Wei Wang

The paper describes the Common Fund Data Ecosystem (CFDE), which integrates NIH Common Fund data resources to enable discovery, analysis, and collaboration across heterogeneous datasets.

The paper develops a data science pipeline to analyze interactions between Ca2+-regulating proteins and cardiovascular diseases, identifying associations with oxidative stress and cardiac arrhythmias.

This study benchmarks large language models in bioinformatics tasks, finding GPT-4 leads in most areas while Gemini excels in math and LLaMA struggles with code generation.

JEDI predicts circular RNA from nucleotide sequences using attention mechanisms and cross-attention layers, outperforming existing methods and enabling backsplicing hotspot discovery.

Bio-JOIE learns joint representations of biological knowledge to predict protein interactions and cluster enzymes, outperforming existing methods in PPI type prediction.

This paper introduces MuPIPR, a deep learning framework that estimates mutation effects on protein-protein interactions by using contextualized amino acid representations and a Siamese residual recurrent convolutional encoder.

Lasagna is an end-to-end framework for protein-protein interaction prediction using primary sequences, outperforming existing methods in binary prediction and showing promise in interaction type and binding affinity tasks.

MiniScrub improves Nanopore read quality through de novo error correction, leading to better genome assembly with fewer misassemblies and indel errors.

TahcoRoll improves k-mer profiling by efficiently handling variable-length k-mers, outperforming existing tools in time and memory usage without disk space.

The paper creates a resource platform for clinical case reports, including a dataset, metadata template, and tools to analyze trends and extract medical insights from PubMed case reports.