Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works

Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,614 · 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

affy—analysis of Affymetrix GeneChip data at the probe level

Gautier, L., Cope, L., Bolstad, B. M., & Irizarry, R. A. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg405 · Cited by 5,221 · see citing works

Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics

Li, Y., Yu, C. Y., Li, X. X., Zhang, P., Tang, J., Yang, Q., Fu, T., Zhang, X., Cui, X., Tu, G., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1076 · Cited by 523 · see citing works

Open TG-GATEs: a large-scale toxicogenomics database

Igarashi, Y., Nakatsu, N., Yamashita, T., Ono, A., Ohno, Y., Urushidani, T., & Yamada, H. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku955 · Cited by 465 · see citing works

A novel features ranking metric with application to scalable visual and bioinformatics data classification

Zou, Q., Zeng, J., Cao, L., & Ji, R. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.12.123 · Cited by 396 · see citing works

NOREVA: normalization and evaluation of MS-based metabolomics data

Li, B., Tang, J., Yang, Q., Li, S., Cui, X., Li, Y., Chen, Y., Xue, W., Li, X., & Zhu, F. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx449 · Cited by 357 · see citing works

Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

Zhang, W., Chen, Y., Liu, F., Luo, F., Tian, G., & Li, X. (2017) · BMC Bioinformatics · doi:10.1186/s12859-016-1415-9 · Cited by 356 · see citing works

SVM-RFE With MRMR Filter for Gene Selection

Mundra, P. A., & Rajapakse, J. C. (2009) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2009.2035284 · Cited by 335 · see citing works

The Role of Absorption, Distribution, Metabolism, Excretion and Toxicity in Drug Discovery

Lin, J., Sahakian, D. C., de Morais, S., Xu, J. J., Polzer, R. J., & Winter, S. M. (2003) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026033452096 · Cited by 329 · see citing works

Predicting drug-disease associations by using similarity constrained matrix factorization

Zhang, W., Yue, X., Lin, W., Wu, W., Liu, R., Huang, F. F., & Liu, F. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2220-4 · Cited by 303 · 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

Identification of drug-target interactions via multiple information integration

Ding, Y., Tang, J., & Guo, F. (2017) · Information Sciences · doi:10.1016/j.ins.2017.08.045 · Cited by 195 · see citing works

Predicting drug side effects by multi-label learning and ensemble learning

Zhang, W., Liu, F., Luo, L., & Zhang, J. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0774-y · Cited by 187 · see citing works

Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy

Zou, Q., Wan, S., Ju, Y., Tang, J., & Zeng, X. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0353-5 · Cited by 158 · see citing works

Predicting potential side effects of drugs by recommender methods and ensemble learning

Zhang, W., Zou, H., Luo, L., Liu, Q., Wu, W., & Xiao, W. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.08.054 · Cited by 146 · see citing works

Fast Correlation Based Filter (FCBF) with a different search strategy

Senliol, B., Gülgezen, G., Yu, L., & Çataltepe, Z. (2008) · International Symposium on Computer and Information Sciences · doi:10.1109/iscis.2008.4717949 · Cited by 139 · see citing works

Toward Predictive Models for Drug-Induced Liver Injury in Humans: Are we There Yet?

Chen, M., Bişğin, H., Tong, L., Hong, H., Fang, H., Borlak, J. T., & Tong, W. (2014) · Biomarkers in Medicine · doi:10.2217/bmm.13.146 · Cited by 136 · see citing works

Protein Folds Prediction with Hierarchical Structured SVM

Li, D., Ju, Y., & Zou, Q. (2016) · Current Proteomics · doi:10.2174/157016461302160514000940 · Cited by 126 · see citing works

Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

Chen, W., Xing, P., & Zou, Q. (2017) · Scientific Reports · doi:10.1038/srep40242 · Cited by 122 · see citing works

Acute Hepatotoxicity: A Predictive Model Based on Focused Illumina Microarrays

Zidek, N., Hellmann, J., Kramer, P.-J., & Hewitt, P. G. (2007) · Toxicological Sciences · doi:10.1093/toxsci/kfm131 · Cited by 105 · see citing works

Clinical Success of Drug Targets Prospectively Predicted by In Silico Study

Zhu, F., Li, X. X., Yang, S. Y., & Chen, Y. (2017) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2017.12.002 · Cited by 100 · see citing works

Gene selection using information gain and improved simplified swarm optimization

Lai, C.-M., Yeh, W., & Chang, C. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.08.089 · Cited by 99 · see citing works

PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm

Xu, Q., Xiong, Y., Dai, H., Kumari, K. M., Xu, Q., Ou, H., & Wei, D. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.01.019 · Cited by 98 · see citing works

Feature-derived graph regularized matrix factorization for predicting drug side effects

Zhang, W., Liu, X., Chen, Y., Wu, W., Wang, W., & Li, X. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.01.085 · Cited by 98 · see citing works

Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network

Zhang, W., Yue, X., Huang, F. F., Liu, R., Chen, Y., & Ruan, C. (2018) · Methods · doi:10.1016/j.ymeth.2018.06.001 · Cited by 93 · see citing works

Protein-protein interface hot spots prediction based on a hybrid feature selection strategy

Qiao, Y., Xiong, Y., Gao, H., Zhu, X., & Chen, P. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2009-5 · Cited by 92 · see citing works

Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks

Xiao, Y., Zhang, J., & Deng, L. (2017) · Scientific Reports · doi:10.1038/s41598-017-03986-1 · Cited by 90 · see citing works

Accurate prediction of human drug toxicity: a major challenge in drug development

Li, A. P. (2004) · Chemico-Biological Interactions · doi:10.1016/j.cbi.2004.09.008 · Cited by 82 · see citing works

Identification of Drug-Drug Interactions Using Chemical Interactions

Chen, L., Chu, C., Zhang, Y., Zheng, M., Zhu, L., Kong, X., & Huang, T. (2016) · Current Bioinformatics · doi:10.2174/1574893611666160618094219 · Cited by 81 · see citing works

Identification of Protein–Ligand Binding Sites by Sequence Information and Ensemble Classifier

Ding, Y., Tang, J., & Guo, F. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00307 · Cited by 80 · see citing works

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches

Singh, K. P., Gupta, S. P., & Rai, P. (2013) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2013.05.017 · Cited by 79 · see citing works

Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Geng, J., Li, M., Dong, Z., & Liao, Y. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.06.070 · Cited by 78 · see citing works

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information

Zhang, W., Chen, Y., & Li, D. (2017) · Molecules · doi:10.3390/molecules22122056 · Cited by 77 · see citing works

A QSAR for Baseline Toxicity: Validation, Domain of Application, and Prediction

Öberg, T. (2004) · Chemical Research in Toxicology · doi:10.1021/tx0498253 · Cited by 75 · see citing works

Drug side effect prediction through linear neighborhoods and multiple data source integration

Zhang, W., Chen, Y., Tu, S., Liu, F., & Qu, Q. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822555 · Cited by 71 · see citing works

High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures

Su, R., Xiong, S., Zink, D., & Loo, L. (2015) · Archives of Toxicology · doi:10.1007/s00204-015-1638-y · Cited by 64 · see citing works

A unified frame of predicting side effects of drugs by using linear neighborhood similarity

Zhang, W., Yue, X., Liu, F., Chen, Y., Tu, S., & Zhang, X. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0477-2 · Cited by 63 · see citing works

Use of pharmacogenetics for predicting cancer prognosis and treatment exposure, response and toxicity

Hertz, D. L., & McLeod, H. L. (2013) · Journal of Human Genetics · doi:10.1038/jhg.2013.42 · Cited by 61 · see citing works

Drug and Nondrug Classification Based on Deep Learning with Various Feature Selection Strategies

Yu, L., Sun, X., Tian, S., Shi, X., & Yan, Y. (2018) · Current Bioinformatics · doi:10.2174/1574893612666170125124538 · Cited by 59 · see citing works

Analysis and Prediction of Nitrated Tyrosine Sites with the mRMR Method and Support Vector Machine Algorithm

Wang, S. P., Zhang, Q., Lu, J., & Cai, Y. (2016) · Current Bioinformatics · doi:10.2174/1574893611666160608075753 · Cited by 57 · see citing works

Antiparasitic efficacy of curcumin from Curcuma longa against Ichthyophthirius multifiliis in grass carp

Liu, Y., Zhang, Q., Xu, D., Fu, Y., Lin, D.-J., Zhou, S.-Y., & Li, J.-P. (2017) · Veterinary Parasitology · doi:10.1016/j.vetpar.2017.02.011 · Cited by 50 · see citing works

Predicting drug-disease associations based on the known association bipartite network

Zhang, W., Yue, X., Chen, Y., Lin, W., Li, B., Liu, F., & Li, X. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217698 · Cited by 49 · see citing works

An Ameliorated Prediction of Drug–Target Interactions Based on Multi-Scale Discrete Wavelet Transform and Network Features

Shen, C., Ding, Y., Tang, J., Xu, X., & Guo, F. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18081781 · Cited by 48 · see citing works

Boosting Prediction Performance of Protein–Protein Interaction Hot Spots by Using Structural Neighborhood Properties

Deng, L., Guan, J., Wei, X., Yi, Y., Zhang, Q. C., & Zhou, S. (2013) · Journal of Computational Biology · doi:10.1089/cmb.2013.0083 · Cited by 47 · see citing works

A boosting approach for prediction of protein-RNA binding residues

Tang, Y., Liu, D., Wang, Z., Wen, T., & Deng, L. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1879-2 · Cited by 42 · see citing works

Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and -8 expression levels

Su, R., Li, Y., Zink, D., & Loo, L. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s16-s16 · Cited by 41 · see citing works

Dose-Response of High-Intensity Training (HIT) on Atheroprotective miRNA-126 Levels

Schmitz, B., Schelleckes, K., Nedele, J., Thorwesten, L., Klose, A., Lenders, M., Krüger, M., Brand, E., & Brand, S. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00349 · Cited by 41 · see citing works

Discriminating ramos and jurkat cells with image textures from diffraction imaging flow cytometry based on a support vector machine

Zhang, N., Sa, Y., Guo, Y., Lin, W., Wang, P., & Feng, Y. (2018) · Current Bioinformatics · doi:10.2174/1574893611666160608102537 · Cited by 37 · see citing works

Toxygates: interactive toxicity analysis on a hybrid microarray and linked data platform

Nyström-Persson, J., Igarashi, Y., Ito, M., Morita, M., Nakatsu, N., Yamada, H., & Mizuguchi, K. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt531 · Cited by 36 · see citing works

A possible dose–response association between distance to farmers’ markets and roadside produce stands, frequency of shopping, fruit and vegetable consumption, and body mass index among customers in the Southern United States

Pitts, S. J., Hinkley, J., Wu, Q., McGuirt, J. T., Lyonnais, M. J., Rafferty, A. P., Whitt, O. R., Winterbauer, N. L., & Phillips, L. (2017) · BMC Public Health · doi:10.1186/s12889-016-3943-7 · Cited by 33 · see citing works

Cytokine Levels in Panic Disorder: Evidence for a Dose-Response Relationship

da Silva, C. T. B., Costa, M. D. A., Bortoluzzi, A., Pfaffenseller, B., Vedana, F. M., Kapczinski, F. P., & Manfro, G. G. (2016) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000384 · Cited by 29 · see citing works

An Integrated Framework for Functional Annotation of Protein Structural Domains

Deng, L., & Chen, Z. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2389213 · Cited by 27 · see citing works

Systems toxicology of chemically induced liver and kidney injuries: histopathology‐associated gene co‐expression modules

Te, J. A., AbdulHameed, M. D. M., & Wallqvist, A. (2016) · Journal of Applied Toxicology · doi:10.1002/jat.3278 · Cited by 24 · see citing works

Assessing the chronic toxicity of copper and aluminium to the tropical sea anemone Exaiptasia pallida

Trenfield, M. A., van Dam, J. W., Harford, A. J., Parry, D. L., Streten, C., Gibb, K. S., & van Dam, R. A. (2017) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2017.02.007 · Cited by 23 · see citing works

Quantitative prediction of drug side effects based on drug-related features

Niu, Y., & Zhang, W. (2017) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-017-0236-5 · Cited by 23 · see citing works

CHEMICAL CONSTITUENTS, IN VITRO ANTIOXIDANT ACTIVITY, ORAL ACUTE TOXICITY AND LD50 DETERMINATION OF MORINGA OLEIFERA LEAVES

Hamed, M. M., Abdalla, A. M., Ghareeb, M. A., & Saleh, S. A. (2017) · International Journal of Pharmacy and Pharmaceutical Sciences · doi:10.22159/ijpps.2017v9i5.17834 · Cited by 20 · see citing works

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

Papamokos, G., & Silins, I. (2016) · Frontiers in Pharmacology · doi:10.3389/fphar.2016.00284 · Cited by 15 · see citing works

Single toxin dose-response models revisited

Demidenko, E., Glaholt, S., Kyker‐Snowman, E., Shaw, J., & Chen, C. (2016) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2016.11.002 · Cited by 15 · see citing works

Improved Prediction of Michaelis Constants in CYP450-Mediated Reactions by Resilient Back Propagation Algorithm

Dai, H., Xu, Q., Xiong, Y., Liu, W., & Wei, D. (2016) · Current Drug Metabolism · doi:10.2174/1389200217666160513144551 · Cited by 10 · see citing works

Natural abenquines and synthetic analogues: Preliminary exploration of their cytotoxic activity

Nain‐Perez, A., Barbosa, L. C. A., Rodríguez‐Hernández, D., Kramell, A. E., Heller, L., & Csük, R. (2017) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2017.01.079 · Cited by 10 · see citing works

Quantitative Structure–Toxicity Prediction of log(1/EC50) for Some Benzene Derivatives from Their Density Functional Theory Calculated Molecular Descriptors

Haghdadi, M., & Fatemi, M. H. (2010) · Bulletin of the Chemical Society of Japan · doi:10.1246/bcsj.20090309 · Cited by 6 · see citing works

Assessment of Heavy Metal Induced Organ Toxicity in marketed Ayurvedhic Formulation and Report its LD50 value with Brine Shrimp Lethality Assay

