Genetics

Cloning Strategies for the Generation of Recombinant Capripoxvirus Through the Use of Screening and Selection Markers.

The ability to manipulate capripoxvirus through gene knockouts and gene insertions has become an increasingly valuable research tool in elucidating the function of individual genes of capripoxvirus, as well as in the development of capripoxvirus-based recombinant vaccines. The homologous recombination technique is commonly used to generate capripoxvirus knockout viruses (KO), and is based on the t...

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis.

BACKGROUND: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) . Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation...

[Application of Artificial Intelligence in Clinical Genomics].

Clinical genomics mainly studies the clinical application of genomics in diagnosis,treatment decision,and prognosis prediction.Artificial intelligence...

[Research status and prospect of artificial intelligence technology in the diagnosis of urinary system tumors].

With the rapid development of artificial intelligence technology, researchers have applied it to the diagnosis of various tumors in the urinary system...

Clinical insights into hematologic malignancies and comparative analysis of molecular signatures of acute myeloid leukemia in different ethnicities using an artificial intelligence offering.

Next generation sequencing generates copious amounts of genomics data, causing manual interpretation to be laborious and non-scalable while remaining ...

Molecular-based precision oncology clinical decision making augmented by artificial intelligence.

The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic seq...

CoCoNet-boosting RNA contact prediction by convolutional neural networks.

Co-evolutionary models such as direct coupling analysis (DCA) in combination with machine learning (ML) techniques based on deep neural networks are a...

iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization.

MOTIVATION: DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, identifica...

Representation learning of RNA velocity reveals robust cell transitions.

RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular...

scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network.

Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous classification of thousands of cells in a single assay based on tra...

Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples.

Osteoporosis is a progressive bone disease in the elderly and lacks an effective classification method of patients. This study constructed a gene sign...

Vitamin D and COVID-19 - Let's Explore the Relationship!

Vitamin D plays a protective role against COVID-19. Patients with deficiency of vitamin D are more prone to severe SARS-CoV-2 infections. It is known ...

Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach.

BACKGROUND: Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric...

Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia.

Although genomic alterations drive the pathogenesis of acute myeloid leukemia (AML), traditional classifications are largely based on morphology, and ...

Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning.

The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and condit...

Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. T...

A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains.

Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased....

An artificial neural network model based on DNA damage response genes to predict outcomes of lower-grade glioma patients.

Although the prognosis of lower-grade glioma (LGG) patients is better than others, outcomes are highly heterogeneous. Isocitrate dehydrogenase (IDH) m...

Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although genome-wide association studies (GWAS) identify the ...

Human host status inference from temporal microbiome changes via recurrent neural networks.

With the rapid increase in sequencing data, human host status inference (e.g. healthy or sick) from microbiome data has become an important issue. Exi...