Tools

• iPhDNet (integrated Phosphoprotein Histone Drug Network)
  S. A. Mollah and S. Subramaniam, “Histone Signatures Predict Therapeutic Efficacy in Breast Cancer”. 2020, IEEE Open Journal of Engineering in Medicine and Biology, vol. 1, pp. 74-82. (doi.org/10.1109/OJEMB.2020.2967105)
  
  
• Gen3DNet ( General 3D Network)
  Paul Morrison, Tina Tang, Charles Lu, Shamim Mollah. Gen3DNet: An R package for generating 3D network models. 2024. (doi.org/10.1101/2024.11.11.623060) . https:// github.com/smollahlab/gen3DNet
  
  
• HOCMO (Higher-Order Correlation Model)
  Charles Lu, Rintsen Sherpa, Liubou Klindziuk, Stefanie Kriel, Shamim Mollah. “HOCMO: A Tensor-based Higher-Order Correlation Model to Deconvolute Epigenetic Microenvironment in Breast Cancer”. 2020. (doi.org/10.1101/2020.12.01.406249). https://github.com/smollahlab/HOCMO
  
  
• NeTOIF (Network-based Approach for Time-Series Omics Data Imputation and Forecasting)
  Min Shi and Shamim Mollah. “NeTOIF: A Network-based Approach for Time-Series Omics Data Imputation and Forecasting.”. 2021. (doi.org/10.1101/2021.06.05.447209). https://github.com/smollahlab/NeTOIF
  
  
• PBHSCM (Peripheral Blood Hematopoietic Stem Cell Mobilization score)
  Xiang J, Shi M, Fiala MA, Gao F, Rettig MP, Uy GL, Schroeder MA, Weilbaecher KN, Stockerl-Goldstein K, Mollah S, DiPersio JF. Machine learning-based scoring models to predict hematopoietic stem cell mobilization in allogeneic donors. Blood Adv. 2021 Sep 23:bloodadvances.2021005149. (doi:10.1182/bloodadvances.2021005149) PMID: 34555850.
  
  
• CREWkb (Chromatin Reader Eraser Writer knowledge base)
  Maya Natesan, Reetika Ghag, Mitchelle Kong, Minyoung Ahn, Tina Tang, Shamim Mollah. “CREWkb 1.0: Optimizing Chromatin Readers, Erasers, and Writers Knowledgebase using Machine Learning-Based Approach”. 2022. (doi.org/10.1101/2022.06.02.494594)