Saranya, A., & Nithya, S. (2017) · Research Journal of Pharmacy and Technology · doi:10.5958/0974-360x.2017.00054.3 · Cited by 6 · see citing works

Mechanism-Based Markers of Drug-Induced Liver Injury to Improve the Physiological Relevance and Predictivity of In Vitro Models

Goldring, C. E., Norris, A. M., Kitteringham, N. R., Aleo, M. D., Antoine, D. J., Heslop, J. A., Howell, B. A., Ingelman‐Sundberg, M., Kia, R., Kamalian, L., et al. (2015) · Applied In Vitro Toxicology · doi:10.1089/aivt.2015.0001 · Cited by 5 · see citing works

Toxicogenomic prediction with group sparse regularization based on transcription factor network information

Nagata, K., Kawahara, Y., Washio, T., & Unami, A. (2015) · Fundamental Toxicological Sciences · doi:10.2131/fts.2.161 · Cited by 2 · see citing works

QSAR Analysis of Acute Toxicity of Chloroanilines to Zebra-Fish

Chang-jun, F. (2012) · Environmental Science & Technology · Cited by 1 · see citing works

Distance-based Support Vector Machine to Predict DNA N6- methyladenine Modification

Zhang, H., Zou, Q., Ju, Y., Song, C., & Chen, D. (2022) · Current Bioinformatics · doi:10.2174/1574893617666220404145517 · Cited by 321 · see citing works

Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response

Su, R., Liu, X., Wei, L., & Zou, Q. (2019) · Methods · doi:10.1016/j.ymeth.2019.02.009 · Cited by 241 · see citing works

Clustering and classification methods for single-cell RNA-sequencing data

Qi, R., Ma, A., Ma, Q., & Zou, Q. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz062 · Cited by 210 · see citing works

Application of Machine Learning in Microbiology

Qu, K., Guo, F., Liu, X., Lin, Y., & Zou, Q. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.00827 · Cited by 199 · see citing works

ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

Rao, B., Zhou, C., Zhang, G., Su, R., & Wei, L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz088 · Cited by 178 · see citing works

A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning

Han, K., Cao, P., Wang, Y., Xie, F., Ma, J., Yu, M., Wang, J., Xu, Y., Zhang, Y., & Wan, J. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.814858 · Cited by 178 · see citing works

Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species

Wei, L., Luan, S., Nagai, L. A. E., Su, R., & Zou, Q. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty824 · Cited by 175 · see citing works

Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework

Wei, L., He, W., Malik, A., Su, R., Cui, L., & Manavalan, B. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa275 · Cited by 162 · see citing works

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Jeong, J., & Choi, J. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.1c07413 · Cited by 147 · see citing works

Anticancer peptides prediction with deep representation learning features

Lv, Z., Cui, F., Zou, Q., Zhang, L., & Xu, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab008 · Cited by 145 · see citing works

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Kim, H., Kim, E., Lee, I., Bae, B., Park, M., & Nam, H. (2020) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-020-0049-y · Cited by 138 · see citing works

StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides

Charoenkwan, P., Chiangjong, W., Nantasenamat, C., Hasan, M. M., Manavalan, B., & Shoombuatong, W. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab172 · Cited by 134 · see citing works

MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters

Zhang, M., Li, F., Marquez‐Lago, T. T., Leier, A., Fan, C., Kwoh, C. K., Chou, K., Song, J., & Jia, C. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz016 · Cited by 124 · see citing works

iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree

Basith, S., Manavalan, B., Shin, T. H., & Lee, G. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.10.007 · Cited by 112 · see citing works

An Efficient Classifier for Alzheimer’s Disease Genes Identification

Xu, L., Liang, G., Liao, C., Chen, G.-D., & Chang, C.-C. (2018) · Molecules · doi:10.3390/molecules23123140 · Cited by 107 · see citing works

A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features

Lv, Z., Jin, S., Ding, H., & Zou, Q. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00215 · Cited by 105 · see citing works

Review of machine learning and deep learning models for toxicity prediction

Guo, W., Liu, J., Dong, F., Song, M., Li, Z., Khan, M. K. H., Patterson, T. A., & Hong, H. (2023) · Experimental Biology and Medicine · doi:10.1177/15353702231209421 · Cited by 104 · see citing works

StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides

Charoenkwan, P., Nantasenamat, C., Hasan, M. M., Moni, M. A., Lió, P., Manavalan, B., & Shoombuatong, W. (2021) · Methods · doi:10.1016/j.ymeth.2021.12.001 · Cited by 102 · see citing works

Gradient boosting decision tree-based method for predicting interactions between target genes and drugs

Xuan, P., Sun, C., Zhang, T., Ye, Y., Shen, T., & Dong, Y. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00459 · Cited by 100 · see citing works

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network

Chen, J., Si, Y., Un, C.-W., & Siu, S. W. I. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00570-8 · Cited by 98 · see citing works

