Genetics

Efficacy and Safety of a Medical Robot for Non-Face-to-Face Nasopharyngeal Swab Specimen Collection: Nonclinical and Clinical Trial Findings for COVID-19 Testing.

ObjectivesTo meet the high demand for polymerase chain reaction (PCR) tests to diagnose COVID-19 and rapidly control the outbreak, an efficient and safe molecular diagnostic protocol is necessary. In this study, we evaluated the efficacy and safety of the medical robot developed for non-face-to-face nasopharyngeal swab specimen collection.MethodsIn a nonclinical study, an otorhinolaryngologist col...

Advanced machine learning-driven characterization of new natural cellulosic Lablab purpureus fibers through PCA and K-means clustering techniques.

The increasing demand for sustainable and eco-friendly materials has spurred significant interest in natural fibers as alternatives to synthetic reinforcements in composite applications. This study aims to explore the potential of Lablab purpureus fibers (LPFs) as sustainable materials by employing advanced characterization techniques and machine learning-driven analysis. Chemical analysis identif...

Integrating Artificial Intelligence and Bioinformatics Methods to Identify Disruptive STAT1 Variants Impacting Protein Stability and Function.

The Signal Transducer and Activator of Transcription 1 () gene is an essential component of the JAK-STAT signaling pathway. This pathway plays a pivo...

Psychiatric Genomics 2025: State of the Art and the Path Forward.

Psychiatric genetics has evolved from candidate-gene studies to whole-genome sequencing efforts. With hundreds of disease-associated loci now identifi...

New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning.

With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene...

TransRM: Weakly supervised learning of translation-enhancing N6-methyladenosine (mA) in circular RNAs.

As our understanding of Circular RNAs (circRNAs) continues to expand, accumulating evidence has demonstrated that circRNAs can interact with microRNAs...

Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.

The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discov...

Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.

OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model based on longitudinal ultrasound data and clinical ...

Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.

Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze ...

A compendium of human gene functions derived from evolutionary modelling.

A comprehensive, computable representation of the functional repertoire of all macromolecules encoded within the human genome is a foundational resour...

Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos.

The oviduct is the site of fertilization and preimplantation embryo development in mammals. Evidence suggests that gametes alter oviductal gene expres...

Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics.

In eukaryotic cells, DNA replication is organised both spatially and temporally, as evidenced by the stage-specific spatial distribution of replicatio...

AI in Breast Cancer Imaging: An Update and Future Trends.

Breast cancer is one of the most common types of cancer affecting women worldwide. Artificial intelligence (AI) is transforming breast cancer imaging ...

Engineering a New Generation of Gene Editors: Integrating Synthetic Biology and AI Innovations.

CRISPR-Cas technology has revolutionized biology by enabling precise DNA and RNA edits with ease. However, significant challenges remain for translati...

Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies.

Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to ...

Label-Free Exosomal SERS Detection Assisted by Machine Learning for Accurately Discriminating Cell Cycle Stages and Revealing the Molecular Mechanisms during the Mitotic Process.

Cell cycle analysis is crucial for disease diagnosis and treatment, especially for investigating cell heterogeneity and regulating cell behaviors. Exo...

Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.

Invasive diagnostic techniques, while offering critical insights into disease pathophysiology, are often limited by high costs, procedural risks, and ...

Pharmacogenomics and response to lithium in bipolar disorder.

AIMS: The present review explores the existing evidence on pharmacogenomic tests for prediction of lithium response in the treatment of bipolar disord...

A novel coarsened graph learning method for scalable single-cell data analysis.

The emergence of single-cell technologies, including flow and mass cytometry, as well as single-cell RNA sequencing, has revolutionized the study of c...

Identification of Biomarkers for Response to Interferon in Chronic Hepatitis B Based on Bioinformatics Analysis and Machine Learning.

Interferon (IFN) is a pivotal agent against hepatitis B virus (HBV) in clinic, but there is a lack of accurate biomarkers to predict the response to I...