Genetics

MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network.

Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targeted therapies, reducing drug-related toxicities and improving patients' survival rates. Nevertheless, accurately predicting candidate cancer-druggable genes remains a critical challenge in translational medicine due to the high heterogeneity and complexity of cancer data. In this study, we proposed ...

AI-driven feature selection and epigenetic pattern analysis: A screening strategy of CpGs validated by pyrosequencing for body fluid identification.

Identification of body fluid stain at crime scene is one of the important tasks of forensic evidence analysis. Currently, body fluid-specific CpGs detected by DNA methylation microarray screening, have been widely studied for forensic body fluid identification. However, some CpGs have limited ability to distinguish certain body fluid types. The ongoing need is to discover novel methylation markers...

Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study.

BACKGROUND: Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conv...

An extrapolation-driven network architecture for physics-informed deep learning.

Current physics-informed neural network (PINN) implementations with sequential learning strategies often experience some weaknesses, such as the failu...

Representing DNA for machine learning algorithms: A primer on one-hot, binary, and integer encodings.

This short paper presents an educational approach to teaching three popular methods for encoding DNA sequences: one-hot encoding, binary encoding, and...

AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data.

AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing, an enormous amount ...

Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

BACKGROUND: Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung canc...

Weakly Supervised Classification for Nasopharyngeal Carcinoma With Transformer in Whole Slide Images.

Pathological examination of nasopharyngeal carcinoma (NPC) is an indispensable factor for diagnosis, guiding clinical treatment and judging prognosis....

CAGCL: Predicting Short- and Long-Term Breast Cancer Survival With Cross-Modal Attention and Graph Contrastive Learning.

In breast cancer treatment, accurately predicting how long a patient will survive is crucial for decision-making. This information guides treatment ch...

Deep learning-driven multi-omics sequential diagnosis with Hybrid-OmniSeq: Unraveling breast cancer complexity.

BackgroundBreast cancer results from an uncontrolled growth of breast tissue. Many methods of diagnosis are using multi-omics data to better understan...

Harnessing machine learning and multi-omics to explore tumor evolutionary characteristics and the role of AMOTL1 in prostate cancer.

Although recent advancements have shed light on the crucial role of coordinated evolution among cell subpopulations in influencing disease progression...

Transformers significantly improve splice site prediction.

Mutations that affect RNA splicing significantly impact human diversity and disease. Here we present a method using transformers, a type of machine le...

Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques.

Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropr...

[The future of medicine: an informed look into the "crystal ball"].

This article explores potential future scenarios for the medical field based on current trends, technological advancements, and social dynamics. By ex...

Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA sequencing and molecular docking.

Post-transplant allograft fibrosis remains a challenge in prolonging allograft survival. Regulated cell death has been widely implicated in various k...

Identification of ageing-associated gene signatures in heart failure with preserved ejection fraction by integrated bioinformatics analysis and machine learning.

The incidence of heart failure with preserved ejection fraction (HFpEF) increases with the ageing of populations. This study aimed to explore ageing-a...

RNA Editing Signatures Powered by Artificial Intelligence: A New Frontier in Differentiating Schizophrenia, Bipolar, and Schizoaffective Disorders.

Mental health disorders are devastating illnesses, often misdiagnosed due to overlapping clinical symptoms. Among these conditions, bipolar disorder, ...

A histopathology-based artificial intelligence system assisting the screening of genetic alteration in intrahepatic cholangiocarcinoma.

BACKGROUND: Targeted therapy for intrahepatic cholangiocarcinoma (ICC) shows superior survival outcomes but patients with certain targetable alteratio...

Machine Learning-Assisted, Dual-Channel CRISPR/Cas12a Biosensor-In-Microdroplet for Amplification-Free Nucleic Acid Detection for Food Authenticity Testing.

The development of novel detection technology for meat species authenticity is imperative. Here, we developed a machine learning-supported, dual-chann...

MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.

We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction ter...