 400w,
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src="https://mlo-lab.github.io/project/trustworthy-ml/UQ_LLM_plot_hu_297efdc9d9ab26be.webp"
width="760"
height="383"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;/p&gt;
&lt;h4 id="a-bias-variance-covariance-decomposition-of-kernel-scores-for-generative-models-icml-2024"&gt;A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models (ICML 2024)&lt;/h4&gt;
&lt;p&gt;This paper tackles a core gap in generative AI: there’s no unified, theory-grounded way to assess generalization and uncertainty across modalities or closed-source models. We introduce the first bias–variance–covariance decomposition for kernel scores, yielding kernel-based measures that can be estimated directly from generated samples, without access to the underlying model. Because kernels work from samples alone and are computed based on vector representations of these samples, the framework applies uniformly to images, audio, and language. In experiments, the approach explains generalization behavior (including mode collapse patterns) and delivers stronger uncertainty signals, even for closed-source LLMs.&lt;/p&gt;
&lt;p&gt;[&lt;a href="https://proceedings.mlr.press/v235/gruber24a.html" target="_blank" rel="noopener"&gt;pdf&lt;/a&gt;, &lt;a href="https://github.com/MLO-lab/BVCD_generative_models" target="_blank" rel="noopener"&gt;repo&lt;/a&gt;]&lt;/p&gt;</description></item><item><title>Computational Molecular Medicine</title><link>https://mlo-lab.github.io/project/comp-medicine/</link><pubDate>Tue, 10 Jun 2025 22:36:09 +0200</pubDate><guid>https://mlo-lab.github.io/project/comp-medicine/</guid><description>&lt;p&gt;Single-cell multi-omics data offer powerful opportunities to study disease at unprecedented resolution, but they also present significant challenges. The data are often sparse, noisy, and extremely high-dimensional, with technical differences between batches or donors that can obscure true biological signals. Our lab combines advanced computational methods with high-dimensional biological data to exludes technical artifacts and uncover mechanisms of disease progresssion and therapy response, with a particular emphasis on cancer and metabolic disorder.&lt;/p&gt;
&lt;h3 id="single-cell-multi-omics-for-clinical-cohorts"&gt;Single-Cell Multi-Omics for Clinical Cohorts&lt;/h3&gt;
&lt;p&gt;We specialize in the analysis of clinical single-cell multi-omics datasets, particularly from cancer and metabolic disease cohorts. Using state-of-the-art machine learning techniques, we analyze single-cell RNA sequencing and chromatin accessibility data to characterize cellular heterogeneity and regulatory dynamics. Our pipeline includes robust dimensionality reduction, clustering, and batch correction methods, allowing us to identify distinct cell populations and states across individuals. Through probabilistic graphical modeling and motif enrichment analysis, we reconstruct gene regulatory networks that govern disease-specific transcriptional programs. These approaches allow us to overcome the sparsity and noise inherent to single-cell data and extract biologically meaningful patterns that inform prognosis and therapeutic strategy.&lt;/p&gt;
&lt;h3 id="multi-omics-for-mouse-models-of-cancer-progression"&gt;Multi-Omics for Mouse Models of Cancer Progression&lt;/h3&gt;
&lt;p&gt;To explore tumor development and treatment effects in vivo, we analyze multi-omics data generated from mouse models, including xenografts and genetically induced cancers. These datasets are complex, encompassing multiple axes of variation such as treatment regimens, time points, and tumor subtypes. To disentangle these factors, we develop tailored probabilistic latent variable models (LVMs) that reveal how sources of variability interact and which molecular features are relevant to human disease.
