title: Protein function in precision medicine: deep understanding with machine learning.
abstract: Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.
authors.affiliation: Department of Informatics and Bioinformatics, Institute for Advanced Studies, Technical University of Munich, Garching, Germany.
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
authors.affiliation: School of Informatics and Computing, Indiana University, Bloomington, IN, USA.
authors.forename: Predrag
authors.initials: P
authors.lastname: Radivojac
authors.affiliation: Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.
authors.forename: Yana
authors.initials: Y
authors.lastname: Bromberg
mesh_terms: D019295:Computational Biology / Q000379:methods; D006801:Humans; D000069550:Machine Learning; D057285:Precision Medicine; D060066:Protein Interaction Maps / Q000235:genetics*; D011506:Proteins / Q000235:genetics*
chemical_list: D011506:Proteins
keywords: computational prediction; molecular mechanism of disease; protein function; variant effect