AIMC Topic: Computational Biology

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Protein function prediction using GO similarity-based heterogeneous network propagation.

Scientific reports
Protein function prediction is a fundamental cornerstone in bioinformatics, providing critical insights into biological processes and disease mechanisms. Despite significant advances, challenges persist due to data sparsity and functional ambiguity. ...

Elucidating the role of KLRD1 in coronary atherosclerosis: harnessing bioinformatics and machine learning to advance understanding.

Journal of cardiothoracic surgery
BACKGROUND: Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serves as a major contributor to cardiovascular diseases. KLRD1 is a gene that encodes a protein involved...

MLOmics: Cancer Multi-Omics Database for Machine Learning.

Scientific data
Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these successful machine learning models are the high-quality training dataset...

Identification of key genes regulating colorectal cancer stem cell characteristics by bioinformatics analysis.

Medicine
Cancer stem cells (CSCs), distinguished by their abilities to differentiate and self-renew, play a pivotal role in the progression of colorectal cancer (CRC). However, the mechanisms that sustain CSCs in CRC remain unclear. This study aimed to identi...

The role of mitochondrial dysfunction in the pathogenesis of atherosclerosis: A new exploration from bioinformatics analysis.

Medicine
Atherosclerosis (AS) is a complex cardiovascular disease associated with mitochondrial dysfunction (MD), which contributes to plaque formation and instability. This study explores the relationship and shared risk factors between the pathogenesis of A...

scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links.

Nature communications
Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our u...

LGLoc as a new language model-driven graph neural network for mRNA localization.

Scientific reports
The localization of mRNA is crucial for the synthesis of functional proteins and plays a significant role in cellular processes. Understanding mRNA localization can enhance applications in disease diagnosis (e.g., cancer, Alzheimer's) and drug develo...

Identifying shared hub genes in LIRI and MASLD through bioinformatics analysis and machine learning.

Scientific reports
Patients with metabolic dysfunction-associated steatotic liver disease (MASLD) are more susceptible to liver ischemia-reperfusion injury (LIRI), complicating liver surgery outcomes. This study aimed to uncover shared hub genes and mechanisms linking ...

Integrating bioinformatics and machine learning to identify biomarkers of branched chain amino acid related genes in osteoarthritis.

BMC musculoskeletal disorders
BACKGROUND: Branched-chain amino acids (BCAA) metabolism is significantly associated with osteoarthritis (OA), but the specific mechanism of BCAA related genes (BCAA-RGs) in OA is still unclear. Therefore, this research intended to identify potential...

Utilizing Dual-Channel Graph and Hypergraph Convolution Network to Discover Microbes Underlying Disease Traits.

Journal of chemical information and modeling
Discovering microbes underlying disease traits opens up opportunities for the diagnosis and effective treatment of diseases. However, traditional methods are often based on biological experiments, which are not only time-consuming but also costly, dr...