Genetics

Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with a poor prognosis due to its aggressive nature and limited treatment options. Cytoskeletal dynamics play a critical role in tumor progression, but the prognostic and therapeutic potential of cytoskeleton-related genes in HCC remains underexplored. In this study, transcriptomic data from the TCGA-LIHC dataset w...

Predicting RNA Structure Utilizing Attention from Pretrained Language Models.

RNA possesses functional significance that extends beyond the transport of genetic information. The functional roles of noncoding RNA can be mediated through their tertiary and secondary structure, and thus, predicting RNA structure holds great promise for unleashing their applications in diagnostics and therapeutics. However, predicting the three-dimensional (3D) structure of RNA remains challeng...

Genetic features for drug responses in cancer - Investigating an ensemble-feature-selection approach.

Predicting drug responses using genetic and transcriptomic features is crucial for enhancing personalized medicine. In this study, we implemented an e...

Integrative multi-omics analysis reveals BEST1 as a potential tumor-associated gene in gliomas.

BACKGROUND: The newest glioma classification in WHO 2021 emphasizes the importance of gene mutations in the glioma molecular pathogenesis. Our researc...

Smart-Plexer 2.0: Leveraging New Features of Amplification Curves to Enhance the Selection of Multiplex PCR Assays in Multi-Target Identification.

Multiplex PCR plays a critical role in diagnostics by enabling the detection of multiple targets in a single reaction. However, its use is often limit...

Deep learning-enhanced development of innovative antioxidant liposomal drug delivery systems from natural herbs.

Free radical-mediated oxidative damage to biological macromolecules, such as DNA and proteins, significantly contributes to cellular ageing. Antioxida...

Leveraging protein language models for cross-variant CRISPR/Cas9 sgRNA activity prediction.

MOTIVATION: Accurate prediction of single-guide RNA (sgRNA) activity is crucial for optimizing the CRISPR/Cas9 gene-editing system, as it directly inf...

Deep learning deciphers the related role of master regulators and G-quadruplexes in tissue specification.

G-quadruplexes (GQs) are non-canonical DNA structures encoded by G-flipons with potential roles in gene regulation and chromatin structure. Here, we e...

DCAF13 Regulates Cell Proliferation and Immune Escape of Hepatocellular Carcinoma Through Activating the NF-κB Pathway.

Hepatocellular carcinoma (HCC), the third leading cause of global cancer deaths, has a high unmet clinical need due to limited therapeutic efficacy. I...

Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis.

Approximately 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, while biomarkers predictive of treatment resp...

False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions.

As collaborative Machine Learning on cross-institutional, fully distributed networks become an important tool in predictive health modeling, its inher...

Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types.

Breast cancer is the most prevalent and lethal form of cancer being the utmost common medical concern of women. Breast cancer etiology implicates nume...

A review of the use of tumour DNA methylation for breast cancer subtyping and prediction of outcomes.

DNA methylation in breast tumours has been extensively studied and has provided valuable insights into the clinical heterogeneity of breast cancer. In...

Clustering cell nuclei on microgrooves for disease diagnosis using deep learning.

Various diseases including laminopathies and certain types of cancer are associated with abnormal nuclear mechanical properties that influence cellula...

GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data.

Congenital glaucoma, a complex and diverse condition, presents considerable difficulties in its identification and categorization. This research used ...

Enhanced security for medical images using a new 5D hyper chaotic map and deep learning based segmentation.

Medical image encryption is important for maintaining the confidentiality of sensitive medical data and protecting patient privacy. Contemporary healt...

Management and prediction of river flood utilizing optimization approach of artificial intelligence evolutionary algorithms.

Flooding is a devastating natural disaster that causes fatalities and property damage worldwide. Effective flood susceptibility mapping (FSM) has beco...

Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers.

PANoptosis is closely associated with tumorigenesis and therapeutic response, yet its role in multiple myeloma (MM) remains unclear. This study analyz...

RareNet: a deep learning model for rare cancer diagnosis.

Although significant advances have been made in the early detection of many cancers, challenges remain in the early diagnosis of rare cancers, includi...

Integrating machine learning and bioinformatics approaches to identify novel diagnostic gene biomarkers for diabetic mice.

Diabetes is a complex metabolic disorder, and its pathogenesis involves the interplay of genetic, environmental factors, and lifestyle choices. With t...