Genetics

Artificial intelligence and metagenomics in intestinal diseases.

Gut microbiota has been shown to associate with the development of gastrointestinal diseases. In the last decade, development in whole metagenome sequencing and 16S rRNA sequencing technology has dramatically accelerated the gut microbiome's research and revealed its association with gastrointestinal disorders. Because of high dimensionality and complexity's intrinsic data characteristics, traditi...

Dual-responsive biohybrid neutrobots for active target delivery.

Swimming biohybrid microsized robots (e.g., bacteria- or sperm-driven microrobots) with self-propelling and navigating capabilities have become an exciting field of research, thanks to their controllable locomotion in hard-to-reach areas of the body for noninvasive drug delivery and treatment. However, current cell-based microrobots are susceptible to immune attack and clearance upon entering the ...

DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction.

The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess th...

Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework.

Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many type...

A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data.

Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide va...

Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results with Routine Blood Tests.

OBJECTIVE: The diagnosis of COVID-19 is based on the detection of SARS-CoV-2 in respiratory secretions, blood, or stool. Currently, reverse transcript...

Integration and transfer learning of single-cell transcriptomes via cFIT.

Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets have been generated that allow for the full transcriptional charac...

Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation.

Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently m...

Prediction of bio-sequence modifications and the associations with diseases.

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identi...

A machine-learning approach to map landscape connectivity in with genetic and environmental data.

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constra...

Categorization of birth weight phenotypes for inclusion in genetic evaluations using a deep neural network.

Birth weight (BW) serves as a valuable indicator of the economically relevant trait of calving ease (CE), and erroneous data collection for BW could i...

CRISPRidentify: identification of CRISPR arrays using machine learning approach.

CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR-C...

Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.

BACKGROUND: Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperativ...

[Banded chromosome images recognition based on dense convolutional network with segmental recalibration].

Human chromosomes karyotyping is an important means to diagnose genetic diseases. Chromosome image type recognition is a key step in the karyotyping p...

A machine learning-based framework for modeling transcription elongation.

RNA polymerase II (Pol II) generally pauses at certain positions along gene bodies, thereby interrupting the transcription elongation process, which i...

Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay.

The interpretation of radiation dose is an important procedure for both radiological operators and persons who are exposed to background or artificial...

Deep learning for nanopore ionic current blockades.

DNA molecules can electrophoretically be driven through a nanoscale opening in a material, giving rise to rich and measurable ionic current blockades....

Prediction of Alzheimer's disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening.

Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover ...

On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

A review, recently published in this journal by Fang (2019), showed that methods trained for the prediction of protein stability changes upon mutation...

Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing.

The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficienc...