Merge to dev branch

README.md

@@ -1,8 +1,17 @@
-# StarBASE
+# StarBASE-GP
 
 <div align="center">
-  <img src="./docs/StarBASE_logo_small.png" alt="Logo">
+  <img src="./docs/starbase-gp-logo.png" alt="Logo" width="540" height="540">
 </div>
 
-StarBASE : Star-Based Automated Single-locus and Epistasis analysis pipeline - Genetic Programming
+StarBASE-GP : Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming
 ==================================
+
+## Abstract
+
+> We present the Star-Based Automated Single-locus and Epistasis analysis tool – Genetic Programming (StarBASE-GP), an automated framework for discovering meaningful genetic variants associated with phenotypic variation in large-scale genomic datasets. 
+StarBASE-GP uses a genetic programming–based multi-objective optimization strategy to evolve machine learning pipelines that simultaneously maximize explanatory power ($r^2$) and minimize pipeline complexity. 
+Biological domain knowledge is integrated at multiple stages, including the use of nine inheritance encoding strategies to model deviations from additivity, a custom linkage disequilibrium pruning node that minimizes redundancy among features, and a dynamic variant recommendation system that prioritizes informative candidates for pipeline inclusion. 
+We evaluate StarBASE-GP on a cohort of \textit{Rattus norvegicus} (brown rat) to identify variants associated with body mass index, benchmarking its performance against a random baseline and a biologically naïve version of the tool. 
+StarBASE-GP consistently evolves Pareto fronts with superior performance, yielding higher accuracy in identifying both ground truth and novel quantitative trait loci, highlighting relevant targets for future validation. 
+By incorporating evolutionary search and relevant biological theory into a flexible automated machine learning framework, StarBASE-GP demonstrates robust potential for advancing variant discovery in complex traits.

Source/evolver.py

@@ -1055,10 +1055,14 @@ def plot_pareto_front(self, pop: List[Pipeline]) -> None:
         plt.clf()
 
     # check if ground truth is found in the Pareto front
-    def check_ground_truth(self) -> bool:
+    def check_ground_truth(self) -> None:
         # get the pareto front from the population
         _, rank = nsga.non_dominated_sorting(obj_scores=self.get_pipeline_scores(self.population, weights=(r2_t(1.0), feature_cnt_t(-1))))
-        pareto_front = self.population[rank == 0]
+        pareto_front = []
+        # get rank == 0 pipelines
+        for i, r in enumerate(rank):
+            if r == 0:
+                pareto_front.append(self.population[i])
 
         # get snps from the pareto front that are not prunned
         good_snps = set()
@@ -1073,8 +1077,7 @@ def check_ground_truth(self) -> bool:
                 chrom, pos = self.snp_chrm_pos(snp)
                 if true_chrom == chrom and np.abs(true_pos - pos) <= self.truth_distance:
                     print('Found ground truth SNP:', true_snp, flush=True)
-
-        return
+
 
     # helper to generate chromosome number and snp position
     def snp_chrm_pos(self, snp: snp_t) -> Tuple[gen_chrom_num_t, gen_chrom_pos_t]: