Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells
Open accessAbstract
Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryonic stem cell (hESC) system, is improved by the adoption of gene networks, in which network edge weights are added as feature vectors when noisy qRT-PCR data fail to make accurate predictions. The accuracies of our system were 97.5-100% for three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs) and non-genotoxic carcinogens (NGCs). For two uncategorized chemicals, bisphenol-A and permethrin, our system yielded reasonable results: bisphenol-A was categorized as an NGC, and permethrin was categorized as an NT; both predictions were supported by recently published papers. Our study has two important features: (i) as the first study to employ gene networks without using conventional quantitative structure-activity relationships (QSARs) as input data for SVMs to analyze toxicogenomics data in an hESC validation system, it uses additional information of gene-to-gene interactions to significantly increase prediction accuracies for noisy gene expression data; and (ii) using only undifferentiated hESCs, our study has considerable potential to predict late-onset chemical toxicities, including abnormalities that occur during embryonic development.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Combining chemical structural description with human transcriptome data analysis can greatly enhance toxicity prediction accuracy.
“We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [133]
Yamane et al. used chemicals to treat human embryonic stem cells, analyzed transcriptomes, classified chemicals into categories, analyzed gene interaction networks, greatly improved accuracy of toxicity prediction and allowed prediction of delayed chemical toxicity with SVM.
“Yamane et al. used chemicals to treat human embryonic stem cells and analyzed their transcriptomes. By classifying the chemicals into different categories, such as neurotoxins, genotoxic carcinogens, and non-genotoxic carcinogens, and by analyzing gene interaction networks, they gained much richer information, which greatly improved the accuracy of toxicity prediction and even allowed for them to”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [133]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236 · see citing works
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697 · see citing works
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954 · see citing works
limma: Linear Models for Microarray Data
Smyth, G. K. (2005) · Statistics for Biology and Health · doi:10.1007/0-387-29362-0_23 · Cited by 5,796 · see citing works
(2000) · Applied Physics Letters · doi:10.1162/153244303322753616 · Cited by 3,581 · see citing works
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Gentleman, R. C., Carey, V. J., Huber, W. K., Irizarry, R. A., & Dudoit, S. (2005) · Statistics in the health sciences · doi:10.1007/0-387-29362-0 · Cited by 3,470 · see citing works
Using Bayesian Networks to Analyze Expression Data
Friedman, N., Michal Linial, B., Nachman, I., & Pe’er, D. (2000) · Journal of Computational Biology · doi:10.1089/106652700750050961 · Cited by 3,248 · see citing works
Identification of a Primary Target of Thalidomide Teratogenicity
Ito, T., Ando, H., Suzuki, T., Ogura, T., Hotta, K., Imamura, Y., Yamaguchi, Y., & Handa, H. (2010) · Science · doi:10.1126/science.1177319 · Cited by 2,136 · see citing works
Do we need hundreds of classifiers to solve real world classification problems
Fernández-Delgado, M., Cernadas, E., Barro, S., & AmorimDinani (2014) · Journal of Machine Learning Research · doi:10.5555/2627435.2697065 · Cited by 2,131 · see citing works
Replica Monte Carlo Simulation of Spin-Glasses
Swendsen, R. H., & Wang, J. (1986) · Physical Review Letters · doi:10.1103/physrevlett.57.2607 · Cited by 2,049 · see citing works
Multiclass cancer diagnosis using tumor gene expression signatures
Ramaswamy, S., Tamayo, P., Rifkin, R. M., Mukherjee, S., Yeang, C., Angelo, M., Ladd, C., Reich, M. M., Latulippe, É., Mesirov, J. P., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.211566398 · Cited by 2,029 · see citing works
Graph-based approach for airborne light detection and ranging segmentation
Vilariño, D. L., Cabaleiro, J. C., Sánchez, J. M., Rivera, F. F., & Pena, T. F. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015020 · Cited by 1,760 · see citing works
Virtual Computational Chemistry Laboratory – Design and Description
Tetko, I. V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D. J., Ertl, P., Palyulin, V. A., Radchenko, E. V., Zefirov, N. S., Makarenko, A. S., et al. (2005) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-005-8694-y · Cited by 1,504 · see citing works
Parallel tempering: Theory, applications, and new perspectives
Earl, D. J., & Deem, M. W. (2005) · Physical Chemistry Chemical Physics · doi:10.1039/b509983h · Cited by 1,317 · see citing works
Parallel Tempering: Theory, Applications, and New PerspectivesPreprint
Earl, D. J., & Deem, M. W. (2008) · Cited by 1,015 · see citing works
Semi-Supervised Support Vector Machines
Bennett, K. P., & Demiriz, A. (1998) · Cited by 832 · see citing works
Ho, S.-M., Tang, W.-Y., Belmonte de Frausto, J., & Prins, G. S. (2006) · Cancer Research · doi:10.1158/0008-5472.can-06-0516 · Cited by 796 · see citing works
The Toxicity Data Landscape for Environmental Chemicals
Judson, R., Richard, A., Dix, D. J., Houck, K. A., Martin, M. T., Kavlock, R. J., Dellarco, V. L., Henry, T. R., Holderman, T. S., Sayre, P. G., et al. (2008) · Environmental Health Perspectives · doi:10.1289/ehp.0800168 · Cited by 510 · see citing works
Mercury Exposure and Child Development Outcomes
Davidson, P. W., MYERS, G. J., & Weiss, B. (2004) · PEDIATRICS · doi:10.1542/peds.113.s3.1023 · Cited by 321 · see citing works
Chemical predictive modelling to improve compound quality
Cumming, J. G., Davis, A. M., Mureşan, S., Haeberlein, M., & Chen, H. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4128 · Cited by 309 · see citing works
Suemori, H., Yasuchika, K., Hasegawa, K., Fujioka, T., Tsuneyoshi, N., & Nakatsuji, N. (2006) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2006.04.135 · Cited by 301 · see citing works
Leranth, C., Hajszán, T., Szigeti‐Buck, K., Bober, J., & MacLusky, N. J. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806139105 · Cited by 232 · see citing works
Predicting Drug-Induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches
Low, Y., Uehara, T., Minowa, Y., Yamada, H., Ohno, Y., Urushidani, T., Sedykh, A., Muratov, E. N., Кузьмин, В. Е., Fourches, D., et al. (2011) · Chemical Research in Toxicology · doi:10.1021/tx200148a · Cited by 224 · see citing works
Support Functions and Datasets for Venables and Ripley's MASS
Ripley, B. D. (2015) · Cited by 181 · see citing works
Nasuti, C., Gabbianelli, R., Falcioni, M. L., Stefano, A. D., Sozio, P., & Cantalamessa, F. (2006) · Toxicology · doi:10.1016/j.tox.2006.10.015 · Cited by 171 · see citing works
Waters, M. D., Stasiewicz, S., Merrick, B. A., Tomer, K. B., Bushel, P. R., Paules, R. S., Stegman, N., Nehls, G. J., Yost, K. J., Johnson, C. H., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm755 · Cited by 149 · see citing works
Opportunities for Human iPS Cells in Predictive Toxicology
Anson, B. D., Kolaja, K., & Kamp, T. J. (2011) · Clinical pharmacology and therapy · doi:10.1038/clpt.2011.9 · Cited by 143 · see citing works
Bisphenol A induce ovarian cancer cell migration via the MAPK and PI3K/Akt signalling pathways
