Genetics

Characterization of Kidney and Liver Cystic Phenotype Associated with GANAB Using Advanced Imaging Biomarkers.

UNLABELLED: : Background and Hypothesis: Monoallelic pathogenic variants in GANAB cause autosomal dominant cystic kidney and liver disease, but quantitative imaging phenotypes remain incompletely defined. METHODS: We performed a retrospective study of 16 individuals with GANAB variants and available abdominal imaging. Deep learning based-cyst segmentation quantified kidney and liver volumes and cy...

Influence of body composition on the efficacy of nivolumab plus ipilimumab for metastatic clear cell renal cell carcinoma.

BACKGROUND: Immune checkpoint inhibitor therapy (ICI) with nivolumab+ipilimumab is a first-line (1L) standard for metastatic clear cell renal cell carcinoma (ccRCC), yet outcomes remain heterogeneous. Increasing evidence suggests that host factors influence the tumor microenvironment and response to ICI. Although higher body mass index (BMI) has been associated with improved outcomes in several ma...

TCR sequencing in cancer immunology and immunotherapy: what, when, where, why, and how.

T-cell receptors (TCRs) are generated through somatic recombination of variable (V), diversity (D), and joining (J) gene segments, resulting in an ext...

Cross-species gene redesign leveraging ortholog information and generative modeling.

Conventional approaches to heterologous gene expression rely on codon optimization, which is limited to swapping synonymous codons and fails to captur...

Gene driven analytical learning model for accurate breast cancer diagnosis.

Patients diagnosed with breast cancer exhibit a diverse range of prognostic outcomes due to the varied nature of the disease across different patient ...

Integration of circulating tumor DNA data enhances lung cancer prediction in patients with COPD.

Patients with chronic obstructive pulmonary disease (COPD) are at increased risk of lung cancer, and the identification of circulating tumor DNA (ctDN...

Elucidating biopsychosocial mechanisms in migraine: an integrative analytics approach combining genetics, neuroimaging, and machine learning.

BACKGROUND: Migraine is a complex and disabling neurological disorder shaped by the interplay of biological, psychological, and behavioral factors. Cu...

Di-(2-ethylhexyl) terephthalate promotes breast cancer progression: Multi-omics integrated experimental validation.

Di-(2-ethylhexyl) terephthalate (DOTP), as an alternative to phthalate plasticizers, has been widely used in sensitive fields such as food packaging a...

A Deep Learning Framework Integrating Tumor Microenvironmental Features Accurately Predicts Multiple Driver Gene Mutations in Lung Cancer Pathology Images.

UNLABELLED: Deep learning (DL) has the potential to enable the prediction of gene mutations directly from routine histopathology slides in lung cancer...

Integration of Short- and Long-Read RNA Sequencing Enables the Discovery of Circular RNAs.

UNLABELLED: Circular RNAs (circRNA) are associated with crucial hallmarks of tumorigenesis. Select circRNAs contain circular open reading frames (cORF...

Subtype-specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers.

Gene expression is tightly controlled by DNA elements called enhancers by associating with lineage-specific transcription factors. These enhancers tra...

M[Formula: see text]DGAT: Multi-view multi-scale dynamic graph attention network(GAT) based prediction of Parkinson's disease(PD) progression using whole-blood RNA sequencing data.

With emerging single-cell transcriptomics data, deep learning approaches have enabled the diagnosis of neurodegenerative disorders such as Parkinson's...

Developing a Metabolic-Associated Prognostic Index for Risk Stratification and Therapeutic Guidance in Stage I Lung Adenocarcinoma via Multiomics Analysis.

PURPOSE: Early-stage lung adenocarcinoma (LUAD) exhibits substantial clinical heterogeneity that is not fully explained by TNM staging, highlighting t...

WEViT: weight-entangled vision transformers with class-specific attention for weakly supervised semantic segmentation.

Weakly Supervised Semantic Segmentation (WSSS) is a challenging task in computer vision, as it relies on limited supervision to generate precise objec...

From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.

Bone tumors such as osteosarcoma and Ewing sarcoma remain among the most challenging cancers to diagnose and monitor because of their biological heter...

Integrating feature selection with unsupervised deep embedding for clustering single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of gene expression at the individual cell level, with clustering serving as a ...

Transformer-based multidimensional feature fusion for accurate prediction of lipid nanoparticles transfection efficiency.

RNA-based technologies have demonstrated significant potential for diverse applications, ranging from vaccination to gene editing. However, their wide...

Machine Learning Reveals the Association Between Gene Expression and Immune Infiltration in Colorectal Cancer: A Comprehensive Study From Single-Cell to Survival Analysis.

Colorectal cancer (CRC) is one of the most common causes of cancer mortality globally. Analysis of immune cell infiltration patterns in the tumour mic...

Leveraging Artificial Intelligence to Transform Thoracic Radiology for Lung Nodules and Lung Cancer: Applications, Challenges, and Future Directions.

This review traces the historical path of artificial intelligence (AI) methods that have been applied to medical image interpretation. Early AI approa...

Effectiveness of low-density high-throughput marker platform and easy-to-measure traits for genomic prediction of biomass yield in oat (Avena sativa L.).

Genomic selection (GS) is a promising strategy for accelerating genetic gains of complex traits in breeding programs. Despite the recent advancements ...