Genetics

Application of machine learning algorithm and modified high resolution DNA melting curve analysis for molecular subtyping of Salmonella isolates from various epidemiological backgrounds in northern Thailand.

Food poisoning from consumption of food contaminated with non-typhoidal Salmonella spp. is a global problem. A modified high resolution DNA melting curve analysis (m-HRMa) was introduced to provide effective discrimination among closely related HRM curves of amplicons generated from selected Salmonella genome sequences enabled Salmonella spp. to be classified into discrete clusters. Combination of...

Evaluation of marker selection methods and statistical models for chronological age prediction based on DNA methylation.

In forensic investigation, retrieving biological information from DNA evidence is a promising field of interest. One of the applications is on the estimation of the age of the donor based on DNA methylation. A large number of studies focused on age prediction using the 450 K Human Methylation Beadchip. Various marker selection methods and prediction models have been considered. However, there is a...

Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas.

Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary background in over one-third of patients. Mutations in...

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study.

The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwi...

Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning.

The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times ...

Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis.

A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerabi...

Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex): Unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters.

We developed a new mutationally well-balanced 32 Y-STR multiplex (CombYplex) together with a machine learning (ML) program PredYMaLe to assess the imp...

Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.

To date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both e...

Denoising DNA Encoded Library Screens with Sparse Learning.

DNA-encoded libraries (DELs) are large, pooled collections of compounds in which every library member is attached to a stretch of DNA encoding its com...

Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning.

Single-cell RNA sequencing (scRNA-seq) measurements of gene expression enable an unprecedented high-resolution view into cellular state. However, curr...

Epigenome-based splicing prediction using a recurrent neural network.

Alternative RNA splicing provides an important means to expand metazoan transcriptome diversity. Contrary to what was accepted previously, splicing is...

Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing.

In personalized medicine, many factors influence the choice of compounds. Hence, the selection of suitable medicine for patients with non-small-cell l...

A fully automated deep learning pipeline for high-throughput colony segmentation and classification.

Adenine auxotrophy is a commonly used non-selective genetic marker in yeast research. It allows investigators to easily visualize and quantify various...

Enhancer recognition and prediction during spermatogenesis based on deep convolutional neural networks.

MOTIVATION: enhancers play an important role in the regulation of gene expression during spermatogenesis. The development of ChIP-Chip and ChIP-Seq se...

Integrating spatial gene expression and breast tumour morphology via deep learning.

Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morpho...

Hereditary disease prediction in eukaryotic DNA: an adaptive signal processing approach.

Hereditary disease prediction in eukaryotic DNA using signal processing approaches is an incredible work in bioinformatics. Researchers of various fie...

Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study.

Despite the success and fast adaptation of deep learning models in biomedical domains, their lack of interpretability remains an issue. Here, we intro...

A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory fa...

Ab initio GO-based mining for non-tandem-duplicated functional clusters in three model plant diploid genomes.

A functional Non-Tandem Duplicated Cluster (FNTDC) is a group of non-tandem-duplicated genes that are located closer than expected by mere chance and ...

KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters.

Advances in high-throughput sequencing technologies have reduced the cost of genotyping dramatically and led to genomic prediction being widely used i...