Recent projects:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;
&lt;p&gt;Multi-Omics combined with &lt;strong&gt;lineage tracing&lt;/strong&gt; technology now allow us to quantify the clonal connectivity between different cell populations and infer the temporal dynamics of cell populations. Using mechanistic modeling, we can uncover the directionality of differentiation trajectories and the dynamical properties of the clones [&lt;a href="https://www.biorxiv.org/content/10.1101/2025.09.10.674954v1.full.pdf" target="_blank" rel="noopener"&gt;pdf&lt;/a&gt;].&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;
&lt;p&gt;Analyzing cancerous mouse models provides valuable insights for developing &lt;strong&gt;personalized oncology&lt;/strong&gt; approaches. We will integrate patient data at an early stage in this process using a forward and reverse translation technique. This method ensures that the results are clinically relevant and enables us to identify patients who are eligible for a new treatment. For example, we are working on a TRR project that investigates how ubiquitination impacts DNA damage repair in AML in order to identify a new anticancer target [&lt;a href="https://ubiqancer.de/project/a13/" target="_blank" rel="noopener"&gt;project&lt;/a&gt;].&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Interpretable Integration of Multi-Omics Data</title><link>https://mlo-lab.github.io/project/multi-omics/</link><pubDate>Tue, 10 Jun 2025 22:11:00 +0200</pubDate><guid>https://mlo-lab.github.io/project/multi-omics/</guid><description>&lt;p&gt;Understanding the complexity of cancer requires methods that can integrate equally complex biological data. In our lab, we are committed to developing probabilistic models that bring together multiple molecular layers, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, to provide a holistic view of each patient. These models uncover hidden structure by capturing both shared and modality-specific variation, allowing us to reduce noise and reveal biologically meaningful patterns. By modeling system-level responses to perturbations such as drug treatments or environmental changes, we aim to generate representations that are not only statistically robust but also interpretable, enabling new biological insights that can be directly validated and translated into clinical understanding.&lt;/p&gt;
&lt;h3 id="muvi"&gt;MuVI&lt;/h3&gt;
&lt;p&gt;MuVI is a general-purpose probabilistic latent variable model for multi-omics integration that incorporates prior biological knowledge into its structure. It uses pathway annotations, gene sets, or cell-type signatures to guide the discovery of latent factors that explain variation across different data types. Even when this prior knowledge is noisy or incomplete, MuVI is able to learn biologically relevant dimensions, enabling scientists to interpret the sources of variation in the data more clearly and to relate them to known mechanisms.
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="MuVI" srcset="
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src="https://mlo-lab.github.io/project/multi-omics/muvi_hu_e2cef2737c7dad40.webp"
width="760"
height="360"
loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;/p&gt;
&lt;h3 id="momo-gp"&gt;MOMO-GP&lt;/h3&gt;
&lt;p&gt;MOMO-GP (Multi-Omic Multi-output Gaussian Processes) addresses the challenge of learning interpretable representations from single-cell multi-omics data, which are typically high-dimensional, sparse, and nonlinear. Unlike traditional methods that trade off interpretability for modeling power, MOMO-GP combines neural networks with Gaussian Processes to achieve both. It learns separate latent embeddings for cells and features, as well as shared and modality-specific components in the multi-view setting. By modeling gene relevance explicitly, MOMO-GP connects cell clusters to marker genes, making the learned structure readily interpretable in biological terms.&lt;/p&gt;
&lt;h3 id="joana"&gt;JOANA&lt;/h3&gt;
&lt;p&gt;JOANA is a probabilistic model for pathway enrichment analysis (PEA) that overcomes limitations of classical approaches like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS). While methods such as GSEA work with continuous scores, they typically operate on a single omics layer and can yield overly broad sets of enriched pathways. JOANA improves on this by modeling enrichment scores across multiple omics layers using mixtures of beta distributions within a Bayesian framework. This allows it to estimate the probability of pathway enrichment both within and across modalities, yielding higher precision and more biologically relevant results.&lt;/p&gt;
&lt;h3 id="mofa-flex"&gt;MOFA-FLEX&lt;/h3&gt;
&lt;p&gt;MOFA-FLEX is our upcoming framework for flexible and interpretable multi-omics integration. Designed to generalize the principles behind models like MuVI and MUSIC, MOFA-FLEX supports heterogeneous data types, modular priors, and scalable inference. Its architecture allows for tailored modeling of real-world datasets, balancing interpretability with modeling flexibility. MOFA-FLEX is currently under active development and will provide a unified foundation for future applications in cancer biology and beyond.
