Genetics

Dynamics reconstruction and classification via Koopman features.

Knowledge discovery and information extraction of large and complex datasets has attracted great attention in wide-ranging areas from statistics and biology to medicine. Tools from machine learning, data mining, and neurocomputing have been extensively explored and utilized to accomplish such compelling data analytics tasks. However, for time-series data presenting active dynamic characteristics, ...

Performance of neural network basecalling tools for Oxford Nanopore sequencing.

BACKGROUND: Basecalling, the computational process of translating raw electrical signal to nucleotide sequence, is of critical importance to the sequencing platforms produced by Oxford Nanopore Technologies (ONT). Here, we examine the performance of different basecalling tools, looking at accuracy at the level of bases within individual reads and at majority-rule consensus basecalls in an assembly...

Analysis of the Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach.

Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body...

Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality.

BACKGROUND: In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatical...

Detection of the BRAF V600E Mutation in Colorectal Cancer by NIR Spectroscopy in Conjunction with Counter Propagation Artificial Neural Network.

This paper proposes a sensitive, sample preparation-free, rapid, and low-cost method for the detection of the B-rapidly accelerated fibrosarcoma (BRAF...

Prediction of activity and specificity of CRISPR-Cpf1 using convolutional deep learning neural networks.

BACKGROUND: CRISPR-Cpf1 has recently been reported as another RNA-guided endonuclease of class 2 CRISPR-Cas system, which expands the molecular biolog...

fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies.

BACKGROUND: Parametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to out...

Protein complex identification based on weighted PPI network with multi-source information.

Proteins form complexes to accomplish biological functions such as transcription of DNA, translation of mRNA and cell growth. Detection of protein com...

Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network.

Modeling in-vivo protein-DNA binding is not only fundamental for further understanding of the regulatory mechanisms, but also a challenging task in co...

Prediction of Enzyme Function Based on Three Parallel Deep CNN and Amino Acid Mutation.

During the past decade, due to the number of proteins in PDB database being increased gradually, traditional methods cannot better understand the func...

Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.

BACKGROUND: Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at...

Identifying genetic determinants of complex phenotypes from whole genome sequence data.

BACKGROUND: A critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. Howeve...

Kinetics of -induced gene silencing can be predicted from combinations of epigenetic and genomic features.

To initiate X-Chromosome inactivation (XCI), the long noncoding RNA mediates chromosome-wide gene silencing of one X Chromosome in female mammals to ...

A machine-learning approach for accurate detection of copy number variants from exome sequencing.

Copy number variants (CNVs) are a major cause of several genetic disorders, making their detection an essential component of genetic analysis pipeline...

A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequ...

An Intelligent DNA Nanorobot with Enhanced Protein Lysosomal Degradation of HER2.

DNA nanorobots have emerged as new tools for nanomedicine with the potential to ameliorate the delivery and anticancer efficacy of various drugs. DNA ...

HTR2C Gene Variant and Salivary Cortisol Levels after Endurance Physical Activity: A Pilot Study.

INTRODUCTION: The 5-hydroxytryptamine 2C receptor (HTR2C) rs6318 polymorphism has been associated with increased sensitivity to stress. This study inv...

Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data.

DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read b...

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse.

Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders ("synaptopathies"). ...

Wilson's disease: A new perspective review on its genetics, diagnosis and treatment.

Wilson's disease (WD) is an autosomal recessive disorder which is caused by poor excretion of copper in mammalian cells. In this review, various issue...