Genetics

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis.

BACKGROUND: The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or po...

Robotic assisted generation of 2'-deoxy-2'-fluoro-modifed RNA aptamers - High performance enabling strategies in aptamer selection.

Aptamer selection is a laborious procedure, requiring expertise and significant resources. These characteristics limit the accessibility of researchers to these molecular tools. We describe a selection procedure, making use of a robotic system that allows the fully automated selection of RNA and 2'deoxy-2'-fluoro pyrimidine RNA aptamers. The platform offers a rapid access to aptamers for basic res...

Salivary microRNAs identified by small RNA sequencing and machine learning as potential biomarkers of alcohol dependence.

Salivary miRNA can be easily accessible biomarkers of alcohol dependence (AD). The miRNA transcriptome in the saliva of 56 African-Americans (AAs; 2...

Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning.

BACKGROUND: The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system...

Pre-Treatment Biomarkers of Anti-Tumour Necrosis Factor Therapy Response in Crohn's Disease-A Systematic Review and Gene Ontology Analysis.

The most prominent treatment for the serious cases of Crohn's disease (CD) are biological tumour necrosis factor (TNF) inhibitors. Unfortunately, ther...

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems.

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search p...

Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.

We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specif...

Exploratory Gene Ontology Analysis with Interactive Visualization.

The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis ...

Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes.

Selection hyper-heuristics (HHs) are randomised search methodologies which choose and execute heuristics during the optimisation process from a set of...

DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.

DNA-binding proteins (DBPs) participate in various biological processes including DNA replication, recombination, and repair. In the human genome, abo...

Gene shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data.

BACKGROUND: Gene shaving (GS) is an essential and challenging tools for biomedical researchers due to the large number of genes in human genome and th...

Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides.

BACKGROUND: Targeting epitopes derived from neo-antigens (or "neo-epitopes") represents a promising immunotherapy approach with limited off-target eff...

Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning.

Preclinical studies of psychiatric disorders use animal models to investigate the impact of environmental factors or genetic mutations on complex trai...

HOME: a histogram based machine learning approach for effective identification of differentially methylated regions.

BACKGROUND: The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution th...

NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer.

BACKGROUND: The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in preci...

Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model.

Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients, which can not only effectively a...

Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning.

BACKGROUND AND OBJECTIVE: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary e...

MCP: A multi-component learning machine to predict protein secondary structure.

The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through the translation of DNA into protei...

Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning.

To develop a deep learning system based on 3D convolutional neural networks (CNNs), and to automatically predict EGFR-mutant pulmonary adenocarcinoma ...

Recognition of 3'-end L1, Alu, processed pseudogenes, and mRNA stem-loops in the human genome using sequence-based and structure-based machine-learning models.

The role of 3'-end stem-loops in retrotransposition was experimentally demonstrated for transposons of various species, where LINE-SINE retrotransposo...