Ptak, A., Hoffmann, M., Gruca, I., & Barć, J. (2014) · Toxicology Letters · doi:10.1016/j.toxlet.2014.07.001 · Cited by 105 · see citing works
Dhimolea, E., Wadia, P. R., Murray, T. J., Settles, M. L., Treitman, J. D., Sonnenschein, C., Shioda, T., & Soto, A. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099800 · Cited by 104 · see citing works
Structure−Toxicity Relationships for Benzenes Evaluated with Tetrahymena pyriformis
Schultz, T. W. (1999) · Chemical Research in Toxicology · doi:10.1021/tx9900730 · Cited by 94 · see citing works
Koutsoukas, A., Paricharak, S., Galloway, W. R. J. D., Spring, D. R., IJzerman, A. P., Glen, R. C., Marcus, D., & Bender, A. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400469u · Cited by 88 · see citing works
A transcriptomics-based in vitro assay for predicting chemical genotoxicity in vivo
Magkoufopoulou, C., Claessen, S. M. H., Tsamou, M., Jennen, D. G. J., Kleinjans, J. C. S., & van Delft, J. H. M. (2012) · Carcinogenesis · doi:10.1093/carcin/bgs182 · Cited by 86 · see citing works
Sakuratani, Y., Zhang, H. Q., Nishikawa, S., Yamazaki, K., Yamada, T., Yamada, J., Gerova, K. D., Chankov, G., Mekenyan, O. G., & Hayashi, M. (2013) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2013.773375 · Cited by 79 · see citing works
Compound selection for in vitro modeling of developmental neurotoxicity
Kadereit, S. (2012) · Frontiers in bioscience · doi:10.2741/4064 · Cited by 73 · see citing works
Vinken, M., Doktorova, T. Y., Ellinger-Ziegelbauer, H. C., Ahr, H.-J., Lock, E. A., Carmichael, P. L., Roggen, E., VANDELFT, J., Kleinjans, J. C. S., & Castell, J. V. (2008) · Mutation Research/Reviews in Mutation Research · doi:10.1016/j.mrrev.2008.04.006 · Cited by 65 · see citing works
Pisani, C., Gaillard, J., Nouvel, V., Odorico, M., Armengaud, J., & Prat, O. (2015) · BMC Genomics · doi:10.1186/s12864-015-1521-5 · Cited by 64 · see citing works
Theunissen, P. T., Robinson, J. F., Pennings, J. L. A., de Jong, E., Claessen, S. M. H., Kleinjans, J. C. S., & Piersma, A. H. (2011) · Toxicological Sciences · doi:10.1093/toxsci/kfr293 · Cited by 60 · see citing works
BeadArray Expression Analysis Using Bioconductor
Ritchie, M. E., Dunning, M., Smith, M. L., Shi, W., & Lynch, A. G. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002276 · Cited by 56 · see citing works
Beronius, A., Johansson, N., Rudén, C., & Hanberg, A. (2013) · Toxicology · doi:10.1016/j.tox.2013.02.012 · Cited by 50 · see citing works
In silicotoxicity prediction by support vector machine and SMILES representation-based string kernel
Cao, D., Zhao, J.-C., Yang, Y.-N., Zhao, C., Yan, J., Liu, S., Hu, Q., Xu, Q., & Liang, Y. (2012) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2011.645874 · Cited by 45 · see citing works
Lan, J., Gou, N., Rahman, S. M., Gao, C., He, M., & Gu, A. Z. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.5b05097 · Cited by 44 · see citing works
Imanishi, S., Okura, M., Zaha, H., Yamamoto, T., Akanuma, H., Nagano, R., Shiraishi, H., Fujimaki, H., & Sone, H. (2011) · Environmental Toxicology · doi:10.1002/tox.20758 · Cited by 43 · see citing works
Jolly, R. A., Ahmed, K. B. R., Zwickl, C. M., Watson, I., & Gombar, V. K. (2015) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2015.01.010 · Cited by 41 · see citing works
He, X., Imanishi, S., Sone, H., Nagano, R., Qin, X., Yoshinaga, J., Akanuma, H., Yamane, J., Fujibuchi, W., & Ohsako, S. (2012) · Toxicology Letters · doi:10.1016/j.toxlet.2012.04.011 · Cited by 40 · see citing works
Gómez-Lechón, M. J., Tolosa, L., Castell, J. V., & Donato, M. T. (2010) · Toxicology in Vitro · doi:10.1016/j.tiv.2010.07.018 · Cited by 39 · see citing works
Imamura, L., Hasegawa, H., Kurashina, K., Hamanishi, A., Tabuchi, A., & Tsuda, M. (2000) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1016/s0022-3565(24)39020-2 · Cited by 34 · see citing works