Fusing convolutional neural network features with hand-crafted features for osteoporosis diagnoses

Su, R., Liu, T., Sun, C., Jin, Q., Jennane, R., & Wei, L. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.12.083 · Cited by 80 · see citing works

Sequence representation approaches for sequence-based protein prediction tasks that use deep learning

Cui, F., Zhang, Z., & Zou, Q. (2020) · Briefings in Functional Genomics · doi:10.1093/bfgp/elaa030 · Cited by 79 · see citing works

k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

Xu, L., Liang, G., Liao, C., Chen, G., & Chang, C. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00033 · Cited by 77 · see citing works

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Serra, A., Fratello, M., Cattelani, L., Liampa, I., Melagraki, G., Kohonen, P. J., Nymark, P. E. H., Federico, A., Kinaret, P., Jagiełło, K., et al. (2020) · Nanomaterials · doi:10.3390/nano10040708 · Cited by 72 · see citing works

An effective framework for predicting drug–drug interactions based on molecular substructures and knowledge graph neural network

Chen, S., Semenov, I., Zhang, F., Yang, Y., Geng, J., Feng, X., Meng, Q., & Lei, K. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107900 · Cited by 68 · see citing works

Exposing the Causal Effect of Body Mass Index on the Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study

Cheng, L., Zhuang, H., Ju, H., Yang, S., Han, J., Tan, R., & Hu, Y. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00094 · Cited by 66 · see citing works

Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses

Wang, H., Liu, R., Schyman, P., & Wallqvist, A. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00042 · Cited by 61 · see citing works

DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model

Pu, Y., Li, J., Tang, J., & Guo, F. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3103966 · Cited by 59 · see citing works

Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases

Jiao, S., Song, W. C., Huang, S., Liu, M., & Gao, B. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.665233 · Cited by 56 · see citing works

Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction

Guo, Z., Wang, P., Liu, Z., & Zhao, Y. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.584807 · Cited by 50 · see citing works

iEnhancer-EBLSTM: Identifying Enhancers and Strengths by Ensembles of Bidirectional Long Short-Term Memory

Niu, K., Luo, X., Zhang, S., Teng, Z., Zhang, T., & Zhao, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.665498 · Cited by 48 · see citing works

Exploiting Discriminative Regions of Brain Slices Based on 2D CNNs for Alzheimer’s Disease Classification

Ren, F., Yang, C., Qiu, Q., Zeng, N., Cai, C., Hou, C., & Zou, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2920241 · Cited by 45 · see citing works

SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning

Li, J., Zhang, L., Shida, H., Guo, F., & Zou, Q. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa401 · Cited by 44 · see citing works

Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review

Liu, S., Shi, T., Yu, J., Li, R., Lin, H., & Deng, K. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25189844 · Cited by 41 · see citing works

Supervised Brain Tumor Segmentation Based on Gradient and Context-Sensitive Features

Zhao, J., Meng, Z., Wei, L., Sun, C., Zou, Q., & Su, R. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00144 · Cited by 41 · see citing works

SRDFM: Siamese Response Deep Factorization Machine to improve anti-cancer drug recommendation

Su, R., Huang, Y., Zhang, D., Xiao, G., & Wei, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab534 · Cited by 41 · see citing works

WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy

Chen, Q., Meng, Z., & Su, R. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00496 · Cited by 39 · see citing works

ACP-GCN: The Identification of Anticancer Peptides Based on Graph Convolution Networks

Rao, B., Zhang, L., & Zhang, G. (2020) · IEEE Access · doi:10.1109/access.2020.3023800 · Cited by 39 · see citing works

Identification of expression signatures for non-small-cell lung carcinoma subtype classification

Su, R., Zhang, J., Liu, X., & Wei, L. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz557 · Cited by 37 · see citing works

iGlu_AdaBoost: Identification of Lysine Glutarylation Using the AdaBoost Classifier

Dou, L., Li, X., Zhang, L., Xiang, H., & Xu, L. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00314 · Cited by 36 · see citing works

dPromoter-XGBoost: Detecting promoters and strength by combining multiple descriptors and feature selection using XGBoost

Li, H., Shi, L., Gao, W., Zhang, Z., Zhang, L., Zhao, Y., & Wang, G. (2022) · Methods · doi:10.1016/j.ymeth.2022.01.001 · Cited by 36 · see citing works

EP3: an ensemble predictor that accurately identifies type III secreted effectors

Li, J., Wei, L., Guo, F., & Zou, Q. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa008 · Cited by 34 · see citing works

Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications

Zeng, R., & Liao, M. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00274 · Cited by 33 · see citing works

Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity

Gardiner, L., Carrieri, A. P., Wilshaw, J., Checkley, S., Pyzer‐Knapp, E. O., & Krishna, R. (2020) · Scientific Reports · doi:10.1038/s41598-020-66481-0 · Cited by 32 · see citing works