&lt;figure &gt;
&lt;div class="d-flex justify-content-center"&gt;
&lt;div class="w-100" &gt;&lt;img alt="MOFA-FLEX" srcset="
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src="https://mlo-lab.github.io/project/multi-omics/mofaflex_hu_99b5cd912129df74.webp"
width="760"
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loading="lazy" data-zoomable /&gt;&lt;/div&gt;
&lt;/div&gt;&lt;/figure&gt;
&lt;/p&gt;
&lt;h3 id="pacmon"&gt;PACMON&lt;/h3&gt;
&lt;p&gt;PACMON is a scalable Bayesian factor model for interpreting high-throughput single-cell perturbation screens. It uses pathway priors to infer molecular programs and quantify how genetic or chemical perturbations affect these programs across modalities. Applied to multimodal Perturb-CITE-seq data and atlas-sized chemical perturbation screens, PACMON identifies coherent immune-, cell-state-, and drug-response programs, linking perturbations to pathway-level molecular changes in an interpretable and scalable way.&lt;/p&gt;
&lt;h3 id="mantra"&gt;MANTRA&lt;/h3&gt;
&lt;p&gt;MANTRA (Multi-view ANalysis with Tensor and matRix Alignment) is a Bayesian framework for integrating matrices and higher-order tensors in multi-omics studies. It preserves experimental structures such as patient × drug × dose tensors while jointly modeling them with standard omics matrices. By learning sparse, interpretable latent factors and handling missing data naturally, MANTRA uncovers clinically relevant patient subgroups and cell-type-specific disease programs that can be missed by conventional matrix-based integration methods.&lt;/p&gt;</description></item><item><title>Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention</title><link>https://mlo-lab.github.io/publication/pmlr-v-258-koebler-25-a/</link><pubDate>Sat, 01 Mar 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/pmlr-v-258-koebler-25-a/</guid><description/></item><item><title>An autonomous agent for auditing and improving the reliability of clinical AI models</title><link>https://mlo-lab.github.io/publication/kuhn-2025-autonomousagentauditingimproving/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/kuhn-2025-autonomousagentauditingimproving/</guid><description/></item><item><title>Application-driven validation of posteriors in inverse problems</title><link>https://mlo-lab.github.io/publication/adler-2025-application/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/adler-2025-application/</guid><description/></item><item><title>Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks</title><link>https://mlo-lab.github.io/publication/hekler-2025-beyond/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/hekler-2025-beyond/</guid><description/></item><item><title>Cross-Species Morphology Learning Enables Nucleic Acid-Independent Detection of Live Mutant Blood Cells</title><link>https://mlo-lab.github.io/publication/khan-2025-crossspecies/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/khan-2025-crossspecies/</guid><description/></item><item><title>DATS: Distance-Aware Temperature Scaling for Calibrated Class-Incremental Learning</title><link>https://mlo-lab.github.io/publication/serra-2025-dats/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/serra-2025-dats/</guid><description/></item><item><title>Decoding heart failure subtypes with neural networks via differential explanation analysis</title><link>https://mlo-lab.github.io/publication/ruz-2025-decoding/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/ruz-2025-decoding/</guid><description/></item><item><title>Differentiation hierarchy in adult B cell acute lymphoblastic leukemia at clonal resolution</title><link>https://mlo-lab.github.io/publication/gessner-2025-differentiation/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gessner-2025-differentiation/</guid><description/></item><item><title>Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers</title><link>https://mlo-lab.github.io/publication/kuhn-2025-efficient/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/kuhn-2025-efficient/</guid><description/></item><item><title>Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond</title><link>https://mlo-lab.github.io/publication/serra-2025-federated/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/serra-2025-federated/</guid><description/></item><item><title>How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning</title><link>https://mlo-lab.github.io/publication/serra-2025-how/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/serra-2025-how/</guid><description/></item><item><title>Machine Learning based Analysis for Radiomics Features Robustness in Real-World Deployment Scenarios</title><link>https://mlo-lab.github.io/publication/khan-2025-radiomics/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/khan-2025-radiomics/</guid><description/></item><item><title>MOFA-FLEX: A Factor Model Framework for