Genetics

Sequential Integration of Fuzzy Clustering and Expectation Maximization for Transcription Factor Binding Site Identification.

The identification of transcription factor binding sites (TFBSs) is a problem for which computational methods offer great hope. Thus far, the expectation maximization (EM) technique has been successfully utilized in finding TFBSs in DNA sequences, but inappropriate initialization of EM has yielded poor performance or running time scalability under a given data set. In this study, we used a sequent...

Identification of synthetic lethality based on a functional network by using machine learning algorithms.

Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 gene editing, efforts have been made to systematically identify synthetic lethal interactions, especially for frequently mutated genes in cancers. Howev...

iRNA-2OM: A Sequence-Based Predictor for Identifying 2'-O-Methylation Sites in Homo sapiens.

2'-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to...

Integration of Gene Expression Profile Data to Screen and Verify Hub Genes Involved in Osteoarthritis.

Osteoarthritis (OA) is one of the most common diseases worldwide, but the pathogenic genes and pathways are largely unclear. The aim of this study was...

Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks.

OBJECTIVE:: Genetic phenotype plays a central role in making treatment decisions of lung adenocarcinoma, especially the tyrosine-kinase-inhibitors-sen...

KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.

Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparti...

Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks.

BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opport...

Establishment of a SVM classifier to predict recurrence of ovarian cancer.

Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier...

Weakly-Supervised Convolutional Neural Network Architecture for Predicting Protein-DNA Binding.

Although convolutional neural networks (CNN) have outperformed conventional methods in predicting the sequence specificities of protein-DNA binding in...

A machine learning-based prediction model of H3K27M mutations in brainstem gliomas using conventional MRI and clinical features.

BACKGROUND: H3K27M is the most frequent mutation in brainstem gliomas (BSGs), and it has great significance in the differential diagnosis, prognostic ...

Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective.

With the recent advancements in analyzing high-volume, complex, and unstructured data, modern learning methods are playing an increasingly critical ro...

Deep learning to predict the lab-of-origin of engineered DNA.

Genetic engineering projects are rapidly growing in scale and complexity, driven by new tools to design and construct DNA. There is increasing concern...

A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.

Key message New fast and accurate method for phasing and imputation of SNP chip genotypes within diploid bi-parental plant populations. This paper pre...

Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring.

Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high-throu...

Top-Down Fabricated Silicon Nanowire Arrays for Field-Effect Detection of Prostate-Specific Antigen.

Highly sensitive electrical detection of biomarkers for the early stage screening of cancer is desired for future, ultrafast diagnostic platforms. In ...

Monitoring changes in the Gene Ontology and their impact on genomic data analysis.

BACKGROUND: The Gene Ontology (GO) is one of the most widely used resources in molecular and cellular biology, largely through the use of "enrichment ...

Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution.

Expression quantitative trait loci (eQTL) analysis is an emerging method for establishing the impact of genetic variations (such as single nucleotide ...

A Rapid Bacteriophage DNA Extraction Method.

Bacteriophages (phages) are intensely investigated as non-antibiotic alternatives to circumvent antibiotic resistance development as well as last reso...

Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles.

The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysi...

Automation of the Differential Digestion Process of Sexual Assault Evidence.

Sexual assault evidence samples require the use of a specific process known as a differential digestion to separate sperm from nonsperm cells prior to...