A
Altaf-Ul-Amin
Researcher at Nara Institute of Science and Technology
Publications - 27
Citations - 946
Altaf-Ul-Amin is an academic researcher from Nara Institute of Science and Technology. The author has contributed to research in topics: Metabolite & Protein–protein interaction. The author has an hindex of 12, co-authored 27 publications receiving 832 citations.
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Development and implementation of an algorithm for detection of protein complexes in large interaction networks
TL;DR: The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units and can help to predict the functions of proteins, and they are also useful to understand and explain certain biological processes.
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KNApSAcK Metabolite Activity Database for Retrieving the Relationships Between Metabolites and Biological Activities
Yukiko Nakamura,Farit Mochamad Afendi,Aziza Kawsar Parvin,Naoaki Ono,Ken Tanaka,Aki Hirai Morita,Tetsuo Sato,Tadao Sugiura,Altaf-Ul-Amin,Shigehiko Kanaya +9 more
TL;DR: The KNApSAcK Metabolite Activity DB is integrated within the KNAcK Family DBs to facilitate further systematized research in various omics fields, especially metabolomics, nutrigenomics and foodomics.
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Systems Biology in the Context of Big Data and Networks
TL;DR: This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.
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PCDq: human protein complex database with quality index which summarizes different levels of evidences of protein complexes predicted from H-Invitational protein-protein interactions integrative dataset
Shingo Kikugawa,Kensaku Nishikata,Kensaku Nishikata,Kensaku Nishikata,Katsuhiko S. Murakami,Yoshiharu Sato,Mami Suzuki,Altaf-Ul-Amin,Shigehiko Kanaya,Tadashi Imanishi +9 more
TL;DR: A new protein complex database with a complex quality index (PCDq) including both predicted and curated human protein complexes is constructed, which includes both known and predicted complexes and subunits.
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Novel Approach to Classify Plants Based on Metabolite-Content Similarity
TL;DR: This work proposes an approach for successfully classifying 216 plants based on their known incomplete metabolite content, and could predict some currently unknown species-metabolite relations.