Kazuhiro Takemoto
Cited by
Cited by
Difficulty in inferring microbial community structure based on co-occurrence network approaches
H Hirano, K Takemoto
BMC bioinformatics 20, 1-14, 2019
PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework
J Song, F Li, K Takemoto, G Haffari, T Akutsu, KC Chou, GI Webb
Journal of theoretical biology 443, 125-137, 2018
Universal adversarial attacks on deep neural networks for medical image classification
H Hirano, A Minagi, K Takemoto
BMC medical imaging 21, 1-13, 2021
Identification of chemogenomic features from drug–target interaction networks using interpretable classifiers
Y Tabei, E Pauwels, V Stoven, K Takemoto, Y Yamanishi
Bioinformatics 28 (18), i487-i494, 2012
An automated system for evaluation of the potential functionome: MAPLE version 2.1. 0
H Takami, T Taniguchi, W Arai, K Takemoto, Y Moriya, S Goto
Dna Research 23 (5), 467-475, 2016
HSEpred: predict half-sphere exposure from protein sequences
J Song, H Tan, K Takemoto, T Akutsu
Bioinformatics 24 (13), 1489-1497, 2008
MAPLE 2.3. 0: an improved system for evaluating the functionomes of genomes and metagenomes
W Arai, T Taniguchi, S Goto, Y Moriya, H Uehara, K Takemoto, H Ogata, ...
Bioscience, biotechnology, and biochemistry 82 (9), 1515-1517, 2018
Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks
H Hirano, K Koga, K Takemoto
Plos one 15 (12), e0243963, 2020
FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model
M Wang, XM Zhao, K Takemoto, H Xu, Y Li, T Akutsu, J Song
Public Library of Science 7 (8), e43847, 2012
Correlation between structure and temperature in prokaryotic metabolic networks
K Takemoto, JC Nacher, T Akutsu
BMC bioinformatics 8, 1-11, 2007
Heterogeneity in ecological mutualistic networks dominantly determines community stability
W Feng, K Takemoto
Scientific reports 4 (1), 5912, 2014
Global COVID-19 transmission rate is influenced by precipitation seasonality and the speed of climate temperature warming
K Chiyomaru, K Takemoto
MedRxiv, 2020.04. 10.20060459, 2020
Data integration aids understanding of butterfly–host plant networks
A Muto-Fujita, K Takemoto, S Kanaya, T Nakazato, T Tokimatsu, ...
Scientific Reports 7 (1), 43368, 2017
Evolving networks by merging cliques
K Takemoto, C Oosawa
Physical Review E 72 (4), 046116, 2005
Human impacts and climate change influence nestedness and modularity in food-web and mutualistic networks
K Takemoto, K Kajihara
PLoS One 11 (6), e0157929, 2016
An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins
C Zheng, M Wang, K Takemoto, T Akutsu, Z Zhang, J Song
PLoS One 7 (11), e49716, 2012
Simple iterative method for generating targeted universal adversarial perturbations
H Hirano, K Takemoto
Algorithms 13 (11), 268, 2020
Introduction to complex networks: measures, statistical properties, and models
K TAkEMOTO, C Oosawa
Statistical and machine learning approaches for network analysis, 45-75, 2012
Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity
K Takemoto, C Oosawa
Mathematical biosciences 208 (2), 454-468, 2007
Large-scale aggregation analysis of eukaryotic proteins reveals an involvement of intrinsically disordered regions in protein folding
E Uemura, T Niwa, S Minami, K Takemoto, S Fukuchi, K Machida, ...
Scientific reports 8 (1), 678, 2018
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Articles 1–20