In vitro gene regulatory networks predict in vivo function of liver
Deng, Y., Johnson, D. R., Guan, X., Ang, C. Y., Ai, J., & Perkins, E. J. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-153 · Cited by 34 · see citing works
Su, B., Tu, Y.-S., Esposito, E. X., & Tseng, Y. J. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300060b · Cited by 25 · see citing works
Yamanaka, T., Toyoshiba, H., Sone, H., Parham, F., & Portier, C. J. (2004) · Environmental Health Perspectives · doi:10.1289/txg.7105 · Cited by 24 · see citing works
Classification of heterogeneous microarray data by maximum entropy kernel
Fujibuchi, W., & Kato, T. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-267 · Cited by 15 · see citing works
Evaluation of DNA microarray results in the Toxicogenomics Project (TGP) consortium in Japan
Nakatsu, N., Igarashi, Y., Ono, A., Yamada, H., Ohno, Y., & Urushidani, T. (2012) · The Journal of Toxicological Sciences · doi:10.2131/jts.37.791 · Cited by 15 · see citing works
Nagano, R., Akanuma, H., Qin, X., Imanishi, S., Toyoshiba, H., Yoshinaga, J., Ohsako, S., & Sone, H. (2011) · International Journal of Molecular Sciences · doi:10.3390/ijms13010187 · Cited by 14 · see citing works
An Overview of Data Mining Algorithms in Drug Induced Toxicity Prediction
Omer, A., Singh, P., Yadav, N. K., & Singh, R. (2014) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/1389557514666140219110244 · Cited by 12 · see citing works
Neurotoxin-Induced Pathway Perturbation in Human Neuroblastoma SH-EP Cells
Do, J. H. (2014) · Molecules and Cells · doi:10.14348/molcells.2014.0173 · Cited by 11 · see citing works
QSAR modelling of integrin antagonists using enhanced Bayesian regularised genetic neural networks
Jalali‐Heravi, M., & Mani‐Varnosfaderani, A. (2011) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2011.569758 · Cited by 11 · see citing works
N., R., & Benzécri, J.-P. (1975) · Population · doi:10.2307/1531237 · Cited by 7 · see citing works
Developing a Practical Toxicogenomics Data Analysis System Utilizing Open-Source Software
Hirai, T., & Kiyosawa, N. (2012) · Methods in molecular biology · doi:10.1007/978-1-62703-059-5_16 · Cited by 3 · see citing works
Kernel Classification Methods for Cancer Microarray Data
Kato, T., & Fujibuchi, W. (2010) · doi:10.1002/9783527630332.ch14 · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 60 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [24]Fujibuchi W. Aburatani S. Yamane J. Imanishi S. Akanuma H. Sone H. Ohsako S. Prediction of chemical toxicity by network-based SVM on ES-cell validation system Proc. of the 2011 Joint Conference of CBI-Society and JSBi 2011 Japanese Society for Bioinformatics
Cited by
Sorted by citations
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
Oxidative Stress as a Common Key Event in Developmental Neurotoxicity
Nishimura, Y., Kanda, Y., Sone, H., & Aoyama, H. (2021) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2021/6685204 · Cited by 89 · see citing works
Prospects and Frontiers of Stem Cell Toxicology
Liu, S., Yin, N., & Faiola, F. (2017) · Stem Cells and Development · doi:10.1089/scd.2017.0150 · Cited by 65 · see citing works
Knowledge-guided deep learning models of drug toxicity improve interpretation
Hao, Y., Romano, J. D., & Moore, J. H. (2022) · Patterns · doi:10.1016/j.patter.2022.100565 · Cited by 40 · see citing works
Meng, L., Zhou, B., Liu, H., Chen, Y., Yuan, R., Chen, Z., Luo, S., & Chen, H. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.174201 · Cited by 34 · see citing works
QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors
Toma, C., Manganaro, A., Raitano, G., Marzo, M., Gadaleta, D., Baderna, D., Roncaglioni, A., Kramer, N. I., & Benfenati, E. (2020) · Molecules · doi:10.3390/molecules26010127 · Cited by 31 · see citing works
