Gene Selection for Single Cell RNA-seq Data via Fuzzy Rough Iterative Computation Model

Background Introduction Single-cell RNA sequencing (scRNA-seq) technology has been widely applied in biomedical research in recent years, as it can reveal the heterogeneity of gene expression at the single-cell level, providing an important tool for understanding cell types, cell states, and disease mechanisms. However, scRNA-seq data is characteri...

Catch Bonds Nonlinearly Control CD8 Cooperation to Shape T Cell Specificity

T cell receptors (TCRs) play a crucial role in the immune system by recognizing antigen peptides presented by major histocompatibility complexes (MHCs), thereby initiating immune responses against pathogens and tumor cells. However, the specificity of TCRs—their ability to distinguish self-antigens from non-self antigens—is central to the effective...

Significance in Scale Space for Hi-C Data Analysis

In the field of genomics, understanding the spatial organization of the genome is crucial for uncovering gene regulatory mechanisms. Hi-C technology, as a genome-wide chromosome conformation capture technique, can reveal the three-dimensional structure of the genome, particularly the key role of chromatin loops in gene regulation. However, existing...

Multi-Modal Interpretable Representation for Non-Coding RNA Classification and Class Annotation

Non-coding RNAs (ncRNAs) play critical roles in cellular processes and disease development. Although genome sequencing projects have revealed a vast number of non-coding genes, the functional classification of ncRNAs remains a complex and challenging issue. The diversity, complexity, and functionality of ncRNAs make them important subjects in biome...

Privacy-Preserving Framework for Genomic Computations via Multi-Key Homomorphic Encryption

Privacy-Preserving Framework for Genomic Analysis: A Study Based on Multi-Key Homomorphic Encryption Academic Background With the reduction in the cost of genome sequencing, the widespread availability of genomic data has opened up new possibilities for personalized medicine (also known as genomic medicine). However, genomic data contains a vast am...

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...

SCICONE: Single-Cell Copy Number Calling and Event History Reconstruction

During tumor development, copy number alterations (CNAs) are key drivers of tumor heterogeneity and evolution. Understanding these variations is crucial for developing personalized cancer diagnostics and therapies. Single-cell sequencing technology offers the highest resolution for copy number analysis, down to the individual cell level. However, l...