Benchmarking Copy Number Aberrations Inference Tools Using Single-Cell Multi-Omics Datasets

Information Science, Artificial Intelligence, Medicine, Oncology, Life Sciences, Cell Biology, Bioengineering, Genetics,
Single-cell RNA sequencing   Copy number aberrations   Tumor heterogeneity   Benchmarking   Multi-omics  

1. Research Background and Significance In the fields of oncology and genomics, chromosomal copy number alterations (CNAs) are a key type of genetic variation driving the occurrence and progression of cancer. CNAs not only determine tumor heterogeneity but also play a crucial role in early tumor detection, subclone evolution analysis, research on d...

HSSPPI: Hierarchical and Spatial-Sequential Modeling for PPIs Prediction

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Biochemistry, Bioengineering, Genetics,
protein–protein interaction sites   hierarchical graph   spatial form   sequential form   feature fusion  

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...

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

Medicine, Oncology, Life Sciences, Cell Biology, Immunology, Genetics,
neural infiltration   cancer hallmark   tumor microenvironment   single-cell RNA sequencing   immunotherapy  

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...

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

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Cell Biology, Genetics,
scRNA-seq   gene regulatory network   unsupervised learning   Granger causality   recurrent variational autoencoder  

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

Medicine, Medical Imaging, Life Sciences, Bioengineering, Genetics,
Genotype–phenotype association   Complex trait   Heritability   Facial phenotyping   3D shape  

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

Information Science, Artificial Intelligence, Medicine, Oncology, Life Sciences, Biochemistry, Genetics,
cancer genes   causality   large language model   multi-omics   causal learning  

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...