Genetics

sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.

We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding. As an important component of the CRISPR/Cas9 system, single-guide RNA (sgRNA) plays an important role in gene redirection and editing. sgRNA has played an important role in the improvement of agronomic species, but there i...

Utilization of circulating cell-free DNA profiling to guide first-line chemotherapy in advanced lung squamous cell carcinoma.

Platinum-based chemotherapy is one of treatment mainstay for patients with advanced lung squamous cell carcinoma (LUSC) but it is still a "one-size fits all" approach. Here, we aimed to investigate the predictive and monitoring role of circulating cell-free DNA (cfDNA) profiling for the outcome of first-line chemotherapy in patients with advanced LUSC. Peripheral blood samples of 155 patients fr...

Evolution of drug resistance in HIV protease.

BACKGROUND: Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resi...

Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy.

BACKGROUND: Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a sm...

Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data.

Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustainin...

A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO.

Breast cancer is the leading diseases of death in women. It induces by a genetic mutation in breast cancer cells. Genetic testing has become popular t...

Network-based drug sensitivity prediction.

BACKGROUND: Drug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. ...

Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications.

In this study, we proposed a systems biology approach to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets a...

Construction and analysis of a joint diagnosis model of random forest and artificial neural network for heart failure.

Heart failure is a global health problem that affects approximately 26 million people worldwide. As conventional diagnostic techniques for heart failu...

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs.

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Res...

Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data.

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its...

pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters.

A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Differe...

Modeling the human aging transcriptome across tissues, health status, and sex.

Aging in humans is an incredibly complex biological process that leads to increased susceptibility to various diseases. Understanding which genes are ...

Necrotic enteritis challenge regulates peroxisome proliferator-1 activated receptors signaling and β-oxidation pathways in broiler chickens.

Necrotic enteritis (NE) is an important enteric disease in poultry and has become a major concern in poultry production in the post-antibiotic era. Th...

Deep neural networks for inferring binding sites of RNA-binding proteins by using distributed representations of RNA primary sequence and secondary structure.

BACKGROUND: RNA binding proteins (RBPs) play a vital role in post-transcriptional processes in all eukaryotes, such as splicing regulation, mRNA trans...

Predicting the DNA Conductance Using a Deep Feedforward Neural Network Model.

Double-stranded DNA (dsDNA) has been established as an efficient medium for charge migration, bringing it to the forefront of the field of molecular e...

Review of the genus Shilovia Makarchenko (Diptera: Chironomidae: Diamesinae: Boreoheptagyiini) from the mountains of Central Asia, with morphological description and DNA barcoding of known species.

Chironomids of the genus Shilovia Makarchenko (Diamesinae, Boreoheptagyiini) from the mountains of Central Asia are revised using both morphological c...

Machine-Learning Single-Stranded DNA Nanoparticles for Bacterial Analysis.

A two-dimensional nanoparticle-single-stranded DNA (ssDNA) array has been assembled for the detection of bacterial species using machine-learning (ML)...

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, vira...

Predicting potential residues associated with lung cancer using deep neural network.

Lung cancer is a prominent type of cancer, which leads to high mortality rate worldwide. The major lung cancers lung adenocarcinoma (LUAD) and lung sq...