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Computational Biology

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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...

DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration.

BMC bioinformatics
BACKGROUND: Chromatin loops are critical for the three-dimensional organization of the genome and gene regulation. Accurate identification of chromatin loops is essential for understanding the regulatory mechanisms in disease. However, current mainst...

Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.

Scientific reports
ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with a poor prognosis. Shortly after STEMI occurs, the increased number of circulating leukocytes including macrophages can lead to the accumulation of more...

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...

Machine Learning Identification of Neutrophil Extracellular Trap-Related Genes as Potential Biomarkers and Therapeutic Targets for Bronchopulmonary Dysplasia.

International journal of molecular sciences
Neutrophil extracellular traps (NETs) play a key role in the development of bronchopulmonary dysplasia (BPD), yet their molecular mechanisms in contributing to BPD remain unexplored. Using the GSE32472 dataset, which includes 100 blood samples from p...

optRF: Optimising random forest stability by determining the optimal number of trees.

BMC bioinformatics
Machine learning is frequently used to make decisions based on big data. Among these techniques, random forest is particularly prominent. Although random forest is known to have many advantages, one aspect that is often overseen is that it is a non-d...

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

PLoS computational biology
Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug...

SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study.

Scientific reports
In this study, we propose a novel approach for breast cancer classification that integrates the Seagull Optimization Algorithm (SGA) for feature selection with the Random Forest (RF) classifier for effective data classification. The novelty of our ap...

Digenic variant interpretation with hypothesis-driven explainable AI.

NAR genomics and bioinformatics
The digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately interpreting and prioritizing digenic combinations of variants based on the proband's phenotypes and f...

N6-methyladenine identification using deep learning and discriminative feature integration.

BMC medical genomics
N6-methyladenine (6 mA) is a pivotal DNA modification that plays a crucial role in epigenetic regulation, gene expression, and various biological processes. With advancements in sequencing technologies and computational biology, there is an increasin...