Genetics

The ENGAGE study: evaluation of a conversational virtual agent that provides tailored pre-test genetic education to cancer patients.

PURPOSE: Novel approaches are needed to ensure all patients with cancer have access to quality genetic education before genetic testing to enable informed treatment decisions. The purpose of this study was to test the use of an artificial intelligence (AI) intervention for the delivery of genetic education by non-genetic providers to patients with cancer undergoing active treatment.

Screening and Identification of DNA Nanostructure Aptamer Using the SELEX Method for ‎Detection of Epsilon Toxin.

BACKGROUND: Epsilon toxin (ETX), produced by , is one of the most potent toxins known, with a lethal potency approaching that of botulinum neurotoxins. Epsilon toxin is responsible for enteritis. Therefore, the development of rapid and simple methods to detect ETX is imperative. Aptamers are single-stranded oligonucleotides that can bind tightly to specific target molecules with an affinity compar...

Generating bulk RNA-Seq gene expression data based on generative deep learning models and utilizing it for data augmentation.

Large-scale high-throughput transcriptome sequencing data holds significant value in biomedical research. However, practical challenges such as diffic...

Multiple generation distinct toxicant exposures induce epigenetic transgenerational inheritance of enhanced pathology and obesity.

Three successive multiple generations of rats were exposed to different toxicants and then bred to the transgenerational F5 generation to assess the i...

Fungal and bacterial gut microbiota differ between colonization and infection.

The bacterial microbiota is well-recognized for its role in colonization and infection, while fungi and yeasts remain understudied. The aim of this ...

Toward three-dimensional DNA industrial nanorobots.

Nanoscale industrial robots have potential as manufacturing platforms and are capable of automatically performing repetitive tasks to handle and produ...

Deep learning-enabled breast cancer endocrine response determination from H&E staining based on ESR1 signaling activity.

Estrogen receptor (ER) positivity by immunohistochemistry has long been a main selection criterium for breast cancer patients to be treated with endoc...

The changing face of circulating tumor DNA (ctDNA) profiling: Factors that shape the landscape of methodologies, technologies, and commercialization.

Liquid biopsies, in particular the profiling of circulating tumor DNA (ctDNA), have long held promise as transformative tools in cancer precision medi...

GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data.

Carbohydrate sequencing is a formidable task identified as a strategic goal in modern biochemistry. It relies on identifying a large number of isomers...

Self-supervised Learning for DNA sequences with circular dilated convolutional networks.

DNA molecules commonly exhibit wide interactions between the nucleobases. Modeling the interactions is important for obtaining accurate sequence-based...

m5UMCB: Prediction of RNA 5-methyluridine sites using multi-scale convolutional neural network with BiLSTM.

As a prevalent RNA modification, 5-methyluridine (mU) plays a critical role in diverse biological processes and disease pathogenesis. High-throughput ...

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks.

We present the Fast Greedy Equivalence Search (FGES)-Merge, a new method for learning the structure of gene regulatory networks via merging locally le...

Validation of a Bacteriophage Hide Application to Reduce STEC in the Lairage Area of Commercial Beef Cattle Operations.

Finalyse, a T4 bacteriophage, is a pre-harvest intervention that utilizes a combination of bacteriophages to reduce incoming O157:H7 prevalence by de...

Strengthening the Diagnosis of Drug-Resistant Tuberculosis Using NGS-Based Approaches and Bioinformatics Pipelines for Data Analysis in India.

In India, drug-resistant tuberculosis (DR-TB) is a major public health issue and a significant challenge to stop TB program. An estimated 27% of new T...

Reconstructing growth and dynamic trajectories from single-cell transcriptomics data.

Time-series single-cell RNA sequencing (scRNA-seq) datasets provide unprecedented opportunities to learn dynamic processes of cellular systems. Due to...

Stauntonia chinensis injection relieves neuropathic pain by increasing the expression of PSD-95 and reducing the proliferation of phagocytic microglia.

Neuroinflammation induced by engulfment of synapses by phagocytic microglia plays a crucial role in neuropathic pain. Stauntonia chinensis is extracte...

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation.

Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, a...

Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition.

Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-st...

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks, including the prediction of gene expression ...

Personal transcriptome variation is poorly explained by current genomic deep learning models.

Genomic deep learning models can predict genome-wide epigenetic features and gene expression levels directly from DNA sequence. While current models p...