Genetics

Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

Morphologic interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features, but the complexity of morphologic and genetic changes makes clear associations challenging. This article interrogates novel clinical subtypes of MDS...

Artificial Intelligence and Hypertension: Recent Advances and Future Outlook.

Prevention and treatment of hypertension (HTN) are a challenging public health problem. Recent evidence suggests that artificial intelligence (AI) has potential to be a promising tool for reducing the global burden of HTN, and furthering precision medicine related to cardiovascular (CV) diseases including HTN. Since AI can stimulate human thought processes and learning with complex algorithms and ...

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.

BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health...

Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver.

PURPOSE: Volumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess ...

Precision medicine and artificial intelligence: overview and relevance to reproductive medicine.

Traditionally, new treatments have been developed for the population at large. Recently, large-scale genomic sequencing analyses have revealed tremend...

Deep learning applications to combat the dissemination of COVID-19 disease: a review.

Recent Coronavirus (COVID-19) is one of the respiratory diseases, and it is known as fast infectious ability. This dissemination can be decelerated by...

Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.

BACKGROUND: Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very info...

Gene set analysis methods for the functional interpretation of non-mRNA data-Genomic range and ncRNA data.

Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to a...

Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation.

Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and patho...

A Machine Learning Approach for the Automated Interpretation of Plasma Amino Acid Profiles.

BACKGROUND: Plasma amino acid (PAA) profiles are used in routine clinical practice for the diagnosis and monitoring of inherited disorders of amino ac...

TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors.

The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effecti...

Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: An AI-based approach.

The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which p...

Anti-inflammatory and antibacterial activities of P. petiolosa (Christ) Ching ethyl acetate extract against S. aureus in mice.

P. petiolosa as a typical Chinese herbal medicine has been generally utilized as Chinese native medicine formulation for treatment of chronic bronchit...

Antimicrobial and synergistic activity of thiazoline derivatives in combination with conventional antibiotics against multidrug resistant Staphylococcus aureus isolated from abscess drainage samples.

Emergence and spread of multidrug resistant (MDR) Staphylococcus aureus strains is becoming major challenge in treatment of soft tissue infections. Th...

Development a hydrolysis probe-based quantitative PCR assay for the specific detection and quantification of .

BACKGROUND AND PURPOSE: is an emerging multidrug-resistant pathogen. The identification of this species with the conventional phenotypic or biochemic...

AI on a chip.

Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported b...

[Synergistic drug combination prediction in multi-input neural network].

Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanis...

Study on the differentially expressed genes and signaling pathways in dermatomyositis using integrated bioinformatics method.

Dermatomyositis is a common connective tissue disease. The occurrence and development of dermatomyositis is a result of multiple factors, but its exac...

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status ...

Prediction of immune checkpoint inhibition with immune oncology-related gene expression in gastrointestinal cancer using a machine learning classifier.

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of gastrointestinal cancer. However, biomarkers correlated with the ...