The promise of toxicogenomics for genetic toxicology: past, present and future
David, R. M. (2020) · Mutagenesis · doi:10.1093/mutage/geaa007 · Cited by 27 · see citing works
Su, R., Wu, H., Liu, X., & Wei, L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz165 · Cited by 26 · see citing works
Assessment of Toxic Effects of Ochratoxin A in Human Embryonic Stem Cells
Erceg, S., Mateo, E. M., Zipančić, I., Jiménez, F. R., Aragó, M. A. P., Jiménez, M., Soria, J. M., & García-Esparza, M. A. (2019) · Toxins · doi:10.3390/toxins11040217 · Cited by 26 · see citing works
Stem Cell Based Preclinical Drug Development and Toxicity Prediction
Kumar, D., Baligar, P., Srivastav, R., Narad, P., Raj, S., Tandon, C., & Tandon, S. (2020) · Current Pharmaceutical Design · doi:10.2174/1381612826666201019104712 · Cited by 19 · see citing works
Reverse-engineering biological networks from large data setsPreprint
Natale, J. L., Hofmann, D., Hernández, D. G., & Nemenman, I. M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/142034 · Cited by 17 · see citing works
Exploring toxicity of perfluorinated compounds through complex network and pathway modeling
Liu, X., Zhu, Y., Liu, T., Xue, Q., Fang, T., Yuan, Y., & Zhao, C. (2019) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2019.1637281 · Cited by 14 · see citing works
Li, H., Ding, C., Ding, Z., Ling, M., Wang, T., Wang, W. T., & Huang, B. (2017) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.13090 · Cited by 12 · see citing works
de Souza, T. M., Kleinjans, J. C. S., & Jennen, D. G. J. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00142 · Cited by 11 · see citing works
Knowledge graph aids comprehensive explanation of drug and chemical toxicity
Hao, Y., Romano, J. D., & Moore, J. H. (2023) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12975 · Cited by 9 · see citing works
Using Chemical-Induced Gene Expression in Cultured Human Cells to Predict Chemical Toxicity
Liu, R., Yu, X., & Wallqvist, A. (2016) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.6b00287 · Cited by 8 · see citing works
Rim, K.-T. (2023) · Molecular & Cellular Toxicology · doi:10.1007/s13273-023-00387-6 · Cited by 8 · see citing works
Yamane, J., Wada, T., Otsuki, H., Inomata, K., Suzuki, M., Hisaki, T., Sekine, S., Kouzuki, H., Kobayashi, K., Sone, H., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104538 · Cited by 7 · see citing works
Zang, R., Xin, X., Zhang, F., Li, D., & Yang, S. (2019) · Biotechnology and Bioengineering · doi:10.1002/bit.26977 · Cited by 7 · see citing works
Stem Cell-Based Methods to Predict Developmental Chemical Toxicity
Takahashi, H., Qin, X., Sone, H., & Fujibuchi, W. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7899-1_21 · Cited by 5 · see citing works
Shimizu, M., Tachikawa, S., Saitoh, N., Nakazono, K., Liu, Y., Suga, M., & Ohnuma, K. (2021) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2021.100978 · Cited by 5 · see citing works
Kikegawa, M., Qin, X., Ito, T., Nishikawa, H., Nansai, H., & Sone, H. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21155564 · Cited by 3 · see citing works
Sone, H. (2025) · YAKUGAKU ZASSHI · doi:10.1248/yakushi.24-00168 · Cited by 2 · see citing works
Insecticide levels in house dust collected around Osaka from 2011 to 2015
Noguchi, M., Watanabe, M., Fukuda, S., & YOSHIDA, S. (2017) · Indoor Environment · doi:10.7879/siej.20.11 · Cited by 2 · see citing works
Otsuka, S., Qin, X., Wang, W., Ito, T., Nansai, H., Abe, K., Fujibuchi, W., Nakao, Y., & Sone, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-33729-4 · Cited by 2 · see citing works
Linearly Convergent Mixup Learning
