Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Benjamin J. Heil"'
Publikováno v:
PLoS Computational Biology, Vol 19, Iss 3, p e1010984 (2023)
Those building predictive models from transcriptomic data are faced with two conflicting perspectives. The first, based on the inherent high dimensionality of biological systems, supposes that complex non-linear models such as neural networks will be
Externí odkaz:
https://doaj.org/article/1a94026c384a4ba5bca5cb2647c9c2bd
Autor:
Daniel S. Himmelstein, Michael Zietz, Vincent Rubinetti, Kyle Kloster, Benjamin J. Heil, Faisal Alquaddoomi, Dongbo Hu, David N. Nicholson, Yun Hao, Blair D. Sullivan, Michael W. Nagle, Casey S. Greene
Hetnets, short for “heterogeneous networks”, contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes — including genes, diseases, drugs, pathways, and an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a8bceec1c5eedeec71a4db2d305d489
https://doi.org/10.1101/2023.01.05.522941
https://doi.org/10.1101/2023.01.05.522941
Autor:
Benjamin J. Heil, Michael M. Hoffman, Casey S. Greene, Florian Markowetz, Su-In Lee, Stephanie C. Hicks
Publikováno v:
Nat Methods
To make machine learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model, and code publication, programming best practices, and workflow automation. By meeting these standards, the community
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbd847a2c6fcf256813a95ed9c4d58d4
https://europepmc.org/articles/PMC9131851/
https://europepmc.org/articles/PMC9131851/
Publikováno v:
Bioinformatics (Oxford, England). 34(9)
Motivation Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbea8089ab2431266f2ecc72f722af37
https://pubmed.ncbi.nlm.nih.gov/29309523
https://pubmed.ncbi.nlm.nih.gov/29309523
MotivationClassification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a5850bfa5f17dc1df06f72af2be7b55c
https://doi.org/10.1101/170407
https://doi.org/10.1101/170407