title: An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation.
abstract: Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods' performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost.
authors.affiliation: Center for Health Informatics and Bioinformatics, New York University Medical Center, New York, New York, USA.
authors.forename: Sisi
authors.initials: S
authors.lastname: Ma
authors.affiliation: Molecular Cancer Research, Center for Molecular Medicine, University Medical Center, Utrecht, The Netherlands.
authors.forename: Patrick
authors.initials: P
authors.lastname: Kemmeren
authors.affiliation: Institute for Health Informatics, Academic Health Center, University of Minnesota, Minneapolis, MN 55455, USA.
authors.forename: Constantin F
authors.initials: CF
authors.lastname: Aliferis
authors.affiliation: Center for Health Informatics and Bioinformatics, New York University Medical Center, New York, New York, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Statnikov
mesh_terms: D000465:Algorithms; D064229:Biological Ontologies; D003198:Computer Simulation; D005240:Feasibility Studies; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D057166:High-Throughput Screening Assays; D006801:Humans; D008954:Models, Biological; D018794:Problem-Based Learning; D012107:Research Design; D012441:Saccharomyces cerevisiae / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism*; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
chemical_list: D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors