Genetics

Biologically Enhanced Machine Learning Model to uncover Novel Gene-Drug Targets for Alzheimer's Disease.

Given the complexity and multifactorial nature of Alzheimer's disease, investigating potential drug-gene targets is imperative for developing effective therapies and advancing our understanding of the underlying mechanisms driving the disease. We present an explainable ML model that integrates the role and impact of gene interactions to drive the genomic variant feature selection. The model levera...

Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence.

Single-cell omics technologies have revolutionized the study of long non-coding RNAs (lncRNAs), offering unprecedented resolution in elucidating their expression dynamics, cell-type specificity, and associated gene regulatory networks (GRNs). Concurrently, the integration of artificial intelligence (AI) methodologies has significantly advanced our understanding of lncRNA functions and its implicat...

TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer.

Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mortality among women globally. Despite advances in ...

The Metabolic Treatabolome and Inborn Errors of Metabolism Knowledgebase therapy tool: Do not miss the opportunity to treat!

Inborn errors of metabolism (IEMs) are rare genetic conditions with significant morbidity and mortality. Technological advances have increased therape...

Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.

Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes...

Unveiling the Immune Landscape of Delirium through Single-Cell RNA Sequencing and Machine Learning: Towards Precision Diagnosis and Therapy.

BACKGROUND: Postoperative delirium (POD) poses significant clinical challenges regarding its diagnosis and treatment. Identifying biomarkers that can ...

From Pixels to Prognosis: Artificial Intelligence and Machine Learning Models in Brain Tumour Mutation Prediction.

Brain tumours are a leading cause of death and disability, impacting individuals across all ages, genders, and ethnicities. They are primarily diagnos...

Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning.

Preeclampsia (PE) is a pregnancy-specific disorder characterized by an unclearly understood pathogenesis and poses a great threat to maternal and feta...

Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links.

It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing strategies for the effective therapy of cancer, as...

Applying AI/ML for Analyzing Gene Expression Patterns.

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of genomi...

SVM-LncRNAPro: An SVM-Based Method for Predicting Long Noncoding RNA Promoters.

Long non-coding RNAs (lncRNAs) are closely associated with the regulation of gene expression, whose promoters play a crucial role in comprehensively u...

A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, men...

Parallel DNA Sequence Alignment on High-Performance Systems with CUDA and MPI

Sequence alignment is a cornerstone of bioinformatics, widely used to identify similarities between DNA, RNA, and protein sequences and studying evo...

Are Vision-Language Models Truly Understanding Multi-vision Sensor?

Large-scale Vision-Language Models (VLMs) have advanced by aligning vision inputs with text, significantly improving performance in computer vision ...

An archaeological Catalog Collection Method Based on Large Vision-Language Models

Archaeological catalogs, containing key elements such as artifact images, morphological descriptions, and excavation information, are essential for ...

Paleoinspired Vision: From Exploring Colour Vision Evolution to Inspiring Camera Design

The evolution of colour vision is captivating, as it reveals the adaptive strategies of extinct species while simultaneously inspiring innovations i...

MINIMA: Modality Invariant Image Matching

Image matching for both cross-view and cross-modality plays a critical role in multimodal perception. In practice, the modality gap caused by differ...

Underwater Image Restoration via Polymorphic Large Kernel CNNs

Underwater Image Restoration (UIR) remains a challenging task in computer vision due to the complex degradation of images in underwater environments...

scReader: Prompting Large Language Models to Interpret scRNA-seq Data

Large language models (LLMs) have demonstrated remarkable advancements, primarily due to their capabilities in modeling the hidden relationships wit...

More is Less? A Simulation-Based Approach to Dynamic Interactions between Biases in Multimodal Models

Multimodal machine learning models, such as those that combine text and image modalities, are increasingly used in critical domains including public...