AIMC Topic: Computational Biology

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DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.

Clinical epigenetics
DNA methylation is an epigenetic modification that regulates gene expression by adding methyl groups to DNA, affecting cellular function and disease development. Machine learning, a subset of artificial intelligence, analyzes large datasets to identi...

Identification and validation of cell senescence genes in recurrent spontaneous abortion via multiple bioinformatics algorithms.

Scientific reports
Recurrent spontaneous abortion (RSA) represents a significant challenge in reproductive obstetrics, affecting approximately 5% of couples globally. Despite various treatments, the effectiveness of these interventions remains highly contentious. Emerg...

Rising Stars: Bioinformatics of Post-translational Modifications.

Journal of molecular biology
Yu Xue is a professor in the College of Life Science and Technology at Huazhong University of Science and Technology, and hold a joint position in the Hubei Hongshan Laboratory at Huazhong Agricultural University. He received two B.E. degrees in poly...

Identification of early warning biomarkers for type 4 cardio-renal syndrome based on bioinformatics analysis and secreted proteins.

Scientific reports
Chronic kidney disease (CKD) can induce chronic heart failure (CHF), a condition referred to as type 4 cardiorenal syndrome (CRS4). The pathophysiological mechanisms remain unclear, and suitable early warning biomarkers for CHF in CKD patients are la...

DeepRNA-Reg: a deep-learning based approach for comparative analysis of CLIP experiments.

RNA biology
DeepRNA-Reg employs advances in deep learning to enable high-fidelity comparative analysis of paired datasets of high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP). In a HITS-CLIP experimental paradigm where Ag...

AI-driven discovery of novel extracellular matrix biomarkers in pelvic organ prolapse.

PLoS computational biology
Deep learning for protein function prediction faces significant challenges in identifying disease-specific proteins. We present Extracellular Matrix Protein Predictor (EPOP), an advanced transfer learning framework leveraging protein language models ...

Exploration common biomarkers and pathogenesis of primary Sjögren's syndrome and interstitial lung disease by machine learning and weighted gene co-expression networks.

PloS one
BACKGROUND: Primary Sjögren's syndrome (pSS) is an autoimmune and inflammatory disorder that may affect the lungs, leading to interstitial lung disease (ILD). However, the diagnosis of progression from pSS to ILD is frequently delayed due to unstanda...

Biology-driven insights into the power of single-cell foundation models.

Genome biology
BACKGROUND: Single-cell foundation models (scFMs) have emerged as powerful tools for integrating heterogeneous datasets and exploring biological systems. Despite high expectations, their ability to extract unique biological insights beyond standard m...

Comprehensive identification of immune-related biomarkers and therapeutic targets in preeclampsia: integrative bioinformatics and experimental validation.

BMC pregnancy and childbirth
BACKGROUND: Preeclampsia (PE) is a serious hypertensive complication during pregnancy characterized by immune dysregulation and vascular dysfunction, however, the precise molecular mechanisms and effective therapeutic strategies remain unclear. This ...

Discovering periodontitis biomarkers and therapeutic targets through bioinformatics and ensemble learning analysis.

Scientific reports
Periodontitis, a prevalent inflammatory disease, leads to the progressive destruction of periodontal tissues and poses significant systemic health risks. Despite its widespread impact, the molecular mechanisms driving periodontitis remain poorly unde...