|
|
|
|
| LEADER |
00000nam a22000003u 4500 |
| 001 |
b10786117 |
| 003 |
CoU |
| 005 |
20190920234344.6 |
| 006 |
m o d f |
| 007 |
cr ||||||||||| |
| 008 |
191002e20190805||| o| f0|||||eng|d |
| 035 |
|
|
|a (TOE)ost1559708
|
| 035 |
|
|
|a (TOE)1559708
|
| 040 |
|
|
|a TOE
|c TOE
|
| 049 |
|
|
|a GDWR
|
| 072 |
|
7 |
|a 59
|2 edbsc
|
| 086 |
0 |
|
|a E 1.99:1559708
|
| 086 |
0 |
|
|a E 1.99:1559708
|
| 245 |
0 |
0 |
|a Predicting kinase inhibitors using bioactivity matrix derived informer sets
|h [electronic resource]
|
| 260 |
|
|
|a Washington, D.C. :
|b United States. Department of Energy. ;
|a Oak Ridge, Tenn. :
|b distributed by the Office of Scientific and Technical Information, U.S. Department of Energy,
|c 2019.
|
| 300 |
|
|
|a Article No. e1006813 :
|b digital, PDF file.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent.
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia.
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier.
|
| 500 |
|
|
|a Published through SciTech Connect.
|
| 500 |
|
|
|a 08/05/2019.
|
| 500 |
|
|
|a PLoS Computational Biology (Online) 15 8 ISSN 1553-7358 AM.
|
| 500 |
|
|
|a Huikun Zhang; Spencer S. Ericksen; Ching-pei Lee; Gene E. Ananiev; Nathan Wlodarchak; Peng Yu; Julie C. Mitchell; Anthony Gitter; Stephen J. Wright; F. Michael Hoffmann; Scott A. Wildman; Michael A. Newton; Avner Schlessinger.
|
| 520 |
3 |
|
|a Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the basis of chemical diversity by some pseudo-random process (for example, the first few plates of a larger library) or by selecting an entire smaller library. These approaches may not produce a sufficient number or diversity of actives. An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets. We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data. We develop this Informer-Based-Ranking (IBR) approach using the Published Kinase Inhibitor Sets (PKIS) as the chemogenomic data to select the informer sets. We test the informer compounds on a target that is not part of the chemogenomic data, then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data. Through new chemical screening experiments, we demonstrate the utility of IBR strategies in a prospective test on three kinase targets not included in the PKIS.
|
| 536 |
|
|
|b AC05-00OR22725.
|
| 650 |
|
7 |
|a Basic Biological Sciences.
|2 edbsc.
|
| 710 |
2 |
|
|a Oak Ridge National Laboratory.
|4 res.
|
| 710 |
1 |
|
|a United States.
|b Department of Energy.
|4 spn.
|
| 710 |
2 |
|
|a National Science Foundation (U.S.).
|4 spn.
|
| 710 |
1 |
|
|a United States.
|b Department of Energy.
|b Office of Scientific and Technical Information.
|4 dst.
|
| 856 |
4 |
0 |
|u http://www.osti.gov/scitech/biblio/1559708
|z Full Text (via OSTI)
|
| 907 |
|
|
|a .b107861173
|b 10-03-19
|c 10-03-19
|
| 998 |
|
|
|a web
|b 10-03-19
|c f
|d m
|e p
|f eng
|g
|h 0
|i 1
|
| 956 |
|
|
|a Information bridge
|
| 999 |
f |
f |
|i 4ff28392-f977-5e92-a004-794f71f99118
|s 8ccc1cf5-b80c-549d-8b55-78100b91d2db
|
| 952 |
f |
f |
|p Can circulate
|a University of Colorado Boulder
|b Online
|c Online
|d Online
|e E 1.99:1559708
|h Superintendent of Documents classification
|i web
|n 1
|