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors

Malik, A. A., Chotpatiwetchkul, W., Phanus‐umporn, C., Nantasenamat, C., Charoenkwan, P., & Shoombuatong, W. (2021) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-021-00418-1 · Cited by 30 · see citing works

Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction

Dai, Q., Guo, M., Duan, X., Teng, Z., & Fu, Y. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00018 · Cited by 30 · see citing works

Identification and classification of promoters using the attention mechanism based on long short-term memory

Li, Q., Zhang, L., Xu, L., Zou, Q., Wu, J., & Li, Q. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0548-9 · Cited by 29 · see citing works

A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data

Su, R., Yang, H., Wei, L., Chen, S., & Zou, Q. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010402 · Cited by 29 · see citing works

PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

Li, Y., Zhang, Z., Teng, Z., & Liu, X. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/8845133 · Cited by 27 · see citing works

Application of Multilayer Network Models in Bioinformatics

Lv, Y., Huang, S., Zhang, T., & Gao, B. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.664860 · Cited by 27 · see citing works

Identification of adaptor proteins using the ANOVA feature selection technique

Wang, Y., Zhang, Y., Zhang, Y., Gu, Z.-F., Zhang, Z., Lin, H., & Deng, K. (2022) · Methods · doi:10.1016/j.ymeth.2022.10.008 · Cited by 27 · see citing works

Predicting drug-induced hepatotoxicity based on biological feature maps and diverse classification strategies

Su, R., Wu, H., Liu, X., & Wei, L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz165 · Cited by 26 · see citing works

Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China

Bai, Z., Chen, M., Lin, Q., Ye, Y., Fan, H., Wen, K., Zeng, J., Huang, D., Mo, W., Lei, Y., et al. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.629681 · Cited by 26 · see citing works

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

Zhao, D., Teng, Z., Li, Y., & Chen, D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.773202 · Cited by 25 · see citing works

rBPDL:Predicting RNA-Binding Proteins Using Deep Learning

Niu, M., Wu, J., Zou, Q., Liu, Z., & Xu, L. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3069259 · Cited by 24 · see citing works

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

Gong, Y., Liao, B., Wang, P., & Zou, Q. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.771808 · Cited by 24 · see citing works

Identification of Triple-Negative Breast Cancer Genes and a Novel High-Risk Breast Cancer Prediction Model Development Based on PPI Data and Support Vector Machines

Li, M., Guo, Y. F., Feng, Y., & Zhang, N. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00180 · Cited by 23 · see citing works

CWLy-pred: A novel cell wall lytic enzyme identifier based on an improved MRMD feature selection method

Meng, C., Wu, J., Guo, F., Dong, B., & Xu, L. (2020) · Genomics · doi:10.1016/j.ygeno.2020.08.015 · Cited by 22 · see citing works

Unlocking the microbial studies through computational approaches: how far have we reached?

Kumar, R., Yadav, G., Kuddus, M., Ashraf, G. M., & Singh, R. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-26220-0 · Cited by 21 · see citing works

Identification of miR-200c and miR141-Mediated lncRNA-mRNA Crosstalks in Muscle-Invasive Bladder Cancer Subtypes

Liu, G., Chen, Z., Danilova, I. G., Bolkov, M. A., Тузанкина, И. А., & Liu, G. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00422 · Cited by 21 · see citing works

FunEffector-Pred: Identification of Fungi Effector by Activate Learning and Genetic Algorithm Sampling of Imbalanced Data

Wang, C., Wang, P., Han, S., Wang, L., Zhao, Y., & Juan, L. (2020) · IEEE Access · doi:10.1109/access.2020.2982410 · Cited by 21 · see citing works

Identification of glioblastoma molecular subtype and prognosis based on deep MRI features

Su, R., Liu, X., Jin, Q., Liu, X., & Wei, L. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107490 · Cited by 20 · see citing works

Predicting Ion Channels Genes and Their Types With Machine Learning Techniques

Han, K., Wang, M., Zhang, L., Wang, Y., Guo, M., Zhao, M. J., Zhao, Q., Zhang, Y., Zeng, N., & Wang, C. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00399 · Cited by 19 · see citing works

iRNA-m5C_NB: A Novel Predictor to Identify RNA 5-Methylcytosine Sites Based on the Naive Bayes Classifier

Dou, L., Li, X.-L., Ding, H., Xu, L., & Xiang, H. (2020) · IEEE Access · doi:10.1109/access.2020.2991477 · Cited by 17 · see citing works

Analysis of Cyclin-Dependent Kinase 1 as an Independent Prognostic Factor for Gastric Cancer Based on Statistical Methods

Zhang, X., Ma, H., Zou, Q., & Wu, J. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.620164 · Cited by 17 · see citing works

