AI Medical Compendium Journal:
Genes

Showing 1 to 10 of 97 articles

The Role of Artificial Intelligence in Identifying Gene Variants and Improving Diagnosis.

Genes
Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder caused by mutations in the gene, typically diagnosed during early childhood and characterized by significant phenotypic heterogeneity. Despite advancements in next-generation sequencin...

Deciphering Gut Microbiome in Colorectal Cancer via Robust Learning Methods.

Genes
BACKGROUND: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and is closely linked to the gut microbiota. Identifying reproducible and generalizable microbial signatures holds significant potential for enhancing early detection ...

GONNMDA: A Ordered Message Passing GNN Approach for miRNA-Disease Association Prediction.

Genes
Small non-coding molecules known as microRNAs (miRNAs) play a critical role in disease diagnosis, treatment, and prognosis evaluation. Traditional wet-lab methods for validating miRNA-disease associations are often time-consuming and inefficient. Wit...

LMFE: A Novel Method for Predicting Plant LncRNA Based on Multi-Feature Fusion and Ensemble Learning.

Genes
: Long non-coding RNAs (lncRNAs) play a crucial regulatory role in plant trait expression and disease management, making their accurate prediction a key research focus for guiding biological experiments. While extensive studies have been conducted on...

Biological Prior Knowledge-Embedded Deep Neural Network for Plant Genomic Prediction.

Genes
Genomic prediction is a powerful approach that predicts phenotypic traits from genotypic information, enabling the acceleration of trait improvement in plant breeding. Traditional genomic prediction methods have primarily relied on linear mixed mode...

CRISPR-MFH: A Lightweight Hybrid Deep Learning Framework with Multi-Feature Encoding for Improved CRISPR-Cas9 Off-Target Prediction.

Genes
BACKGROUND: The CRISPR-Cas9 system has emerged as one of the most promising gene-editing technologies in biology. However, off-target effects remain a significant challenge. While recent advances in deep learning have led to the development of models...

Integrating Artificial Intelligence and Bioinformatics Methods to Identify Disruptive STAT1 Variants Impacting Protein Stability and Function.

Genes
The Signal Transducer and Activator of Transcription 1 () gene is an essential component of the JAK-STAT signaling pathway. This pathway plays a pivotal role in the regulation of different cellular processes, including immune responses, cell growth,...

Integrating Machine Learning-Based Approaches into the Design of ASO Therapies.

Genes
Rare diseases impose a significant burden on affected individuals, caregivers, and healthcare systems worldwide. Developing effective therapeutics for these small patient populations presents substantial challenges. Antisense oligonucleotides (ASOs) ...

Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA.

Genes
BACKGROUND/OBJECTIVES: Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve pro...