Tools

This page contains the tools and software packages we have developed here at WangLab or previously at other institutions.

Please visit our GitHub page for our latest releases.

M3SDA: Moment Matching for Multi-Source Domain Adaptation

Transfer knowledge learned from multiple labeled source domains to an unlabeled target domain by dynamically aligning moments of their feature distributions.

Related publications:

Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, Bo Wang. ICCV, 2019. SNF: Computational method for data integration

A new computational method for data integration.

Related publications:

Bo Wang, Aziz M Mezlini, Feyyaz Demir, Marc Fiume, Zhuowen Tu, Michael Brudno, Benjamin Haibe-Kains, Anna Goldenberg. Similarity network fusion for aggregating data types on a genomic scale. Nature Methods, 2014. SIMLR: Single-cell interpretation via multi-kernel learning

A novel similarity-learning framework for dimension reduction, clustering and visualization.

Related publications:

Bo Wang, Junjie Zhu, Emma Pierson, Serafim Batzoglou. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nature Methods, 2017.
Bo Wang, Daniele Ramazzotti, Luca De Sano, Junjie Zhu, Emma Pierson, Serafim Batzoglou. SIMLR: A Tool for Large‐Scale Genomic Analyses by Multi‐Kernel Learning. Proteomics, 2018. Vicus: Capturing neighborhood structures of the network

Exploiting local structures to improve network-based analysis of biological data.

Related publications:

Bo Wang, Lin Huang, Yuke Zhu, Anshul Kundaje, Serafim Batzoglou, Anna Goldenberg. Vicus: Exploiting local structures to improve network-based analysis of biological data. PLoS Computational Biology, 2017. Network Enhancement: Denoising weighted biological networks

A general method to denoise weighted biological networks.

Related publications:

Bo Wang, Armin Pourshafeie, Marinka Zitnik, Junjie Zhu, Carlos D Bustamante, Serafim Batzoglou, Jure Leskovec. Network Enhancement: a general method to denoise weighted biological networks. Nature Communications, 2018.

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