about
A multi-label approach to target prediction taking ligand promiscuity into accountIntegrated 3D-printed reactionware for chemical synthesis and analysisEfficient modeling and active learning discovery of biological responsesTowards structural systems pharmacology to study complex diseases and personalized medicineCollaborative analysis of multi-gigapixel imaging data using CytomineEfficient discovery of responses of proteins to compounds using active learningDeciding when to stop: efficient experimentation to learn to predict drug-target interactionsThe parameter sensitivity of random forests.Active machine learning-driven experimentation to determine compound effects on protein patterns.Biological imaging software tools.Image analysis in fluorescence microscopy: bacterial dynamics as a case study.Active learning for computational chemogenomics.CellOrganizer: Image-derived models of subcellular organization and protein distribution.Toward High-Throughput Predictive Modeling of Protein Binding/Unbinding Kinetics.Predicting drug-target interactions using probabilistic matrix factorization.ChemSAR: an online pipelining platform for molecular SAR modeling.Systems Biology-Driven Hypotheses Tested In Vivo: The Need to Advancing Molecular Imaging Tools.Discriminate the response of Acute Myeloid Leukemia patients to treatment by using proteomics data and Answer Set Programming.Statistics, damned statistics and nanoscience – using data science to meet the challenge of nanomaterial complexity
P2860
Q27902280-266C98F2-2581-426D-8642-EFF553F79A60Q28314888-14B5D268-568B-4025-B815-88FB3351986CQ28537673-E1CC83B9-467D-4C48-92E5-FEB1B8F3D9A7Q28538838-6025C62F-0FC4-43F5-A135-0731153F3CCDQ28603653-05E98077-15B3-46AD-98EA-946CB3411987Q35177638-7808DD3A-6B3F-44E9-BFD2-8FC96FE76DAEQ35685663-866D3817-6719-4151-B1FD-C9DAE49450C1Q36120552-95E30FF8-CC67-4A3E-B188-2C59CC2BF344Q36703610-14116824-5E2D-4978-BC3A-2D4C03C654A7Q36861970-3D332E4F-3EEF-419B-92AD-16F15F50202FQ37993498-5245054A-EBE1-4377-9BF7-AF65A489A10CQ38750215-318C360F-0298-4D89-994A-430CFEE230D7Q39368429-0EC4EFAD-709B-4F32-AAE5-1B64E8E96D06Q41190100-A4184DE2-8893-4506-AAB3-CE1A3B809A7DQ42778934-DF9DAE97-3B7D-4203-9B5B-5AFAC889EA98Q43089449-56DF90B3-1E16-4D80-8787-9709CCDA19FDQ47614568-A95F3B55-D366-4777-8EF0-70F161DC26D7Q52656594-453490B7-6610-4B4F-867D-3E78E3867843Q57186920-54124222-5D85-46C6-BF74-1DD3516395BA
P2860
description
2011 nî lūn-bûn
@nan
2011 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
An active role for machine learning in drug development
@ast
An active role for machine learning in drug development
@en
type
label
An active role for machine learning in drug development
@ast
An active role for machine learning in drug development
@en
prefLabel
An active role for machine learning in drug development
@ast
An active role for machine learning in drug development
@en
P2860
P356
P1476
An active role for machine learning in drug development
@en
P2093
Robert F Murphy
P2860
P2888
P304
P356
10.1038/NCHEMBIO.576
P577
2011-06-01T00:00:00Z