Genetics

Improvement of Neoantigen Identification Through Convolution Neural Network.

Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer vaccine and adoptive T-cell therapy. However, current algorithms for predicting neoantigens are limited by binding affinity data and algorithmic constraints, inevitably resulting in high false positives. In this study, we proposed a deep convolutio...

Artificial intelligence for diagnosis of inherited retinal disease: an exciting opportunity and one step forward.

COVID-19 diagnosis by routine blood tests using machine learning.

Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from...

DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.

It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding protein...

DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.

Predicting in vivo protein-DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoter...

Application of multi-omics data integration and machine learning approaches to identify epigenetic and transcriptomic differences between in vitro and in vivo produced bovine embryos.

Pregnancy rates for in vitro produced (IVP) embryos are usually lower than for embryos produced in vivo after ovarian superovulation (MOET). This is p...

Benchmarking deep learning splice prediction tools using functional splice assays.

Hereditary disorders are frequently caused by genetic variants that affect pre-messenger RNA splicing. Though genetic variants in the canonical splice...

MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks.

Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional ima...

Autosomal deletion/insertion polymorphisms for global stratification analyses and ancestry origin inferences of different continental populations by machine learning methods.

A lot of population data of 30 deletion/insertion polymorphisms (DIPs) of the Investigator DIPplex kit in different continental populations have been ...

XGraphBoost: Extracting Graph Neural Network-Based Features for a Better Prediction of Molecular Properties.

Determining the properties of chemical molecules is essential for screening candidates similar to a specific drug. These candidate molecules are furth...

Specificity of SARS-CoV-2 Real-Time PCR Improved by Deep Learning Analysis.

Real-time PCR (RT-PCR) is widely used to diagnose human pathogens. RT-PCR data are traditionally analyzed by estimating the threshold cycle ( ) at whi...

Feasibility of predicting allele specific expression from DNA sequencing using machine learning.

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies sho...

Role of Regulatory Non-Coding RNAs in Aggressive Thyroid Cancer: Prospective Applications of Neural Network Analysis.

Thyroid cancer (TC) is the most common endocrine malignancy. Most TCs have a favorable prognosis, whereas anaplastic thyroid carcinoma (ATC) is a leth...

The in vitro micronucleus assay using imaging flow cytometry and deep learning.

The in vitro micronucleus (MN) assay is a well-established assay for quantification of DNA damage, and is required by regulatory bodies worldwide to s...

An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties.

BACKGROUND: DNA-binding hot spots are dominant and fundamental residues that contribute most of the binding free energy yet accounting for a small por...

Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic.

The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovatio...

Identification of potential genomic biomarkers for Parkinson's disease using data pooling of gene expression microarrays.

In this study, we aimed to identify potential diagnostic biomarkers Parkinson's disease (PD) by exploring microarray gene expression data of PD patie...

A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.

DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate co...

Kernel machine SNP set analysis finds the association of BUD13, ZPR1, and APOA5 variants with metabolic syndrome in Tehran Cardio-metabolic Genetics Study.

Metabolic syndrome (MetS) is one of the most important risk factors for cardiovascular disease. The 11p23.3 chromosomal region plays a potential role ...

Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection.

Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcom...