Integrating multi-omics data through deep learning for accurate cancer prognosis prediction H Chai, X Zhou, Z Zhang, J Rao, H Zhao, Y Yang Computers in biology and medicine 134, 104481, 2021 | 84 | 2021 |
Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization Y Liang, H Chai, XY Liu, ZB Xu, H Zhang, KS Leung BMC medical genomics 9, 1-11, 2016 | 49 | 2016 |
Deep learning-based ovarian cancer subtypes identification using multi-omics data LY Guo, AH Wu, Y Wang, L Zhang, H Chai, XF Liang BioData Mining 13, 1-12, 2020 | 43 | 2020 |
Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning–based neural network X Zhou, H Chai, H Zhao, CH Luo, Y Yang GigaScience 9 (7), giaa076, 2020 | 38 | 2020 |
Robust Sparse Logistic Regression With the ( ) Regularization for Feature Selection Using Gene Expression Data ZY Yang, Y Liang, H Zhang, H Chai, B Zhang, C Peng IEEE Access 6, 68586-68595, 2018 | 30 | 2018 |
A novel logistic regression model combining semi-supervised learning and active learning for disease classification H Chai, Y Liang, S Wang, H Shen Scientific reports 8 (1), 13009, 2018 | 25 | 2018 |
A new semi-supervised learning model combined with Cox and SP-AFT models in cancer survival analysis H Chai, Z Li, D Meng, L Xia, Y Liang Scientific reports 7 (1), 13053, 2017 | 25 | 2017 |
Identification of 13 blood-based gene expression signatures to accurately distinguish tuberculosis from other pulmonary diseases and healthy controls HH Huang, XY Liu, Y Liang, H Chai, LY Xia Bio-Medical Materials and Engineering 26 (s1), S1837-S1843, 2015 | 20 | 2015 |
Semi-supervised learning with ensemble self-training for cancer classification Q Wang, LY Xia, H Chai, Y Zhou 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced …, 2018 | 13 | 2018 |
Descriptor selection via log-sum regularization for the biological activities of chemical structure LY Xia, YW Wang, DY Meng, XJ Yao, H Chai, Y Liang International journal of molecular sciences 19 (1), 30, 2017 | 13 | 2017 |
The L1/2 regularization approach for survival analysis in the accelerated failure time model H Chai, Y Liang, XY Liu Computers in biology and medicine 64, 283-290, 2015 | 13 | 2015 |
Application of L 1/2 regularization logistic method in heart disease diagnosis B Zhang, H Chai, Z Yang, Y Liang, G Chu, X Liu Bio-Medical Materials and Engineering 24 (6), 3447-3454, 2014 | 10 | 2014 |
Complex harmonic regularization with differential evolution in a memetic framework for biomarker selection S Wang, HW Shen, H Chai, Y Liang PloS one 14 (2), e0210786, 2019 | 8 | 2019 |
Detecting lncRNA–Cancer Associations by Combining miRNAs, Genes, and Prognosis with Matrix Factorization H Yan, H Chai, H Zhao Frontiers in Genetics 12, 639872, 2021 | 7 | 2021 |
Robust sparse accelerated failure time model for survival analysis H Shen, H Chai, M Li, Z Zhou, Y Liang, Z Yang, H Huang, X Liu, B Zhang Technology and Health Care 26 (S1), 55-63, 2018 | 4 | 2018 |
Protein-protein interaction network construction for cancer using a new L1/2-penalized Net-SVM model H Chai, HH Huang, HK Jiang, Y Liang, LY Xia | 4 | 2016 |
A novel Log penalty in a path seeking scheme for biomarker selection S Wang, H Zhang, H Chai, Y Liang Technology and Health Care 27 (S1), 85-93, 2019 | 2 | 2019 |
Semi-Supervised Learning Framework based on Cox and AFT Models with L1/2 Regularization for Patient's Survival Prediction Y Liang, H Chai, XY Liu US Patent App. 15/219,484, 2017 | 2 | 2017 |
System and method for determining an association of at least one biological feature with a medical condition Y Liang, H Chai, XY Liu US Patent 10,192,642, 2019 | 1 | 2019 |
Method and system for determining an estimated survival time of a subject with a medical condition Y Liang, H Chai, Z Yang, XY Liu US Patent App. 15/150,580, 2017 | 1 | 2017 |