Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Jeffrey N Law"'
Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks
Publikováno v:
JACS Au. 3:113-123
The discovery of new materials in unexplored chemical spaces necessitates quick and accurate prediction of thermodynamic stability, often assessed using density functional theory (DFT), and efficient search strategies. Here, we develop a new approach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b093725f685bce5e7db7748c41008b97
https://doi.org/10.1021/jacsau.2c00540
https://doi.org/10.1021/jacsau.2c00540
Autor:
Shree Sowndarya S. V., Jeffrey N. Law, Charles E. Tripp, Dmitry Duplyakin, Erotokritos Skordilis, David Biagioni, Robert S. Paton, Peter C. St. John
Publikováno v:
Nature Machine Intelligence. 4:720-730
Advances in the field of goal-directed molecular optimization offer the promise of finding feasible candidates for even the most challenging molecular design applications. One example of a fundamental design challenge is the search for novel stable r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb3f85901a3b50bb4b90356abd8af3e4
https://doi.org/10.1038/s42256-022-00506-3
https://doi.org/10.1038/s42256-022-00506-3
Publikováno v:
F1000Research, Vol 7 (2018)
PathLinker is a graph-theoretic algorithm originally developed to reconstruct the interactions in a signaling pathway of interest. It efficiently computes multiple short paths within a background protein interaction network from the receptors to tran
Externí odkaz:
https://doaj.org/article/e4e46b6b8bd24209ae58d79a8046d5cd
Publikováno v:
F1000Research, Vol 6 (2017)
PathLinker is a graph-theoretic algorithm for reconstructing the interactions in a signaling pathway of interest. It efficiently computes multiple short paths within a background protein interaction network from the receptors to transcription factors
Externí odkaz:
https://doaj.org/article/5609706100cd45bc9c0dfa5f8ddcea60
Publikováno v:
bioRxiv
Motivation Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogene
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cda5f5378b4ebb4999720cf0faf6ae9
https://pubmed.ncbi.nlm.nih.gov/36158455
https://pubmed.ncbi.nlm.nih.gov/36158455
Protein sequence models for prediction and comparative analysis of the SARS-CoV-2 -human interactome
Autor:
Meghana, Kshirsagar, Nure, Tasnina, Michael D, Ward, Jeffrey N, Law, T M, Murali, Juan M, Lavista Ferres, Gregory R, Bowman, Judith, Klein-Seetharaman
Publikováno v:
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 26
Viruses such as the novel coronavirus, SARS-CoV-2, that is wreaking havoc on the world, depend on interactions of its own proteins with those of the human host cells. Relatively small changes in sequence such as between SARS-CoV and SARS-CoV-2 can dr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::52bd6b07ee2e6bef2795b953aae1153f
https://pubmed.ncbi.nlm.nih.gov/33691013
https://pubmed.ncbi.nlm.nih.gov/33691013
Protein sequence models for prediction and comparative analysis of the SARS-CoV-2 —human interactome
Autor:
Gregory R. Bowman, Nure Tasnina, Judith Klein-Seetharaman, T. M. Murali, Juan Lavista Ferres, Meghana Kshirsagar, Michael D. Ward, Jeffrey N. Law
Publikováno v:
PSB
Viruses such as the novel coronavirus, SARS-CoV-2, that is wreaking havoc on the world, depend on interactions of its own proteins with those of the human host cells. Relatively small changes in sequence such as between SARS-CoV and SARS-CoV-2 can dr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::db89076833daec0f38d48e59e9ad94a0
https://doi.org/10.1142/9789811232701_0015
https://doi.org/10.1142/9789811232701_0015
Publikováno v:
BCB
Over a dozen methods have been developed to infer gene regulatory networks (GRNs) from single-cell RNA-seq data. An experimentalist seeking to analyze a new dataset faces a daunting task in selecting an appropriate inference method since there are no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::788963c336d06cc5b5564566468e3057
https://doi.org/10.1145/3388440.3414213
https://doi.org/10.1145/3388440.3414213
Autor:
Jeffrey N Law, Kyle Akers, Nure Tasnina, Catherine M Della Santina, Shay Deutsch, Meghana Kshirsagar, Judith Klein-Seetharaman, Mark Crovella, Padmavathy Rajagopalan, Simon Kasif, T M Murali
Publikováno v:
GigaScience
Background Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., det
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1806f5bc8e037efc22cfe03f203e66e0
Publikováno v:
BMC Bioinformatics
BMC Bioinformatics, Vol 20, Iss S16, Pp 1-15 (2019)
BIBM
BMC Bioinformatics, Vol 20, Iss S16, Pp 1-15 (2019)
BIBM
Background Understanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses ab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::501ab81b4fbb9a2fbde6f73bc38548e4
http://europepmc.org/articles/PMC6886211
http://europepmc.org/articles/PMC6886211