Predictive Model for Daily Risk Alerts in Sepsis Patients in the ICU: Visualization and Clinical Analysis of Risk Indicators

Sepsis is a systemic inflammatory response syndrome triggered by infection, often leading to multiple organ failure and high mortality rates. Although modern medical technology has made significant progress in the treatment of sepsis, some patients still die due to the rapid deterioration of their condition. Therefore, accurately predicting the mor...

Deep Learning-Based Multi-Modal Data Integration Enhancing Breast Cancer Disease-Free Survival Prediction

Breast cancer is one of the most common malignancies among women worldwide. Although early intervention and appropriate treatment have significantly improved patient survival rates, approximately 30% of cases still experience recurrence and distant metastasis, resulting in a 5-year survival rate of less than 23%. Traditional clinical prediction met...

GutBugDB: A Web Resource to Predict the Human Gut Microbiome-Mediated Biotransformation of Biotic and Xenobiotic Molecules

In recent years, the significant role of the human gut microbiota (HGM) in the metabolism of drugs and nutrients has gradually been recognized. The gut microbiota not only affects the bioavailability of orally administered drugs but also participates in the biotransformation of drugs and bioactive molecules through its metabolic enzymes, thereby in...

EPICPred: Predicting Phenotypes Driven by Epitope-Binding TCRs Using Attention-Based Multiple Instance Learning

T-cell receptors (TCRs) play a crucial role in the adaptive immune system by recognizing pathogens through binding to specific antigen epitopes. Understanding the interactions between TCRs and epitopes is essential for uncovering the biological mechanisms of immune responses and developing T cell-mediated immunotherapies. However, although the impo...

DeepES: Deep Learning-Based Enzyme Screening for Identifying Orphan Enzyme Genes

Academic Background With the rapid advancement of sequencing technology, scientists have been able to obtain a vast amount of protein sequence data, including many enzyme sequences. However, despite the establishment of large enzyme databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BRENDA, sequence information for many enzyme...

MostPlas: A Self-Correction Multi-Label Learning Model for Plasmid Host Range Prediction

Plasmids are small, circular, double-stranded DNA molecules that exist independently of chromosomal DNA in bacteria. They facilitate horizontal gene transfer, enabling host bacteria to acquire beneficial traits such as antibiotic resistance and metal resistance. Some plasmids can transfer, replicate, or persist in multiple microorganisms, and these...

Sequence Analysis: DNA Sequence Alignment Using Transformer Models

Academic Background DNA sequence alignment is a core task in genomics, aiming to map short DNA fragments (reads) to the most probable locations on a reference genome. Traditional methods typically involve two steps: first, indexing the genome, followed by efficient searching to locate potential positions for the reads. However, with the exponential...

FlowPacker: Protein Side-Chain Packing with Torsional Flow Matching

The three-dimensional structure of a protein is determined by its amino acid sequence, and the function of the protein is highly dependent on its three-dimensional structure. The side-chain conformations of proteins play a crucial role in protein folding, protein-protein interactions, and de novo protein design. Accurate prediction of protein side-...

CryoTEN: Efficiently Enhancing Cryo-EM Density Maps Using Transformers

Academic Background Cryogenic Electron Microscopy (Cryo-EM) is a crucial experimental technique for determining the structures of macromolecules such as proteins. However, the effectiveness of Cryo-EM is often hindered by noise and missing density values caused by experimental conditions such as low contrast and conformational heterogeneity. Althou...

GCLink: A Graph Contrastive Link Prediction Framework for Gene Regulatory Network Inference

Research Background Gene Regulatory Networks (GRNs) are crucial tools for understanding the complex biological processes within cells. They reveal the interactions between Transcription Factors (TFs) and target genes, thereby controlling gene transcription and regulating cellular behavior. With the advancement of single-cell RNA sequencing (scRNA-s...