AIMC Topic: MicroRNAs

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Validation and Derivation of miRNA-Based Germline Signatures Predicting Radiation Toxicity in Prostate Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Although radiotherapy (RT) is one of the primary treatment modalities used in the treatment of cancer, patients often experience toxicity during or after treatment. RT-induced genitourinary (GU) toxicity is a significant survivorship challen...

Deep-Learning-Based Integration of Sequence and Structure Information for Efficiently Predicting miRNA-Drug Associations.

Journal of chemical information and modeling
Extensive research has shown that microRNAs (miRNAs) play a crucial role in cancer progression, treatment, and drug resistance. They have been recognized as promising potential therapeutic targets for overcoming drug resistance in cancer treatment. H...

Enhanced Early Detection of Colorectal Cancer via Blood Biomarker Combinations Identified Through Extracellular Vesicle Isolation and Artificial Intelligence Analysis.

Journal of extracellular vesicles
Colorectal cancer (CRC) remains a major cause of cancer-related deaths worldwide, with early detection being crucial for improving survival rates. Despite the potential of extracellular vesicles (EVs) as blood biomarkers for CRC diagnosis, their clin...

Artificial intelligence-driven microRNA signature for early detection of gastric cancer: discovery and clinical functional exploration.

British journal of cancer
BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with late-stage diagnoses frequently leading to poor outcomes. This underscores the need for effective early-stage gastric cancer (ESGC) diagnostics.

Identification of novel therapeutic targets in hepatitis-B virus-associated membranous nephropathy using scRNA-seq and machine learning.

Scientific reports
Hepatitis B Virus-associated membranous nephropathy (HBV-MN) significantly impacts renal health, particularly in areas with high HBV prevalence. Understanding the molecular mechanisms underlying HBV-MN is crucial for developing effective therapeutic ...

CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders.

Nucleic acids research
Correcting for confounding variables is often overlooked when computing RNA-RNA correlations, even though it can profoundly affect results. We introduce CorrAdjust, a method for identifying and correcting such hidden confounders. CorrAdjust selects a...

Nonparametric IPSS: fast, flexible feature selection with false discovery control.

Bioinformatics (Oxford, England)
MOTIVATION: Feature selection is a critical task in machine learning and statistics. However, existing feature selection methods either (i) rely on parametric methods such as linear or generalized linear models, (ii) lack theoretical false discovery ...

Identification of hypertension subtypes using microRNA profiles and machine learning.

European journal of endocrinology
OBJECTIVE: Hypertension is a major cardiovascular risk factor affecting about 1 in 3 adults. Although the majority of hypertension cases (∼90%) are classified as "primary hypertension" (PHT), endocrine hypertension (EHT) accounts for ∼10% of cases an...

Inter-view contrastive learning and miRNA fusion for lncRNA-protein interaction prediction in heterogeneous graphs.

Briefings in bioinformatics
Predicting long non-coding RNA (lncRNA)-protein interactions is essential for understanding biological processes and discovering new therapeutic targets. In this study, we propose a novel model based on inter-view contrastive learning and miRNA fusio...

Optimizing sample size for supervised machine learning with bulk transcriptomic sequencing: a learning curve approach.

Briefings in bioinformatics
Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining a suitable sample size that ensures adequate classification accuracy without undue resource allocat...