P
Philip E. Bourne
Researcher at University of Virginia
Publications - 357
Citations - 64294
Philip E. Bourne is an academic researcher from University of Virginia. The author has contributed to research in topics: Protein Data Bank & Structural genomics. The author has an hindex of 68, co-authored 331 publications receiving 54563 citations. Previous affiliations of Philip E. Bourne include University of Sheffield & University of California, Los Angeles.
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Proceedings ArticleDOI
An integrated workflow for proteome-wide off-target identification and polypharmacology drug design
TL;DR: A Proteome-wide Off-target Pipeline (POP) that integrates ligand binding site analysis, protein-ligand docking, the statistical analysis of docking scores, and electrostatic potential calculations is developed.
Journal ArticleDOI
Is "bioinformatics" dead?
TL;DR: In this paper, a computational biologist with 40 years of research experience says bioinformatics is dead and the short answer is, in being the Founding Dean of a new School of Data Science, what we do suddenly looks different.
Journal ArticleDOI
From biomedical cloud platforms to microservices: next steps in FAIR data and analysis
Nathan Sheffield,Vivien Bonazzi,Philip E. Bourne,Tony Burdett,Timothy Clark,Robert L. Grossman,Ola Spjuth,Andrew D. Yates +7 more
TL;DR: In this article , the authors argue that despite their advantages, cloud platforms in and of themselves do not automatically support FAIRness and propose that emphasizing modularity and interoperability would lead to a more powerful Unix-like ecosystem of web services for biomedical analysis and data retrieval.
Journal ArticleDOI
Resolving a distribution of charge into intrinsic multipole moments: A rankwise distributed multipole analysis
Apostol Gramada,Philip E. Bourne +1 more
TL;DR: The recursive construction of the intrinsic multipole moments are described and the algebraic expression of the multipole centers are derived and the resulting distributed multipole expansion provides a conceptual framework for the analysis and modeling of the electrostatic field and of its associated distribution of charge.
Journal ArticleDOI
Ten Simple Rules for Lifelong Learning, According to Hamming
TL;DR: This sequel to the 2007 contribution to the Ten Simple Rules series attempts to distil the essence of Richard Hamming's authoritative advice into ten rules that will equip the reader to more confidently face the unremitting emergence of an exponentially increasing amount of new knowledge, coupled with the equally relentless obsolescence of established knowledge, in a world containing a greater number of scientists than ever before.