Integrating Omics Data with Prior Knowledge</title><link>https://mlo-lab.github.io/publication/qoku-2025-mofaflex/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/qoku-2025-mofaflex/</guid><description/></item><item><title>A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models</title><link>https://mlo-lab.github.io/publication/gruber-2024-bias/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gruber-2024-bias/</guid><description/></item><item><title>Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors</title><link>https://mlo-lab.github.io/publication/popordanoska-2024-consistent/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/popordanoska-2024-consistent/</guid><description/></item><item><title>Disentangling Mean Embeddings for Better Diagnostics of Image Generators</title><link>https://mlo-lab.github.io/publication/gruber-2024-disentangling/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gruber-2024-disentangling/</guid><description/></item><item><title>DomainLab: A modular Python package for domain generalization in deep learning</title><link>https://mlo-lab.github.io/publication/sun-2024-domainlab/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/sun-2024-domainlab/</guid><description/></item><item><title>Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance</title><link>https://mlo-lab.github.io/publication/decker-2024-explanatory/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/decker-2024-explanatory/</guid><description/></item><item><title>Exploratory analysis of metabolic changes using mass spectrometry data and graph embeddings</title><link>https://mlo-lab.github.io/publication/alvarez-2024-exploratory/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/alvarez-2024-exploratory/</guid><description/></item><item><title>Metrics reloaded: recommendations for image analysis validation</title><link>https://mlo-lab.github.io/publication/maier-2024-metrics/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/maier-2024-metrics/</guid><description/></item><item><title>MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model</title><link>https://mlo-lab.github.io/publication/koebler-2024-morellm/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/koebler-2024-morellm/</guid><description/></item><item><title>Provably Better Explanations with Optimized Aggregation of Feature Attributions</title><link>https://mlo-lab.github.io/publication/decker-2024-provably/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/decker-2024-provably/</guid><description/></item><item><title>Through the Eyes of the Expert: Aligning Human and Machine Attention for Industrial AI</title><link>https://mlo-lab.github.io/publication/koebler-2024-through/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/koebler-2024-through/</guid><description/></item><item><title>Understanding metric-related pitfalls in image analysis validation</title><link>https://mlo-lab.github.io/publication/reinke-2024-understanding/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/reinke-2024-understanding/</guid><description/></item><item><title>Unified Analysis of Multi-order Tensors for Integrative Molecular Profiling</title><link>https://mlo-lab.github.io/publication/deazevedo-2024-unified/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/deazevedo-2024-unified/</guid><description/></item><item><title>Deep Learning Model for Video-Classification of Echocardiography Images</title><link>https://mlo-lab.github.io/publication/destito-2023-deep/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/destito-2023-deep/</guid><description/></item><item><title>Encoding domain knowledge in multi-view latent variable models: A bayesian approach with structured sparsity</title><link>https://mlo-lab.github.io/publication/qoku-2023-encoding/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/qoku-2023-encoding/</guid><description/></item><item><title>Multimodal analysis methods in predictive biomedicine</title><link>https://mlo-lab.github.io/publication/qoku-2023-multimodal/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/qoku-2023-multimodal/</guid><description/></item><item><title>Test Time Augmentation Meets Post-hoc Calibration: Uncertainty Quantification under Real-World Conditions</title><link>https://mlo-lab.github.io/publication/hekler-2023-test/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/hekler-2023-test/</guid><description/></item><item><title>Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition</title><link>https://mlo-lab.github.io/publication/gruber-2023-uncertainty/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gruber-2023-uncertainty/</guid><description/></item><item><title>Better uncertainty calibration via proper scores for classification and beyond</title><link>https://mlo-lab.github.io/publication/gruber-2022-better/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gruber-2022-better/</guid><description/></item><item><title>Inflammatory