Genetics

EMQIT: a machine learning approach for energy based PWM matrix quality improvement.

BACKGROUND: Transcription factor binding affinities to DNA play a key role for the gene regulation. Learning the specificity of the mechanisms of binding TFs to DNA is important both to experimentalists and theoreticians. With the development of high-throughput methods such as, e.g., ChiP-seq the need to provide unbiased models of binding events has been made apparent. We present EMQIT a modificat...

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape...

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

UNLABELLED: Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognitio...

Microcystin-leucine-arginine Modulates the Expression Patterns of Proinflammatory Cytokines and an Apoptotic Gene in Chicken Liver.

Microcystins (MCs) are included in drinking water and a family of cyclic heptapeptide hepatotoxins that have been implicated in the impairment of live...

Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning.

Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomon...

Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism.

PurposeRecognizing individuals with inherited diseases can be difficult because signs and symptoms often overlap those of common medical conditions. F...

Applying an artificial neural network model for developing a severity score for patients with hereditary amyloid polyneuropathy.

Hereditary (familial) amyloid polyneuropathy (FAP) is a systemic disease that includes a sensorimotor polyneuropathy related to transthyretin (TTR) mu...

Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma.

Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performa...

A Novel Case of Somatic Mutation in Pediatric-Onset Aldosterone-Producing Adenoma.

Aldosterone-producing adenoma (APA), a subtype of primary aldosteronism, is a common cause of secondary hypertension in adults. Somatic mutations hav...

An artificial neural network system to identify alleles in reference electropherograms.

Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electr...

ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.

BACKGROUND: A key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the geno...

Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm.

Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges...

Machine learning based identification of protein-protein interactions using derived features of physiochemical properties and evolutionary profiles.

Proteins are the central constitute of a cell or biological system. Proteins execute their functions by interacting with other molecules such as RNA, ...

1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life.

We present 1,003 reference genomes that were sequenced as part of the Genomic Encyclopedia of Bacteria and Archaea (GEBA) initiative, selected to maxi...

Scalable Device for Automated Microbial Electroporation in a Digital Microfluidic Platform.

Electrowetting-on-dielectric (EWD) digital microfluidic laboratory-on-a-chip platforms demonstrate excellent performance in automating labor-intensive...

DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads.

The MinION device by Oxford Nanopore produces very long reads (reads over 100 kBp were reported); however it suffers from high sequencing error rate. ...

Digital-to-biological converter for on-demand production of biologics.

Manufacturing processes for biological molecules in the research laboratory have failed to keep pace with the rapid advances in automization and parel...

CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks.

Many structural variations (SVs) detection methods have been proposed due to the popularization of next-generation sequencing (NGS). These SV calling ...

pDHS-SVM: A prediction method for plant DNase I hypersensitive sites based on support vector machine.

DNase I hypersensitive sites (DHSs) are accessible chromatin regions hypersensitive to cleavages by DNase I endonucleases. DHSs are indicative of cis-...

Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition.

BACKGROUND AND OBJECTIVES: Enhancers are pivotal DNA elements, which are widely used in eukaryotes for activation of transcription genes. On the basis...