Metabolomics spectral formatting, alignment and conversion tools (MSFACTs). - Anthony L. Duran; Jian Yang; Liangjiang Wang; Lloyd W. Sumner (Bioinform., 2003)

BioStar models of clinical and genomic data for biomedical data warehouse design. - Liangjiang Wang; Aidong Zhang; Murali Ramanathan (Int. J. Bioinform. Res. Appl., 2005)

Prediction of RNA-Binding Residues in Protein Sequences Using Support Vector Machines. - Liangjiang Wang; Susan J. Brown (EMBC, 2006)

Prediction of Dna-binding Residues from Sequence Features. - Liangjiang Wang; Susan J. Brown (J. Bioinform. Comput. Biol., 2006)

BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences. - Liangjiang Wang; Susan J. Brown (Nucleic Acids Res., 2006)

A Machine Learning Approach for Prediction of Lipid-Interacting Residues in Amino Acid Sequences. - Stephanie Jimenez Irausquin; Liangjiang Wang (BIBE, 2007)

A Semi-Supervised Learning Approach for Prediction of DNA-Binding Residues in Protein Sequences. - Liangjiang Wang (BIOCOMP, 2007)

BeetleBase: the model organism database for Tribolium castaneum. - Liangjiang Wang; Suzhi Wang; Yonghua Li; Martin S. R. Paradesi; Susan J. Brown (Nucleic Acids Res., 2007)

BindN+ for Improved Prediction of DNA or RNA Binding Residues in Protein Sequences Using Evolutionary Information. - Liangjiang Wang (BIOCOMP, 2008)

Random Forests for Prediction of DNA-Binding Residues in Protein Sequences Using Evolutionary Information. - Liangjiang Wang (FGCN (3: BSBT), 2008)

Prediction of lipid-interacting amino acid residues from sequence features. - Liangjiang Wang; Stephanie Jimenez Irausquin; Jack Y. Yang (Int. J. Comput. Biol. Drug Des., 2008)

Biological Features for Sequence-Based Prediction of Protein Stability Changes upon Amino Acid Substitutions. - Shaolei Teng; Anand K. Srivastava; Liangjiang Wang (IJCBS, 2009)

New Descriptors of Evolutionary Information for Accurate Prediction of DNA and RNA-Binding Residues in Protein Sequences. - Liangjiang Wang; Caiyan Huang (IJCBS, 2009)

Random forest-based prediction of protein sumoylation sites from sequence features. - Shaolei Teng; Hong Luo; Liangjiang Wang (BCB, 2010)

Computational Identification of Human Tissue-Specific Genes Using Public Microarray Expression Data. - Liangjiang Wang (BIOCOMP, 2010)

BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features. - Liangjiang Wang; Caiyan Huang; Mary Qu Yang; Jack Y. Yang (BMC Syst. Biol., 2010)

Structural assessment of the effects of Amino Acid Substitutions on protein stability and protein-protein interaction. - Shaolei Teng; Anand K. Srivastava; Charles E. Schwartz; Emil Alexov; Liangjiang Wang (Int. J. Comput. Biol. Drug Des., 2010)

Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms. - Jack Y. Yang; A. Keith Dunker; Jun S. Liu; Xiang Qin; Hamid R. Arabnia; William Yang; Andrzej Niemierko; Zhongxue Chen; Zuojie Luo; Liangjiang Wang; Yunlong Liu; Dong Xu; et al. (BMC Bioinform., 2014)

Identification of genes and pathways involved in kidney renal clear cell carcinoma. - William Yang; Kenji Yoshigoe; Xiang Qin; Jun S. Liu; Jack Y. Yang; Andrzej Niemierko; Youping Deng; Yunlong Liu; A. Keith Dunker; Zhongxue Chen; Liangjiang Wang; Dong Xu; et al. (BMC Bioinform., 2014)

Support vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates. - Steven B. Cogill; Liangjiang Wang (Bioinform., 2016)

Prediction of back-splicing sites reveals sequence compositional features of human circular RNAs. - Jun Wang; Liangjiang Wang (ICCABS, 2017)

A Genetic Algorithm for Finding Discriminative Functional Motifs in Long Non-coding RNAs. - Brian L. Gudenas; Liangjiang Wang (ISBRA, 2017)

A Highly Sensitive Intensity-Modulated Optical Fiber Magnetic Field Sensor Based on the Magnetic Fluid and Multimode Interference. - Yi Huang; Tingyun Wang; Chuanlu Deng; Xiaobei Zhang; Fufei Pang; Xuekun Bai; Weilong Dong; Liangjiang Wang; Zhenyi Chen (J. Sensors, 2017)

Co-expression of long non-coding RNAs and autism risk genes in the developing human brain. - Steven B. Cogill; Anand K. Srivastava; Mary Qu Yang; Liangjiang Wang (BMC Syst. Biol., 2018)

Deep Learning of CTCF-Mediated Chromatin Loops in 3D Genome Organization. - Shuzhen Kuang; Liangjiang Wang (ICCABS, 2019)

Deep learning of the back-splicing code for circular RNA formation. - Jun Wang; Liangjiang Wang (Bioinform., 2019)

Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features. - Jun Wang; Liangjiang Wang (BMC Bioinform., 2020)

Expression-based prediction of human essential genes and candidate lncRNAs in cancer cells. - Shuzhen Kuang; Yanzhang Wei; Liangjiang Wang (Bioinform., 2021)

Deep Learning of Sequence Patterns for CCCTC-Binding Factor-Mediated Chromatin Loop Formation. - Shuzhen Kuang; Liangjiang Wang (J. Comput. Biol., 2021)

Deep sequence representation learning for predicting human proteins with liquid-liquid phase separation propensity and synaptic functions. - Anqi Wei; Liangjiang Wang (BCB, 2022)