fibroblasts mediate resistance to neoadjuvant therapy in rectal cancer</title><link>https://mlo-lab.github.io/publication/nicolas-2022-inflammatory/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/nicolas-2022-inflammatory/</guid><description/></item><item><title>Single cell analyses identify a highly regenerative and homogenous human CD34+ hematopoietic stem cell population</title><link>https://mlo-lab.github.io/publication/anjos-2022-single/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/anjos-2022-single/</guid><description/></item><item><title>The proteogenomic subtypes of acute myeloid leukemia</title><link>https://mlo-lab.github.io/publication/jayavelu-2022-proteogenomic/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/jayavelu-2022-proteogenomic/</guid><description/></item><item><title>Hierarchical Domain Invariant Variational Auto-Encoding with weak domain supervision</title><link>https://mlo-lab.github.io/publication/sun-2021-hierarchical/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/sun-2021-hierarchical/</guid><description/></item><item><title>Multi-output Gaussian Processes for uncertainty-aware recommender systems</title><link>https://mlo-lab.github.io/publication/yang-2021-multi/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/yang-2021-multi/</guid><description/></item><item><title>Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration</title><link>https://mlo-lab.github.io/publication/tomani-2021-parameterized/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/tomani-2021-parameterized/</guid><description/></item><item><title>Towards trustworthy predictions from deep neural networks with fast adversarial calibration</title><link>https://mlo-lab.github.io/publication/tomani-2021-towards/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/tomani-2021-towards/</guid><description/></item><item><title>Post-hoc Uncertainty Calibration for Domain Drift Scenarios</title><link>https://mlo-lab.github.io/publication/tomani-post-hoc-2020/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/tomani-post-hoc-2020/</guid><description/></item><item><title>Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression</title><link>https://mlo-lab.github.io/publication/cuomo-single-cell-2020/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/cuomo-single-cell-2020/</guid><description/></item><item><title>TIMELY: Improving Labeling Consistency in Medical Imaging for Cell Type Classification</title><link>https://mlo-lab.github.io/publication/liu-timely-2020/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/liu-timely-2020/</guid><description/></item><item><title>Ano-rectal wall dose-surface maps localize the dosimetric benefit of hydrogel rectum spacers in prostate cancer radiotherapy</title><link>https://mlo-lab.github.io/publication/vanneste-ano-rectal-2019/</link><pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/vanneste-ano-rectal-2019/</guid><description/></item><item><title>Document informed neural autoregressive topic models with distributional prior</title><link>https://mlo-lab.github.io/publication/gupta-document-2019/</link><pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gupta-document-2019/</guid><description/></item><item><title>Metabolic regulation of pluripotency and germ cell fate through α-ketoglutarate</title><link>https://mlo-lab.github.io/publication/tischler-metabolic-2019/</link><pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/tischler-metabolic-2019/</guid><description/></item><item><title>Single cell multi-omics profiling reveals a hierarchical epigenetic landscape during mammalian germ layer specification</title><link>https://mlo-lab.github.io/publication/argelaguet-single-2019/</link><pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/argelaguet-single-2019/</guid><description/></item><item><title>Impressum</title><link>https://mlo-lab.github.io/terms/</link><pubDate>Thu, 28 Jun 2018 00:00:00 +0100</pubDate><guid>https://mlo-lab.github.io/terms/</guid><description>&lt;h2 id="anbieter-der-internetpräsenz"&gt;Anbieter der Internetpräsenz&lt;/h2&gt;
&lt;p&gt;Prof. Dr. Florian Buettner&lt;br&gt;
Deutsches Konsortium für Translationale Krebsforschung (DKTK)&lt;br&gt;
Deutsches Krebsforschungszentrum&lt;br&gt;
Goethe University Frankfurt&lt;/p&gt;
&lt;p&gt;Theodor-Stern-Kai 7&lt;br&gt;
60596 Frankfurt am Main&lt;/p&gt;
&lt;h3 id="kontakt"&gt;Kontakt&lt;/h3&gt;
&lt;p&gt;Telefon: +49 696 30186212&lt;br&gt;
E-Mail: florian.buettner|dkfz-heidelberg.de&lt;/p&gt;
&lt;h2 id="streitschlichtung"&gt;Streitschlichtung&lt;/h2&gt;
&lt;p&gt;Die Europäische Kommission stellt eine Plattform zur Online-Streitbeilegung (OS) bereit: &lt;a href="https://ec.europa.eu/consumers/odr" target="_blank" rel="noopener"&gt;https://ec.europa.eu/consumers/odr&lt;/a&gt;.
