HSSPPI: Hierarchical and Spatial-Sequential Modeling for PPIs Prediction

Background: Unveiling the Bottlenecks and Opportunities in Protein Interaction Prediction Proteins serve as the core molecules for life activities, participating in almost all biological processes and cellular functions, including gene expression, RNA transcription, DNA synthesis, immune response, and more. Protein-protein interactions (PPI), as we...

GCduo: An Open-Source Software for GC × GC–MS Data Analysis

Academic Background and Research Motivation With the growing demand for the analysis of complex samples, chromatographic technologies—especially comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC–MS)—have emerged as a powerhouse for untargeted metabolomics and related fields, demonstrating exceptional resolving p...

Unveiling a Novel Cancer Hallmark by Evaluation of Neural Infiltration in Cancer

Cancer, as a major global public health challenge, is characterized by complex mechanisms underlying its onset and progression. For a long time, processes within the tumor microenvironment (TME)—such as immunity, inflammation, and angiogenesis—have been extensively studied and considered key determinants of tumor biological behavior. In recent year...

MAEST: Accurate Spatial Domain Detection in Spatial Transcriptomics with Graph Masked Autoencoder

Spatial Transcriptomics: Cutting-Edge Technology for Deciphering Spatial Heterogeneity in Tissues Spatial transcriptomics (ST) is an emerging sequencing technology that has rapidly developed in recent years. Its core advantage lies in the simultaneous acquisition of gene expression and spatial location information at the tissue section level, provi...

TopoQA: A Topological Deep Learning-Based Approach for Protein Complex Structure Interface Quality Assessment

Academic Background The elucidation of protein complex 3D structures is a central topic in modern structural biology, molecular mechanism studies, drug design, and even artificial protein design. The function of a protein is often determined by its structure, and many biological processes involve complex interactions between proteins. Although trad...

Inferring Gene Regulatory Networks from Time-Series scRNA-Seq Data via Granger Causal Recurrent Autoencoders

1. Academic Background and Research Motivation In recent years, single-cell RNA sequencing (scRNA-seq) has become one of the most groundbreaking technologies in life sciences and medical research, enabling researchers to capture subtle differences in transcript levels among numerous cells at the resolution of individual cells. This technology has g...

Optimized Phenotyping of Complex Morphological Traits: Enhancing Discovery of Common and Rare Genetic Variants

1. Academic Background and Research Motivation In recent years, genotype–phenotype (G-P) association analysis has become a core means of revealing the genetic basis of complex traits, especially with rapid development in the study of multidimensional structural traits such as the human face, limbs, and skeleton. Traditionally, G-P analyses rely on ...

Cancer Gene Identification through Integrating Causal Prompting Large Language Model with Omics Data–Driven Causal Inference

Cancer gene identification is a core challenge in the fields of basic cancer research and precision medicine. Recently, a research team from Jilin University and Zhejiang Sci-Tech University published an original study titled “Cancer gene identification through integrating causal prompting large language model with omics data–driven causal inferenc...

Cox-SAGE: Enhancing Cox Proportional Hazards Model with Interpretable Graph Neural Networks for Cancer Prognosis

1. Research Background and Disciplinary Frontiers Cancer prognosis analysis has always been a core research direction in the medical field. In recent years, with the widespread application of high-throughput sequencing technologies, scientists have been able to delve deeper into exploring molecular biomarkers and clinical characteristics of cancer ...

Testing and Overcoming the Limitations of Modular Response Analysis

Research Background: New Challenges in Network Inference In the fields of modern molecular biology and systems biology, the precise elucidation of biomolecular networks—such as gene regulatory networks, protein interaction networks, and signaling networks—is regarded as central to understanding cellular processes, disease mechanisms, and drug actio...