Genetics

COVID-19 diagnosis using clinical markers and multiple explainable artificial intelligence approaches: A case study from Ecuador.

The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respiratory disorders since the symptoms are similar. As of today, the reverse transcription-polymerase chain reaction (RT-PCR) test is utilized for diagnosing ...

Deep-Cloud: A Deep Neural Network-Based Approach for Analyzing Differentially Expressed Genes of RNA-seq Data.

Presently, the field of analyzing differentially expressed genes (DEGs) of RNA-seq data is still in its infancy, with new approaches constantly being proposed. Taking advantage of deep neural networks to explore gene expression information on RNA-seq data can provide a novel possibility in the biomedical field. In this study, a novel approach based on a deep learning algorithm and cloud model was ...

Rapid Discrimination of ST175 Isolates Involved in a Nosocomial Outbreak Using MALDI-TOF Mass Spectrometry and FTIR Spectroscopy Coupled with Machine Learning.

The goal of this study was to evaluate matrix-assisted laser desorption ionization-iime of flight mass spectrometry (MALDI-TOF MS) and Fourier-transfo...

A fusion framework of deep learning and machine learning for predicting sgRNA cleavage efficiency.

CRISPR/Cas9 system is a powerful tool for genome editing. Numerous studies have shown that sgRNAs can strongly affect the efficiency of editing. Howev...

RNA contact prediction by data efficient deep learning.

On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complem...

IChrom-Deep: An Attention-Based Deep Learning Model for Identifying Chromatin Interactions.

Identification of chromatin interactions is crucial for advancing our knowledge of gene regulation. However, due to the limitations of high-throughput...

Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches.

Accurate prediction of nucleic binding residues is essential for the understanding of transcription and translation processes. Integration of feature-...

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has dev...

Coot-Lion optimized deep learning algorithm for COVID-19 point mutation rate prediction using genome sequences.

In this study, a deep quantum neural network (DQNN) based on the Lion-based Coot algorithm (LBCA-based Deep QNN) is employed to predict COVID-19. Here...

Harnessing deep learning for population genetic inference.

In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the...

scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.

Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanis...

Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.

DNA methylation takes on critical significance to the regulation of gene expression by affecting the stability of DNA and changing the structure of ch...

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks.

Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced dr...

Automated prognosis of renal function decline in ADPKD patients using deep learning.

An accurate prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is crucial for early intervention. Current bio...

Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these su...

Predicting transcriptional outcomes of novel multigene perturbations with GEARS.

Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involve...

Cytogenetics and genomics in pediatric acute lymphoblastic leukaemia.

The last five decades have witnessed significant improvement in diagnostics, treatment and management of children with acute lymphoblastic leukaemia (...

A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks.

The organisation of the genome in nuclear space is an important frontier of biology. Chromosome conformation capture methods such as Hi-C and Micro-C ...

Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective.

Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an adva...

Predicting congenital renal tract malformation genes using machine learning.

Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic ...