Software
- MVMF for Integrative Data Analysis: A Python toolkit for multi-view NMF models (MC-NMF, SS-MC-NMF, ARD-NMF, variational NMF) for biological data integration and feature discovery.
- DECRES: A deep learning toolkit for identifying cis-regulatory elements (CREs), featuring MLPs, autoencoders, CNNs, and deep feature selection, with datasets from 8 human cell lines.
- Non-Negative Matrix Factorization toolbox: Implements standard and advanced NMF methods (Sparse, Semi-, Convex, Kernel, Orthogonal), supporting clustering, feature extraction, classification, and imputation.
- Sparse Representation toolbox: MATLAB toolbox for sparse coding, dictionary learning, and linear regression classification (LRC), with kernel and non-negative constraints.
- RLMK: Regularized Linear Models and Kernels toolbox for linear models with various loss functions and regularizations, including major SVM variants.
- Probabilistic Graphical Models toolbox: MATLAB toolbox for analyzing graphical models, including BDeu/BIC scores, HO-DBNs, and qualitative influence modeling for GRN reconstruction.
- Spectral Clustering toolbox: MATLAB implementation of spectral clustering with a graph-based cluster validity index.
- LibMTL : A Python Library for Deep Multi-Task and multi-objective Learning. For more details, see the JMLR.