6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning

Huang, Q., Zhou, W., Guo, F., Xu, L., & Zhang, L. (2021) · PeerJ · doi:10.7717/peerj.10813 · Cited by 17 · see citing works

Identification of hormone-binding proteins using a novel ensemble classifier

Wang, K., Li, S., Wang, Q., & Hou, C. (2018) · Computing · doi:10.1007/s00607-018-0682-x · Cited by 16 · see citing works

AAPred-CNN: Accurate predictor based on deep convolution neural network for identification of anti-angiogenic peptides

Lin, C., Wang, L., & Shi, L. (2022) · Methods · doi:10.1016/j.ymeth.2022.01.004 · Cited by 16 · see citing works

TMPpred: A support vector machine-based thermophilic protein identifier

Meng, C., Ju, Y., & Shi, H. W. (2022) · Analytical Biochemistry · doi:10.1016/j.ab.2022.114625 · Cited by 16 · see citing works

Identification of Phage Viral Proteins With Hybrid Sequence Features

Ru, X., Li, L., & Wang, C. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.00507 · Cited by 16 · see citing works

iDNA-MT: Identification DNA Modification Sites in Multiple Species by Using Multi-Task Learning Based a Neural Network Tool

Yang, X., Ye, X., Li, X., & Wei, L. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.663572 · Cited by 15 · see citing works

Distant metastasis identification based on optimized graph representation of gene interaction patterns

Su, R., Zhu, Y., Zou, Q., & Wei, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab468 · Cited by 15 · see citing works

Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods

Gu, X., Chen, Z., & Wang, D. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00635 · Cited by 15 · see citing works

NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data

Wang, C., Wu, J., Xu, L., & Zou, Q. (2020) · Microbial Genomics · doi:10.1099/mgen.0.000483 · Cited by 14 · see citing works

Prediction of lncRNA–Protein Interactions via the Multiple Information Integration

Chen, Y., Fu, X., Li, Z., Li, P., & Zhuo, L. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.647113 · Cited by 14 · see citing works

Identification of Secreted Proteins From Malaria Protozoa With Few Features

Li, Q., Dong, B., Wang, D., & Wang, S. (2020) · IEEE Access · doi:10.1109/access.2020.2994206 · Cited by 13 · see citing works

Predicting the Disease Risk of Protein Mutation Sequences With Pre-training Model

Li, K., Zhong, Y., Lin, X., & Quan, Z. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.605620 · Cited by 12 · see citing works

Recent Advances in Predicting Protein S‐Nitrosylation Sites

Zhao, Q., Ma, J., Xie, F., Wang, Y., Zhang, Y., Li, H., Sun, Y., Wang, L., Guo, M., & Han, K. (2021) · BioMed Research International · doi:10.1155/2021/5542224 · Cited by 12 · see citing works

Toxicogenomic analysis of physiologically important metals: An integrated in silico approach

Balasubramanian, S., Duraikannan, V., & Perumal, E. (2023) · Food and Chemical Toxicology · doi:10.1016/j.fct.2023.113895 · Cited by 11 · see citing works

Deep reinforcement learning with emergent communication for coalitional negotiation games

Chen, S., Yang, Y., & Su, R. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022212 · Cited by 11 · see citing works

Single-cell transcriptome analysis reveals immune microenvironment changes and insights into the transition from DCIS to IDC with associated prognostic genes

Sun, Y., Pan, Z., Wang, Z., Wang, H., Wei, L., Cui, F., Zou, Q., & Zhang, Z. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05706-6 · Cited by 10 · see citing works

4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism

Zeng, R., Cheng, S., & Liao, M. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.664669 · Cited by 10 · see citing works

Structure Mapping Generative Adversarial Network for Multi-View Information Mapping Pattern Mining

Bi, X.-A., Huang, Y., Yang, Z., Chen, K., Xing, Z., Xu, L., Li, X., Liu, Z., & Liu, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3330795 · Cited by 10 · see citing works

White Blood Cell Image Segmentation Based on Color Component Combination and Contour Fitting

Wang, C., Zhang, H., Li, Z., Zhou, X., Cheng, Y. J., & Chen, R. (2019) · Current Bioinformatics · doi:10.2174/1574893614666191017102310 · Cited by 9 · see citing works

Identifying G-Protein Coupled Receptors Using Mixed-Feature Extraction Methods and Machine Learning Methods

Ao, C., Gao, L., & Yu, L. (2020) · IEEE Access · doi:10.1109/access.2020.2983105 · Cited by 9 · see citing works

ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

Feng, C., Wei, H., Yang, D., Feng, B., Ma, Z., Han, S., Zou, Q., & Shi, H. W. (2021) · PROTEOMICS · doi:10.1002/pmic.202100017 · Cited by 9 · see citing works

6mAPred-MSFF: A Deep Learning Model for Predicting DNA N6-Methyladenine Sites across Species Based on a Multi-Scale Feature Fusion Mechanism