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&lt;h3 id="urheberrecht"&gt;Urheberrecht&lt;/h3&gt;
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&lt;p&gt;Soweit die Inhalte auf dieser Seite nicht vom Betreiber erstellt wurden, werden die Urheberrechte Dritter beachtet. Insbesondere werden Inhalte Dritter als solche gekennzeichnet. Sollten Sie trotzdem auf eine Urheberrechtsverletzung aufmerksam werden, bitten wir um einen entsprechenden Hinweis. Bei Bekanntwerden von Rechtsverletzungen werden wir derartige Inhalte umgehend entfernen.&lt;/p&gt;</description></item><item><title>Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia</title><link>https://mlo-lab.github.io/publication/gabrys-design-2018/</link><pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gabrys-design-2018/</guid><description/></item><item><title>Document informed neural autoregressive topic models</title><link>https://mlo-lab.github.io/publication/gupta-document-2018/</link><pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gupta-document-2018/</guid><description/></item><item><title>Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets</title><link>https://mlo-lab.github.io/publication/argelaguet-multi-omics-2018/</link><pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/argelaguet-multi-omics-2018/</guid><description/></item><item><title>texttovec: Deep contextualized neural autoregressive models of language with distributed compositional prior</title><link>https://mlo-lab.github.io/publication/gupta-texttovec-2018-1/</link><pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gupta-texttovec-2018-1/</guid><description/></item><item><title>Texttovec: Deep contextualized neural autoregressive topic models of language with distributed compositional prior</title><link>https://mlo-lab.github.io/publication/gupta-texttovec-2018/</link><pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gupta-texttovec-2018/</guid><description/></item><item><title>A divergent population of autoantigen-responsive CD4+ T cells in infants prior to β cell autoimmunity</title><link>https://mlo-lab.github.io/publication/heninger-divergent-2017/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/heninger-divergent-2017/</guid><description/></item><item><title>cgcorrect: a method to correct for confounding cell–cell variation due to cell growth in single-cell transcriptomics</title><link>https://mlo-lab.github.io/publication/blasi-cgcorrect-2017/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/blasi-cgcorrect-2017/</guid><description/></item><item><title>f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq</title><link>https://mlo-lab.github.io/publication/buettner-f-sclvm-2017/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-f-sclvm-2017/</guid><description/></item><item><title>Non-targeted metabolomic approach reveals two distinct types of metabolic responses to telomerase dysfunction in S. cerevisiae</title><link>https://mlo-lab.github.io/publication/buettner-non-targeted-2017/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-non-targeted-2017/</guid><description/></item><item><title>Parotid gland mean dose as a xerostomia predictor in low-dose domains</title><link>https://mlo-lab.github.io/publication/gabrys-parotid-2017/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/gabrys-parotid-2017/</guid><description/></item><item><title>Prospective identification of hematopoietic lineage choice by deep learning</title><link>https://mlo-lab.github.io/publication/buggenthin-prospective-2017/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buggenthin-prospective-2017/</guid><description/></item><item><title>Vitamin A-retinoic acid signaling regulates hematopoietic stem cell dormancy</title><link>https://mlo-lab.github.io/publication/cabezas-wallscheid-vitamin-2017/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/cabezas-wallscheid-vitamin-2017/</guid><description/></item><item><title>destiny: diffusion maps for large-scale single-cell data in R</title><link>https://mlo-lab.github.io/publication/angerer-destiny-2016/</link><pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/angerer-destiny-2016/</guid><description/></item><item><title>Unbiased prediction and feature selection in high-dimensional survival regression</title><link>https://mlo-lab.github.io/publication/laimighofer-unbiased-2016/</link><pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/laimighofer-unbiased-2016/</guid><description/></item><item><title>Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations</title><link>https://mlo-lab.github.io/publication/wilson-combined-2015/</link><pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/wilson-combined-2015/</guid><description/></item><item><title>Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells</title><link>https://mlo-lab.github.io/publication/buettner-computational-2015/</link><pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-computational-2015/</guid><description/></item><item><title>Computational assignment of cell-cycle stage from single-cell transcriptome data</title><link>https://mlo-lab.github.io/publication/scialdone-computational-2015/</link><pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/scialdone-computational-2015/</guid><description/></item><item><title>Decoding the regulatory network of early blood development from single-cell gene expression measurements</title><link>https://mlo-lab.github.io/publication/moignard-decoding-2015/</link><pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/moignard-decoding-2015/</guid><description/></item><item><title>Diffusion maps for high-dimensional single-cell analysis of differentiation