From e59f6f594dc0535a1efbdf7aee87622a277a9e0b Mon Sep 17 00:00:00 2001
From: xuewei cao <36172337+xueweic@users.noreply.github.com>
Date: Thu, 21 Sep 2023 01:45:26 -0400
Subject: [PATCH] Update software.md

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 _pages/software.md | 12 +++++++++++-
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 title: "Software"
 permalink: /software/
 author_profile: true
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+---
+
+
+## [R/GPN](https://github.com/xueweic/GPN)
+A novel method for multiple phenotype association studies by constructing a bipartite signed network, linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN), which is a new insight to investigate correlation among phenotypes. The GPN can be constructed by both quantitative and qualitative traits, especially phenotypes have extremely unbalanced case-control ratios.
+
+## [R/TGPred](https://github.com/xueweic/TGPred) and [Python/TGPred](https://github.com/tobefuture/TGPred)
+R and Python software including six efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning, and optimization.
+
+## [Python/CLCLSA](https://github.com/xueweic/CLCLSA)
+A deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model uses cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-view contrastive learning, which is used to maximize the mutual information between different types of omics, is employed before latent feature concatenation. Also, the feature-level self-attention and omics-level self-attention are employed to dynamically select the most informative features for multi-omics data integration.