AIMC Topic: Machine Learning

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ADCNet: a unified framework for predicting the activity of antibody-drug conjugates.

Briefings in bioinformatics
Antibody-drug conjugates (ADCs) have revolutionized the field of cancer treatment in the era of precision medicine due to their ability to precisely target cancer cells and release highly effective drugs. Nevertheless, the rational design and discove...

Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH.

Respiratory research
BACKGROUND: Classification of the etiologies of pleural effusion is a critical challenge in clinical practice. Traditional diagnostic methods rely on a simple cut-off method based on the laboratory tests. However, machine learning (ML) offers a novel...

MANIFEST: Multiomic Platform for Cancer Immunotherapy.

Cancer discovery
Immunotherapy has revolutionized survival outcomes for many patients diagnosed with cancer. However, biomarkers that can reliably distinguish treatment responders from nonresponders, predict potential life-threatening and life-changing drug-induced t...

Gated-GPS: enhancing protein-protein interaction site prediction with scalable learning and imbalance-aware optimization.

Briefings in bioinformatics
In protein-protein interaction site (PPIS) prediction, existing machine learning models struggle with small datasets, limiting their predictive accuracy for unseen proteins. Additionally, class imbalance in protein complexes, where binding residues c...

Integrated multimodal hierarchical fusion and meta-learning for enhanced molecular property prediction.

Briefings in bioinformatics
Accurately predicting the pharmacological and toxicological properties of molecules is a critical step in the drug development process. Owing to the heterogeneity of molecular property prediction tasks, most of the current methods rely on building a ...

LightCTL: lightweight contrastive TCR-pMHC specificity learning with context-aware prompt.

Briefings in bioinformatics
Identification of T cell receptor (TCR) specificities for antigens from large-scale single-cell or bulk TCR repertoire data plays a vital role in disease diagnosis and immunotherapy. In silico prediction models have emerged in recent years. However, ...

Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.

Brain and behavior
INTRODUCTION: Post-stroke depression is one of the important complications of stroke and affects patients' quality of life. Early identification of post-stroke depression is crucial for its timely prevention. The accuracy of machine learning as a pre...

Machine learning-augmented m6A-Seq analysis without a reference genome.

Briefings in bioinformatics
Methylated RNA m6A immunoprecipitation sequencing (m6A-Seq) is a powerful technique for investigating transcriptome-wide m6A modification. However, most of the existing m6A-Seq protocols rely on reference genomes, limiting their use in species lackin...

Identifying GAP43, NMU, and TEX29 as Potential Prognostic Biomarkers for COPD Combined With Lung Cancer Patients Using Machine Learning.

The journal of gene medicine
Chronic obstructive pulmonary disease (COPD) and lung cancer, frequently comorbid conditions intricately linked through smoking, represent significant global health challenges. COPD is a common comorbidity in nonsmall cell lung cancer (NSCLC) patient...

Investigating the Link between Type 2 Diabetes and Epstein-Barr Virus: a Machine Learning and Mendelian Randomization.

Clinical laboratory
BACKGROUND: Epstein-Barr virus (EBV) is a ubiquitous herpesvirus that is known to cause infectious mononucleosis and is associated with several autoimmune diseases and cancers through immune system dysregulation and chronic inflammatory mechanisms.