Genetics

Machine learning-based prognostic model and single-cell transcriptomic integration for identifying brain metastasis-associated malignant subpopulations and potential therapeutic targets in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer. Brain metastasis (BrM) is a major cause of mortality in patients with LUAD. The tumor microenvironment (TME) of LUAD BrM remains incompletely characterized, and there is an urgent need to identify biologically relevant biomarkers and potential therapeutic vulnerabilities. METHODS: Single-cell transcriptomi...

Comparing machine learning methods predicting transcriptome from epigenome with applications to association studies.

BACKGROUND: Understanding how epigenome variation contributes to gene expression in disease and development is a fundamental challenge. Regulatory regions show cell type-specific epigenome activity and differ in their location, size, and distance to their target genes, complicating discovery and analysis. Recent machine learning models have been proposed to address these problems by learning funct...

Identification of ITGB2, ELN, and KLRK1 as stress granule-related biomarkers of pterygium through integrated transcriptomic analysis and machine learning.

BACKGROUND: Stress granules (SGs) play a crucial role in cellular stress responses, but their involvement remains unclear in pterygium. This study aim...

Salt-Mediated Electro-Deformation of AAV Capsids Revealed by Nanopipette-Based Single-Particle Analysis.

Adeno-associated viruses (AAVs) are leading gene therapy vectors; however, maintaining their structural integrity and cargo stability remains a major ...

Developing digital PCR technologies based on wettable substrates.

Since the invention of the polymerase chain reaction (PCR) in 1985, the technology has evolved from qualitative PCR to real-time fluorescent quantitat...

Label-Free Prediction of EGFR Mutation Status Using Fluorescence Lifetime Imaging and Deep Learning in Lung Adenocarcinoma.

UNLABELLED: Accurate prediction of epidermal growth factor receptor (EGFR) mutations is essential for guiding targeted therapy in non-small cell lung ...

Pharmacogenomics in oncology: mutation-targeted therapy and biomarker integration in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most common cancer-related cause of death among all countries globally, mostly because of late diagnosis, he...

A Controlled Comparison of Human and AI-Assisted Automated Revision of Delphi Statements on RNA-Based Medicines: Parallel, 2-Arm Study.

BACKGROUND: The Delphi method is widely used to derive expert consensus on complex clinical problems, yet it is slow and resource intensive. Recent ad...

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms.

Predicting cancer drug responses (CDRs) accurately remains a significant challenge due to the complexity of tumor biology and the limitations of exist...

Genetic markers of stomatal cluster development in Begoniaceae revealed through trait analysis assisted by interactive deep-learning.

Stomata of plants track the immediate demand for carbon dioxide for photosynthesis while limiting transpirational water loss. Solitary stomatal patter...

Bidirectional microbiota-cancer crosstalk: Emerging platforms for advanced diagnostics and precision therapeutics in oncology.

Cancer is among the major causes of morbidity and mortality worldwide, and current treatment methods are limited by various drawbacks such as drug res...

Serum metabolomics profiling in head and neck squamous cell carcinoma: a multicentre diagnostic study.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is frequently diagnosed at advanced stages, when outcomes are poor. Biomarkers for HNSCC det...

Beyond Lipid Lowering: Targeting lncRNAs as a Novel Therapeutic Strategy for Vascular Wall Defense in Atherosclerosis.

Atherosclerosis remains the primary pathological foundation for severe cardiovascular events. Although guideline-directed medical therapy-particularly...

Deep learning-based CD8+ T cell model for predicting prognosis and‌ targeted immunotherapy benefit in ccRCC.

Deep learning holds significant promise for improving clear cell renal cell carcinoma (ccRCC) diagnosis and treatment. However, studies concerning tum...

High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples.

Single-cell sequencing methods such as scRNA-seq and scATAC-seq have advanced our understanding of individual cellular functions but experimentally ad...

Understanding language model scaling for protein fitness prediction.

Protein language models, as well as models that incorporate structural information or homologous sequences, estimate the sequence likelihood p(sequenc...

SERS-based spectral analysis of ITS1 PCR products for differentiation of three old world Leishmania species.

Leishmaniasis, a zoonotic disease caused by parasites of the genus Leishmania, poses a significant medical and veterinary importance worldwide. This s...

A scoping review on emerging biomarkers in inflammatory bowel disease: Towards precision medicine in diagnosis and therapeutic management.

BACKGROUND: Inflammatory Bowel Diseases (IBD) are chronic conditions presenting significant diagnostic and management challenges. Current invasive met...

Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning.

BACKGROUND: Psoriasis is a multifaceted skin and systemic disorder driven by a complex interplay of genetic, immunological, and environmental factors....

Evolving optimal text clusters: A novel GA-driven framework for dynamic ensemble fusion of multi-model contextual embeddings.

Text clustering is an essential activity in unsupervised natural language processing (NLP), and allows the automatic structure of large-scale textual ...