I am a Computational Biologist and an AI/ML technology enthusiast.
Let's connect and explore new possibilities in AI-driven bioinformatics!
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I'm Ruifeng Hu, Ph.D., a computational biologist specializing in bioinformatics, multi-omics data analysis and machine learning. Currently, I work as a Research Scientist at Yale School of Medicine, applying advanced statistical and AI-driven approaches to decipher complex biological data for target discovery, precision medicine, and disease modeling.
I actively contribute to open-source projects and have developed various bioinformatics tools and databases. My research focuses on leveraging AI and data-driven methods to advance biomedical discoveries.
I thrive in collaborative environments, working closely with wet lab scientists and other researchers to bridge the gap between computational and experimental biology. Always exploring the cutting-edge AI applications in bioinformatics!
Multi-omics Data Analysis: NGS, Single-cell & bulk RNA-seq, spatial transcriptomics, CRISPR screens, WGS, Genomics/Genetics.
Machine Learning & Deep Learning: Regression, clustering, generative models (VAE, LSTM, Transformer).
Pipeline, Cloud & HPC Computing: Nextflow, Airflow, Git, Docker, AWS, GCP, high-performance computing.
Bioinformatics Pipelines & Web Development: Automated workflows and interactive genomic data platforms.
an AI-driven tool for HLA-I epitope prediction and interpretable clinical immunopeptidome analysis.
VisiumST Dot Frame Detection and Cropping
The brain omics data analysis and visualization platform:scRNAseq, scATACseq, ChIPseq, Spatial Transcriptomics, WGS and other omics data.
Drug response prediction using Variational Autoencoder based Elastic Net models.
exploring mitochondrial heteroplasmy and gene expression from single-cell sequencing assays.
Nextflow pipeline for single-cell RNAseq data analysis
Predict future UPDRS III trends in Parkinson's Disease using transformer.