Zeng, R., & Liao, M. (2021) · Applied Sciences · doi:10.3390/app11167731 · Cited by 9 · see citing works

ATGPred-FL: sequence-based prediction of autophagy proteins with feature representation learning

Jiao, S., Chen, Z., Zhang, L., Zhou, X., & Shi, L. (2022) · Amino Acids · doi:10.1007/s00726-022-03145-5 · Cited by 9 · see citing works

Computational Approaches for Drug-Induced Liver Injury (DILI) Prediction: State of the Art and Challenges

Béquignon, O. J. M., Pawar, G. V., van de Water, B., Cronin, M., & van Westen, G. J. P. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11535-1 · Cited by 9 · see citing works

Bioluminescent Proteins Prediction with Voting Strategy

Zhao, S., Ju, Y., Ye, X., Zhang, J., & Han, S. (2020) · Current Bioinformatics · doi:10.2174/1574893615999200601122328 · Cited by 8 · see citing works

Toxicity prediction and classification of Gunqile-7 with small sample based on transfer learning method

Zhao, H., Qiu, S., Bai, M., Wang, L., & Wang, Z. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108348 · Cited by 8 · see citing works

CWLy-RF: A novel approach for identifying cell wall lyases based on random forest classifier

Jiao, S., Xu, L., & Ju, Y. (2021) · Genomics · doi:10.1016/j.ygeno.2021.06.038 · Cited by 8 · see citing works

Better Performance with Transformer: CPPFormer in the Precise Prediction of Cell-penetrating Peptides

Xue, Y., Ye, X., Wei, L., Zhang, X., Sakurai, T., & Wei, L. (2021) · Current Medicinal Chemistry · doi:10.2174/0929867328666210920103140 · Cited by 8 · see citing works

MFA-DTI: Drug-target interaction prediction based on multi-feature fusion adopted framework

Chen, S., Li, M., & Semenov, I. (2024) · Methods · doi:10.1016/j.ymeth.2024.02.008 · Cited by 7 · see citing works

Prediction of Thermophilic Proteins Using Voting Algorithm

Li, J., Zhu, P., & Zou, Q. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-17938-0_18 · Cited by 7 · see citing works

Machine Learning Methods in Prediction of Protein Palmitoylation Sites: A Brief Review

Li, Y., Pu, F., Wang, J., Zhou, Z., Zhang, C., He, F., Ma, Z., & Zhang, J. (2020) · Current Pharmaceutical Design · doi:10.2174/1381612826666201112142826 · Cited by 7 · see citing works

Accurate Prediction and Key Feature Recognition of Immunoglobulin

Gong, Y., Liao, B., Peng, D., & Zou, Q. (2021) · Applied Sciences · doi:10.3390/app11156894 · Cited by 7 · see citing works

HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug–disease association prediction

Shang, Y., Wang, Z., Chen, Y., Yang, X., Ren, Z., Zeng, X., & Xu, L. (2025) · BMC Biology · doi:10.1186/s12915-025-02206-x · Cited by 6 · see citing works

A machine learning method for predicting disease-associated microRNA connections using network internal topology data

Jindal, L., Sharma, A., Prasad, K. D. V., Irshad, A., Rivera, R., & Karimovna, A. D. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100215 · Cited by 6 · see citing works

Multidimensional scaling method for prediction of lysine glycation sites

Li, T., Yin, Q., Song, R., Gao, M., & Chen, Y. (2019) · Computing · doi:10.1007/s00607-019-00710-x · Cited by 6 · see citing works

Predicting drug-induced hepatotoxicity based on biological feature maps and diverse classification strategies

Su, R., Wu, H., Liu, X., & Wei, L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz165 · Cited by 26 · see citing works

Use of toxicogenomics to understand mechanisms of drug-induced hepatotoxicity during drug discovery and development

Blomme, E. A. G., Yang, Y., & Waring, J. F. (2008) · Toxicology Letters · doi:10.1016/j.toxlet.2008.09.017 · Cited by 127 · see citing works

The effect of four non-genotoxic carcinogens and four non-carcinogens on NRK-52E cells using a transcriptomics approach

Bloch, K. M., Evans, A. R., van Delft, J. H. M., & Lock, E. A. (2011) · Toxicology · doi:10.1016/j.tox.2011.09.019 · Cited by 2 · see citing works

TCN-RDP: Predicting Drug-Induced Liver Injury from Time-Series Toxicogenomic Data

Zhao, Z., Li, B., Zuo, H., Chen, R., Deng, R., Geng, J., Sun, C., Wei, L., Lang, M., & Su, R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01678 · Cited by 1 · see citing works

In vitro transcriptomic prediction of hepatotoxicity for early drug discovery

Cheng, F., Theodorescu, D., Schulman, I. G., & Lee, J. K. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2011.08.009 · Cited by 36 · see citing works