Bioinformatics Software (Recently) Developed by Wan Lab (2022 - present)

Contrastive Learning

1. Enhanced Gaussian noise augmentation-based protein-protein interaction network embedding contrastive learning (EGsCL)

   
   
   
   Source code: https://github.com/ibrahimsaggaf/EGsCL
   
   Pre-trained models: https://doi.org/10.5281/zenodo.12143797
   
   Citation: Alsaggaf, I., Freitas, A.A. and Wan, C. (2024) Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein–protein interaction networks, NAR Genomics and Bioinformatics.
   




2. Gaussian noise augmentation-based single-cell RNA-Seq contrastive learning (GsRCL)

   
   
   
   Source code: https://github.com/ibrahimsaggaf/GsRCL
   
   Pre-trained models: https://doi.org/10.5281/zenodo.10050548.
   
   Citation: Alsaggaf, I., Buchan, D. and Wan, C. (2024) Improving cell-type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning, Briefings in Functional Genomics, elad059.
   




---

Biological Langauge Models

3. Temporal Convolutional Networks-based DNA sequence language model for predicting yeast gene expression profiles

   
   Source code: https://github.com/de-Boer-Lab/random-promoter-dream-challenge-2022/tree/main/dream_submissions/Wan%26Barton_BBK/code
   
   Citation: Rafi, A.M., et al. (2024) A community effort to optimize sequence-based deep learning models of gene regulation, Nature Biotechnology.
                   Alsaggaf, I., et al. (2022) Team WanBarton_BBK Submission. Available at: https://github.com/de-Boer-Lab/random-promoter-dream-challenge-2022/blob/main/dream_submissions/Wan%26Barton_BBK/report.pdf
   




---