AIMC Topic: Computational Biology
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Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.
Nature communications
Apr 15, 2025
Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To a...
Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning.
Frontiers in immunology
Apr 15, 2025
INTRODUCTION: Aging is characterized by gradual structural and functional changes in the body over time, with intervertebral disc degeneration (IVDD) representing a key manifestation of spinal aging and a major contributor to low back pain (LBP).
Deep learning tools predict variants in disordered regions with lower sensitivity.
BMC genomics
Apr 12, 2025
BACKGROUND: The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined ...
Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.
Scientific reports
Apr 11, 2025
Over the past several decades, there has been a significant increase in the number of breast cancer patients. Among the four subtypes of breast cancer, Her2-positive breast cancer is one of the most aggressive breast cancers. In this study, we screen...
Identification of hub genes for the diagnosis associated with heart failure using multiple cell death patterns.
ESC heart failure
Apr 10, 2025
AIMS: Heart failure (HF) is an important public health problem worldwide, and programmed cell death (PCD) plays a crucial role in its pathologic process. This study aims to identify the hub genes associated with HF through PCD in order to better unde...
Predicting viral host codon fitness and path shifting through tree-based learning on codon usage biases and genomic characteristics.
Scientific reports
Apr 10, 2025
Viral codon fitness (VCF) of the host and the VCF shifting has seldom been studied under quantitative measurements, although they could be concepts vital to understand pathogen epidemiology. This study demonstrates that the relative synonymous codon ...
Analysis and validation of hub genes for atherosclerosis and AIDS and immune infiltration characteristics based on bioinformatics and machine learning.
Scientific reports
Apr 10, 2025
Atherosclerosis is the major cause of cardiovascular diseases worldwide, and AIDS linked with chronic inflammation and immune activation, increases atherosclerosis risk. The application of bioinformatics and machine learning to identify hub genes for...
Integrative Multi-Omics Analysis Reveals Molecular Subtypes of Ovarian Cancer and Constructs Prognostic Models.
Journal of immunotherapy (Hagerstown, Md. : 1997)
Apr 9, 2025
Ovarian cancer (OV) remains the most lethal gynecological malignancy. The aim of this study was to identify molecular subtypes of OV through integrative multi-omics analysis and construct machine learning-based prognostic models for predicting the ef...
Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis.
BMC cancer
Apr 9, 2025
BACKGROUND: Gastric cancer (GC) is a highly aggressive and heterogeneous cancer with extremely complex biological characteristics. Lysine and its metabolism are closely related to human cancer, but little is known about how lysine metabolism-related ...
Gene Expression Regulation, Neoplastic
Nomograms
Cell Proliferation
Carcinogenesis
Gene Regulatory Networks
Single-Cell Gene Expression Analysis
Computational Biology
Sequence Analysis, RNA
Vimentin
Lysine
Humans
Machine Learning
Prognosis
Stomach Neoplasms
Biomarkers, Tumor
Cell Line, Tumor
Single-Cell Analysis
RNA-Seq
Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma.
BMC cancer
Apr 9, 2025
OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise i...
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