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Missing Value Estimation for Compound‐Target Activity Data
Author(s) -
Tanrikulu Yusuf,
Kondru Rama,
Schneider Gisbert,
So W. Venus,
Bitter HansMarcus
Publication year - 2010
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201000073
Subject(s) - computer science , data mining , identification (biology) , imputation (statistics) , informatics , similarity (geometry) , drug target , biological data , missing data , machine learning , computational biology , artificial intelligence , data science , bioinformatics , biology , engineering , botany , pharmacology , electrical engineering , image (mathematics)
Relationships between drug targets and associated diseases have traditionally been investigated by means of sequence similarity, comparative protein modeling, and pathway analysis. Recently, a complementary paradigm has emerged to link targets and drugs via biological responses within activity data and visualize findings in networks. It has been indicated that one of the obstacles towards the identification of novel interactions is the sparsity of available data. In this article, we provide a survey of estimation methods that address the challenge of data sparsity. Each method is described in terms of its advantages and limitations, and an exemplary application on compound‐target activity data is demonstrated. With such imputation methods in‐hand, the opportunity to combine efforts in molecular informatics can be realized, yielding novel insights into ligand‐target space.