Prediction Is a Balancing Act: Importance of Sampling Methods to Balance Sensitivity and Specificity of Predictive Models Based on Imbalanced Chemical Data Sets

ProTox-II: a webserver for the prediction of toxicity of chemicals

(2018) Priyanka Banerjee et al.   NUCLEIC ACIDS RESEARCH

BitterSweetForest: A Random Forest Based Binary Classifier to Predict Bitterness and Sweetness of Chemical Compounds

(2018) Priyanka Banerjee et al.   Frontiers in Chemistry

The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury

(2017) Shraddha Thakkar et al.   Expert Review of Gastroenterology & Hepatology

Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs

(2017) Huixiao Hong et al.   Scientific Reports

DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans

(2016) Minjun Chen et al.   DRUG DISCOVERY TODAY

In silicoPrediction of Drug Induced Liver Toxicity Using Substructure Pattern Recognition Method

(2016) Chen Zhang et al.   Molecular Informatics

Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization

(2016) Ruili Huang et al.   Nature Communications

Computational methods for prediction of in vitro effects of new chemical structures

(2016) Priyanka Banerjee et al.   Journal of Cheminformatics

Deep Learning for Drug-Induced Liver Injury

(2015) Youjun Xu et al.   Journal of Chemical Information and Modeling

Random Balance: Ensembles of variable priors classifiers for imbalanced data

(2015) José F. Díez-Pastor et al.   KNOWLEDGE-BASED SYSTEMS

Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands

(2015) Germán M. Pérez et al.   MOLECULAR DIVERSITY

Coupling different methods for overcoming the class imbalance problem

(2015) Loris Nanni et al.   NEUROCOMPUTING

Classifying imbalanced data sets using similarity based hierarchical decomposition

(2015) Cigdem Beyan et al.   PATTERN RECOGNITION

Applying machine learning techniques for ADME-Tox prediction: a review

(2014) Vinícius Gonçalves Maltarollo et al.   Expert Opinion on Drug Metabolism & Toxicology

Chemical space networks: a powerful new paradigm for the description of chemical space

(2014) Gerald M. Maggiora et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Machine learning methods in chemoinformatics

(2014) John B. O. Mitchell   Wiley Interdisciplinary Reviews-Computational Molecular Science

Mechanisms of Drug-induced Liver Injury

(2013) Liyun Yuan et al.   Clinics in Liver Disease

An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics

(2013) Victoria López et al.   INFORMATION SCIENCES

Analysis of sampling techniques for imbalanced data: An n=648 ADNI study

(2013) Rashmi Dubey et al.   NEUROIMAGE

Mixed learning algorithms and features ensemble in hepatotoxicity prediction

(2011) Chin Yee Liew et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Developing Structure−Activity Relationships for the Prediction of Hepatotoxicity

(2010) Nigel Greene et al.   CHEMICAL RESEARCH IN TOXICOLOGY

A learning method for the class imbalance problem with medical data sets

(2010) Der-Chiang Li et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Extended-Connectivity Fingerprints

(2010) David Rogers et al.   Journal of Chemical Information and Modeling

Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

(2009) Katja Hansen et al.   Journal of Chemical Information and Modeling

Chronic suprapubic catheterization in the management of patients with spinal cord injuries: analysis of upper and lower urinary tract complications

(2008) Takayuki Sugimura et al.   BJU INTERNATIONAL