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Software for Promoting Open Health Data Science

Software Developed in The Hu Lab

Software Developed in The Hu Lab

Software Developed in The Hu Lab

Software Web:  ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction







Software Web   Deep learning-based MRI-genomic mapping







Software Web:  Molecular Property Prediction based on Bimodal Supervised Contrast Learning





Software Web:  Machine Learning Model trained on a High-Throughput Antibacterial Screen





Software Web: Multiomics-based deep tensor survival model for time-to-event prediction





Software Web: Self-supervised deep learning models for CT image segmentation 






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Software Web: Semi-Supervised Deep Generative Models for Image Segmentation





Software Web: A re-trainable deep learning tool for single cell RNA-sequencing based cell type labeling 




​Software Web: Deep learning-driven algorithm for clustering small molecules





Software Web: Sparse Matrix Profile DenseNet for COVID-19 Diagnosis 





​Software Web: Deep learning-driven algorithm for predicting drug mechanism of action






Software Web: Computational prediction of the pathogenic status of cancer-specific somatic variants 





Software Web: Bayesian tensor factorization-drive breast cancer subtyping by integrating multi-omics data 





Software Web: DTF: Deep tensor factorization for predicting anticancer drug synergy 





Software Web: Matrix profile-guided attention LSTM model for forecasting COVID-19 cases

 




Software Web: An OpenMP based tool for finding LCS of DNA sequence data 




Software Developed in The Hu Lab

Software Developed in The Hu Lab

Description:  An atom-bond transformer-based message-passing neural network (ABT-MPNN), to improve the molecular representation embedding process for molecular property predictions.

Citation:  Liu et al. 2022, Journal of Cheminformatics



Description   Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer.

Citation:  Liu et al. 2023 Biomarker Research




Description:   A bimodal supervised contrastive learning (BSCL) framework to integrate the SMILES string and the molecular graph in a unified network.to predict molecular property.   
Citation:  Sun et al. 2022, IEEE BIBM 


Description:  A Machine Learning Model trained on a High-Throughput Antibacterial Screen Increases the Hit Rate of Drug Discovery
Citation:  Rahman, liu et al. 2022, Plos Computational Biology. 



Description:  Tightly integrated multiomics-based deep tensor survival model for time-to-event prediction
Citation:  Zhang et al. 2022, Bioinformatics 




Description: Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease, and COVID-19​
Citation: Fung et al. 2021, Journal of Translational Medicine



Description: Semi-Supervised COVID-19 CT Image Segmentation Using Deep Generative Models 

Citation: Zammit et al. 2022, BMC Bioinformatics 



Description: ChrNet: A re-trainable chromosome-based 1D convolutional neural network for predicting immune cell types
Citation: Jia et al. 2021, Genomics



Description: Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means Citation: Hadipour et al. 2022, BMC Bioinformatics 



Description: A Two-dimensional Sparse Matrix Profile DenseNet for COVID-19 Diagnosis Using Chest CT Images
Citation: Liu et al. 2020, IEEE Access



Description: Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles 

Citation: Liu et al. 2022, Journal of Cheminformatics
 


Description: Computational prediction of the pathogenic status of cancer-specific somatic variants 

Citation: Feizi et al. 2022, Frontiers in Genetics  



Description: Bayesian tensor factorization-drive breast cancer subtyping by integrating multi-omics data 

Citation: Liu et al. 2022, Journal of Biomedical Informatics  



Description: A new algorithm integrating tensor factorization and deep learning to predict anticancer drug combinations
Citation: Sun et al. 2020, Bioinformatics


Description: A novel matrix profile-guided attention LSTM model for forecasting COVID-19 cases in USA 

Citation: Liu et al. 2021, Frontiers in Public Health  



Description: This repository contains three parallel implementation of the LCS algorithm in MPI, OpenMP, and hybrid MPI-OpenMP platforms. 

Citation: Shikder et al. 2019, BMC Research Notes.

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