Genetics

HCLmNet: A unified hybrid continual learning strategy multimodal network for lung cancer survival prediction.

Lung cancer survival prediction remains one of the most challenging tasks in modern healthcare, as accurate and adaptive prediction models are essential for improving patient outcomes. However, the continuous inflow of new patient data in hospital environments demands models that can update incrementally without losing prior knowledge a challenge known as catastrophic forgetting. This problem is c...

Multimodal biomarker AI techniques for early neurocognitive disorder diagnosis: A systematic review.

BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and related dementias remains challenging because no single biomarker sufficiently captures the complex and multifactorial nature of the underlying pathology. In recent years, multimodal artificial intelligence (AI) models capable of integrating heterogeneous data sources-such as neuroimaging, fluid biomarkers, genetics, and cognitive assessm...

A heterogeneous graph neural network for candidate gene prediction in endometriosis.

OBJECTIVE: Precision medicine applications depend on elucidating the underlying molecular mechanisms of disease. However, many disorders, such as endo...

The Genetics of TDP-43 Type C Neurodegeneration: A Whole-Genome Sequencing Study and Literature Review.

BACKGROUND AND OBJECTIVES: Frontotemporal lobar degeneration TDP43 type C (TDP-C) is a rare and unique neurodegenerative disease that attacks the ante...

Machine learning derived ORC6 as a hub biomarker for predicting tumor microenvironment, prognoses, and therapeutic responses in lung adenocarcinoma.

The origin recognition complex (ORC) is a DNA-binding complex composed of six subunits involved in DNA replication in cancer cells. The prognostic and...

Interpretable learning of temporal cellular dynamics from single-cell data.

Reconstructing temporal cellular dynamics from static single-cell transcriptomics remains a major challenge. Methods based on RNA velocity are useful,...

A systems view on DNA damage response kinetics in Tetrahymena.

A tightly regulated DNA damage response is critical to the overall integrity of the genome. Here, we combine transcriptomics and proteomics to study D...

Mapping the subgingival HerBiome and HisBiome over the human healthspan.

BACKGROUND: Understanding the intricate relationship between sex, age, and the oral microbiome is crucial for deciphering the onset and progression of...

ToxPLTC: Peptide Toxicity Prediction by Integrating Pretrained T5 Protein Language Model and Text Convolutional Neural Network.

Peptide-based therapeutics show promising potential in treating a range of diseases, such as diabetes, cancer, and chronic pain. However, critical cha...

Genomic Sequencing from Sanger to Next-Generation Sequencing: Historical Context, Comparative Advances, and Prospects for Next-Generation Phenomics.

DNA sequencing has revolutionized biological and biomedical research, offering profound insights into genome organization, function, and variability. ...

Comprehensive multi omics profiling and Mendelian randomization assessment of lipid metabolites in lung cancer prognosis.

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for ...

Integrated single-cell and spatial mapping coupled with machine learning unveils core stemness landscapes and regulatory drivers in triple-negative breast cancer.

OBJECTIVE: Triple-negative breast cancer (TNBC) exhibits pronounced intratumoral heterogeneity, and cancer stem cells (CSCs) are thought to play a piv...

The Synovial Fingerprint of Ankle Fractures: A Central Role for WNT Pathway Activation in Posttraumatic Ankle Osteoarthritis.

BACKGROUND: Osteoarthritis (OA) of the ankle is a disabling and understudied disease, most commonly emerging posttraumatically after ankle fractures (...

D-LIM: A neural network for interpretable gene-gene interactions.

Recent advances in gene editing can produce large genotype-fitness maps for targeted genes, yet predicting the effects of mutations between genes rema...

In silico discovery of selective TPX2 and BUB1B inhibitors as novel antimitotic agents in breast cancer therapy.

Breast cancer (BC) is one of the most common malignancies in women globally, characterized by significant genetic and clinical heterogeneity. This com...

Integrative machine learning analysis suggests novel molecular targets for liver cancer diagnosis and therapy.

BACKGROUND: Hepatocellular carcinoma (HCC) is a critical condition characterized by unchecked cellular growth in the liver, often leading to systemic ...

Integrated bioinformatics and machine learning, research on specific biomarkers for large-artery atherosclerosis stroke.

Large-artery atherosclerosis stroke (LAA) is the main subtype of ischemic stroke. Currently, the diagnosis of LAA is confirmed through magnetic resona...

LLM-guided population-based reinforcement learning: A scalable methodology for adaptive hyperparameter optimization.

Population-Based Training (PBT) has the drawback of using fixed, pre-programmed mutation and selection rules to optimize hyperparameters, which are no...

Unveiling the prognostic and therapeutic landscape of the zinc transporter protein SLC39A family in colorectal cancer through multi-omics and machine learning approaches.

Colorectal cancer (CRC) remains a significant global health challenge due to its high prevalence and mortality rates. Zinc transporter proteins of the...

Recent Advances in the Non-viral Delivery of Genes to Central Nervous System Disorders.

Disorders of the central nervous system (CNS), neurological disorders, neurodegenerative disorders, genetic disorders) constitute a significant burden...