Difficulty in inferring microbial community structure based on co-occurrence network approaches H Hirano, K Takemoto BMC bioinformatics 20, 1-14, 2019 | 149 | 2019 |
Universal adversarial attacks on deep neural networks for medical image classification H Hirano, A Minagi, K Takemoto BMC medical imaging 21, 1-13, 2021 | 138 | 2021 |
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 | 135 | 2018 |
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 | 105 | 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 | 66 | 2016 |
HSEpred: predict half-sphere exposure from protein sequences J Song, H Tan, K Takemoto, T Akutsu Bioinformatics 24 (13), 1489-1497, 2008 | 63 | 2008 |
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 | 54 | 2020 |
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 | 54 | 2018 |
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 | 54 | 2012 |
Correlation between structure and temperature in prokaryotic metabolic networks K Takemoto, JC Nacher, T Akutsu BMC bioinformatics 8, 1-11, 2007 | 52 | 2007 |
Heterogeneity in ecological mutualistic networks dominantly determines community stability W Feng, K Takemoto Scientific reports 4 (1), 5912, 2014 | 49 | 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 | 42 | 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 | 38 | 2017 |
Evolving networks by merging cliques K Takemoto, C Oosawa Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 72 (4 …, 2005 | 37 | 2005 |
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 | 35 | 2018 |
Simple iterative method for generating targeted universal adversarial perturbations H Hirano, K Takemoto Algorithms 13 (11), 268, 2020 | 34 | 2020 |
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 | 34 | 2016 |
Introduction to complex networks: measures, statistical properties, and models K TAkEMOTO, C Oosawa Statistical and machine learning approaches for network analysis, 45-75, 2012 | 34 | 2012 |
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 | 33 | 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 | 31 | 2007 |