JOURNAL ARTICLE
Random-forest model for drug–target interaction prediction via Kullback–Leibler divergence
Sang-Jin AhnSi Eun LeeMi‐Hyun Kim
Year: 2022 Journal: Journal of Cheminformatics Vol: 14 (1)Pages: 67-67 Publisher: BioMed Central
Keywords:
Random forest Computer science Virtual screening Kernel (algebra) Kullback–Leibler divergence Divergence (linguistics) Kernel density estimation Nonparametric statistics Artificial intelligence Feature (linguistics) Similarity (geometry) Support vector machine Data mining Machine learning Drug discovery Pattern recognition (psychology) Mathematics Bioinformatics Statistics
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38
Cited By
9.74
FWCI (Field Weighted Citation Impact)
53
Refs
0.96
Citation Normalized Percentile
Is in top 1%
Is in top 10%
Citation History
Topics
Computational Drug Discovery Methods
Physical Sciences → Computer Science → Computational Theory and Mathematics
Machine Learning in Materials Science
Physical Sciences → Materials Science → Materials Chemistry
Metabolomics and Mass Spectrometry Studies
Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology
Related Documents
JOURNAL ARTICLE
Additional file 1 of Random-forest model for drug–target interaction prediction via Kullback–Leibler divergence
Ahn, SangjinLee, Si EunKim, Mi-hyun
Journal: OPAL (Open@LaTrobe) (La Trobe University) Year: 2022
BOOK-CHAPTER
Kullback-Leibler Divergence
International Encyclopedia of Statistical Science Year: 2011 Pages: 720-722