Obi, G., Saito, A., Sasaki, Y., & Kato, T. (2025) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit63088.2025.11195461 · Cited by 1 · see citing works
Daston, G. P., Burbank, M., Gautier, F., Hales, B. F., Jamalpoor, A., Kanda, Y., Makris, S. L., Piersma, A. H., Powles-Glover, N., Sobrian, S. K., et al. (2025) · Reproductive Toxicology · doi:10.1016/j.reprotox.2025.109119 · Cited by 1 · see citing works
AI-based methods for the assessment of DNA damage and repair mechanisms
Lecca, P., Lecca, M., & Ihekwaba, A. (2026) · Frontiers in Systems Biology · doi:10.3389/fsysb.2026.1734322 · Cited by 1 · see citing works
Panina, Y., Yamane, J., Kobayashi, K., Sone, H., & Fujibuchi, W. (2021) · The Journal of Toxicological Sciences · doi:10.2131/jts.46.131 · Cited by 1 · see citing works
Yamane, J., Wada, T., Otsuki, H., Inomata, K., Suzuki, M., Hisaki, T., Sekine, S., Kouzuki, H., Kobayashi, K., Sone, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.05.466718
Toxicogenomics for the prediction of carcinogenicity of xenobiotic substances
Srivastava, A. K., Kumar, D., Singh, D. R., & Singh, R. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90560-2.00005-4
Hunt, D. J., Branson, M., Putnam, E. A., & Pershouse, M. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-813724-6.00004-9
Computational approaches for smart cell creation in the bioeconomy era
Aburatani, S., Ishiya, K., Shirai, T., Shida, Y., Ogasawara, W., Takaku, H., & Tamura, T. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91601-1.00012-2
KNOWLEDGE GRAPH AIDS COMPREHENSIVE EXPLANATION OF DRUG TOXICITYPreprint
Hao, Y., Romano, J. D., & Moore, J. H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.07.511348
Sone, H., Qin, X., Hayamizu, K., Fujibuchi, W., & Nakao, Y. (2022) · Journal of Nutritional Science and Vitaminology · doi:10.3177/jnsv.68.s131
Manoj, M., Bhagavathi, K., Fathima, S. A. N., Pillai, S. S., Malarvizhi, M., Shenbagam, M., Velayuthaprabhu, S., & Anand, A. V. (2026) · CABI biotechnology series. · doi:10.1079/9781800628649.0001
- Authors
- Yamane, J., Aburatani, S., Imanishi, S., Akanuma, H., Nagano, R., Kato, T., Sone, H., Ohsako, S., & Fujibuchi, W.
- Affiliations
- Kyoto University (JP)
- National Institute of Advanced Industrial Science and Technology (JP)
- Center for Disease Biology and Integrative Medicine, The University of Tokyo (JP)
- National Institute for Environmental Studies (JP)
- Gunma University (JP)
- Corresponding author
- Fujibuchi, Wataru
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-05-20
- Volume
- 44
- Issue
- 12
- Pages
- 5515-5528
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC4937330
- OpenAlex
- W2400926507
- Cited by
- 37
- References
- 60
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 1 · 2023: 3 · 2022: 6 · 2021: 4 · 2020: 6 · 2019: 4 · 2018: 2 · 2017: 5 · 2016: 1
- FWCI
- 3.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- Kyoto University Research Information Repository (Kyoto University) (submittedVersion, cc-by-nc)
- Funders
- Research Center Network for Realization of Regenerative Medicine
- Ministry of Health, Labour and Welfare
- Japan Society for the Promotion of Science (15H01749, 15H02830)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Carcinogens and Genotoxicity Assessment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- human embryonic stem cells, gene networks, support vector machine, toxicogenomics, neurotoxins, genotoxic carcinogens, non-genotoxic carcinogens, bisphenol A, permethrin, stochastic gene expression, predictive toxicology
- MeSH terms
- Human Embryonic Stem Cells, Bisphenol A Compounds, Benzhydryl Compounds, Carcinogens, DNA Damage, Humans, Neurotoxins, Phenols, Computational Biology, Quantitative Structure-Activity Relationship, Permethrin, Gene Regulatory Networks, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
