Genetics

Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study.

The purpose of this study was to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two women with contralateral breast cancer and 153 women with unilateral breast cancer were identified within the Women's Environmental Cancer and Radiatio...

Prediction of Side Effects Using Comprehensive Similarity Measures.

Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and si...

Alendronate/Vitamin D for attenuating bone mineral density loss during antiretroviral initiation: a pilot randomized controlled trial.

Antiretroviral therapy (ART) initiation is associated with decreases in bone mineral density (BMD). To plan for a larger trial, we sought to obtain p...

In vivo imaging of phosphocreatine with artificial neural networks.

Phosphocreatine (PCr) plays a vital role in neuron and myocyte energy homeostasis. Currently, there are no routine diagnostic tests to noninvasively m...

Optimization of culture conditions for differentiation of melon based on artificial neural network and genetic algorithm.

Artificial neural network is an efficient and accurate fitting method. It has the function of self-learning, which is particularly important for predi...

Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments.

BACKGROUND: Next-generation sequencing enables massively parallel processing, allowing lower cost than the other sequencing technologies. In the subse...

Convolutional Neural Networks-Based Image Analysis for the Detection and Quantification of Neutrophil Extracellular Traps.

Over a decade ago, the formation of neutrophil extracellular traps (NETs) was described as a novel mechanism employed by neutrophils to tackle infecti...

Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes.

BACKGROUND: Transforming large amounts of genomic data into valuable knowledge for predicting complex traits has been an important challenge for anima...

GenEpi: gene-based epistasis discovery using machine learning.

BACKGROUND: Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However,...

A genome-wide multiphenotypic association analysis identified candidate genes and gene ontology shared by four common risky behaviors.

BACKGROUND: Risky behaviors can lead to huge economic and health losses. However, limited efforts are paid to explore the genetic mechanisms of risky ...

Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection.

Recently, deep reinforcement learning, associated with medical big data generated and collected from medical Internet of Things, is prospective for co...

Invited Review: DNA methylation-based classification of paediatric brain tumours.

DNA methylation-based machine learning algorithms represent powerful diagnostic tools that are currently emerging for several fields of tumour classif...

Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and F-FDG PET-CT...

Vitamin D insufficiency is associated with subclinical atherosclerosis in HIV-1-infected patients on combination antiretroviral therapy.

Vitamin D insufficiency has been associated with faster progression of atherosclerosis and increased cardiovascular disease risk, but limited data ar...

A comparison between Artificial Neural Network and Hybrid Intelligent Genetic Algorithm in predicting the severity of fixed object crashes among elderly drivers.

Run-off-road (ROR) crashes have always been a major concern as this type of crash is usually associated with a considerable number of serious injury a...

An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation.

Butyrylation plays a crucial role in the cellular processes. Due to limit of techniques, it is a challenging task to identify histone butyrylation sit...

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicro...

Multimodal Data Analysis of Alzheimer's Disease Based on Clustering Evolutionary Random Forest.

Alzheimer's disease (AD) has become a severe medical challenge. Advances in technologies produced high-dimensional data of different modalities includ...

Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice.

Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack ...

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning.

INTRODUCTION: Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a c...