data</title><link>https://mlo-lab.github.io/publication/haghverdi-diffusion-2015/</link><pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/haghverdi-diffusion-2015/</guid><description/></item><item><title>Effects of high-dose oral insulin on immune responses in children at high risk for type 1 diabetes: the Pre-POINT randomized clinical trial</title><link>https://mlo-lab.github.io/publication/bonifacio-effects-2015/</link><pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/bonifacio-effects-2015/</guid><description/></item><item><title>RAMONA: a Web application for gene set analysis on multilevel omics data</title><link>https://mlo-lab.github.io/publication/sass-ramona-2015/</link><pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/sass-ramona-2015/</guid><description/></item><item><title>Single-cell transcriptomic reconstruction reveals cell cycle and multi-lineage differentiation defects in Bcl11a-deficient hematopoietic stem cells</title><link>https://mlo-lab.github.io/publication/tsang-single-cell-2015/</link><pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/tsang-single-cell-2015/</guid><description/></item><item><title>Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes</title><link>https://mlo-lab.github.io/publication/winkler-feature-2014/</link><pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/winkler-feature-2014/</guid><description/></item><item><title>Molecular phenotypic profiling of a Saccharomyces cerevisiae strain at the single-cell level</title><link>https://mlo-lab.github.io/publication/schmidt-molecular-2014/</link><pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/schmidt-molecular-2014/</guid><description/></item><item><title>Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data</title><link>https://mlo-lab.github.io/publication/buettner-probabilistic-2014/</link><pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-probabilistic-2014/</guid><description/></item><item><title>Two non-parametric methods for derivation of constraints from radiotherapy dose–histogram data</title><link>https://mlo-lab.github.io/publication/ebert-two-2014/</link><pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/ebert-two-2014/</guid><description/></item><item><title>A modular framework for gene set analysis integrating multilevel omics data</title><link>https://mlo-lab.github.io/publication/sass-modular-2013/</link><pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/sass-modular-2013/</guid><description/></item><item><title>Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis</title><link>https://mlo-lab.github.io/publication/moignard-characterization-2013/</link><pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/moignard-characterization-2013/</guid><description/></item><item><title>A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst</title><link>https://mlo-lab.github.io/publication/buettner-novel-2012/</link><pubDate>Sun, 01 Jan 2012 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-novel-2012/</guid><description/></item><item><title>Novel approaches to improve the therapeutic index of head and neck radiotherapy: an analysis of data from the PARSPORT randomised phase III trial</title><link>https://mlo-lab.github.io/publication/buettner-novel-2012-1/</link><pubDate>Sun, 01 Jan 2012 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-novel-2012-1/</guid><description/></item><item><title>The dose–response of the anal sphincter region–an analysis of data from the MRC RT01 trial</title><link>https://mlo-lab.github.io/publication/buettner-doseresponse-2012/</link><pubDate>Sun, 01 Jan 2012 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-doseresponse-2012/</guid><description/></item><item><title>Modeling late rectal toxicities based on a parameterized representation of the 3D dose distribution</title><link>https://mlo-lab.github.io/publication/buettner-modeling-2011/</link><pubDate>Sat, 01 Jan 2011 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-modeling-2011/</guid><description/></item><item><title>Using Bayesian logistic regression to evaluate a new type of dosimetric constraint for prostate radiotherapy treatment planning</title><link>https://mlo-lab.github.io/publication/buettner-using-2010/</link><pubDate>Fri, 01 Jan 2010 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-using-2010/</guid><description/></item><item><title>Assessing correlations between the spatial distribution of the dose to the rectal wall and late rectal toxicity after prostate radiotherapy: an analysis of data from the MRC RT01 trial (ISRCTN 47772397)</title><link>https://mlo-lab.github.io/publication/buettner-assessing-2009/</link><pubDate>Thu, 01 Jan 2009 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-assessing-2009/</guid><description/></item><item><title>Using dose-surface maps to predict radiation-induced rectal bleeding: a neural network approach</title><link>https://mlo-lab.github.io/publication/buettner-using-2009/</link><pubDate>Thu, 01 Jan 2009 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/buettner-using-2009/</guid><description/></item><item><title>Dosimetric impact of motion in free-breathing and gated lung radiotherapy: A 4D Monte Carlo study of intrafraction and interfraction effects</title><link>https://mlo-lab.github.io/publication/seco-dosimetric-2008/</link><pubDate>Tue, 01 Jan 2008 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/seco-dosimetric-2008/</guid><description/></item><item><title>Optical trapping dynamics for cell identification</title><link>https://mlo-lab.github.io/publication/volpe-optical-2006/</link><pubDate>Sun, 01 Jan 2006 00:00:00 +0000</pubDate><guid>https://mlo-lab.github.io/publication/volpe-optical-2006/</guid><description/></item></channel></rss>