Genetics

Establishing predictive machine learning models for drug responses in patient derived cell culture.

The concept of personalised medicine in cancer therapy is becoming increasingly important. There already exist drugs administered specifically for patients with tumours presenting well-defined genetic mutations. However, the field is still in its infancy, and personalised treatments are far from being standard of care. Personalised medicine is often associated with the utilisation of omics data. Y...

Integrated analysis of oral rinse-derived and plasma circulating tumour DNA for mutation profiling and outcome prediction with oral squamous cell carcinoma.

This study evaluates the potential of oral rinse-derived and plasma circulating tumour DNA (ctDNA) in HPV-negative oral squamous cell carcinoma (OSCC), where early recurrence occurs in a significant proportion of patients, contributing to poor prognosis. Analysis of paired tissue, oral rinse, and plasma samples from 123 patients revealed ctDNA detection rates of 94.3% in oral rinse and 80.5% in pl...

Development of a novel transcriptomic measure of aging: Transcriptomic Mortality-risk Age (TraMA).

Increasingly, research suggests that aging is a coordinated multi-system decline in functioning that occurs at multiple biological levels. We develope...

Neur-Ally: a deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disorders.

Large-scale quantitative studies have identified significant genetic associations for various neurological disorders. Expression quantitative trait lo...

Development and validation of an MRI spatiotemporal interaction model for early noninvasive prediction of neoadjuvant chemotherapy response in breast cancer: a multicentre study.

BACKGROUND: The accurate and early evaluation of response to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for optimizing treatment strat...

Generative prediction of causal gene sets responsible for complex traits.

The relationship between genotype and phenotype remains an outstanding question for organism-level traits because these traits are generally . The cha...

RNAS-sgRNA: Recurrent Neural Architecture Search for Detection of On-Target Effects in Single Guide RNA.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 is a leading genomic editing tool, but its effectiveness is limited by conside...

Rapid prediction of antibiotic resistance in complex using whole-genome and metagenomic sequencing.

Clinical management and surveillance of the complex (ECC) face significant challenges due to inaccurate species identification and prolonged turnarou...

Exploring the molecular mechanisms of lactylation-related biological functions and immune regulation in sepsis-associated acute kidney injury.

Lactylation, a novel post-translational modification, has been implicated in various pathophysiological processes; however, its role in sepsis-associa...

A Trust-Region Projection Neural Network for Nonlinear Programming.

The trust-region method and projection neural networks are two branches of optimization approaches with different operational principles and character...

Clinical decision tree for optimising endoscopic assessment of signet ring cell carcinoma in hereditary diffuse gastric cancer surveillance.

Background and study aims Prophylactic total gastrectomy (PTG) is the definitive treatment for hereditary diffuse gastric cancer syndrome (HDGC). Endo...

MPSE identifies newborns for whole genome sequencing within 48 h of NICU admission.

Identifying critically ill newborns who will benefit from whole genome sequencing (WGS) is difficult and time-consuming due to complex eligibility cri...

Incorporating automation in a cytogenetics laboratory: three practitioners' perspectives on benefits and limitations.

Automation has been developed and continues to be refined for cytogenetics, including advances in the processing of samples, in analysis of convention...

Toward a general framework for AI-enabled prediction in crop improvement.

A theoretical framework for AI and ensembled prediction for crop improvement is introduced and demonstrated using the logistic map. Symbolic/sub-symbo...

Integrative exome sequencing and machine learning identify MICB and interferon pathway genes as contributors to SSc risk.

OBJECTIVES: Systemic sclerosis (SSc) is a complex autoimmune disease with both known and unidentified genetic contributors. While genome-wide associat...

Multiscale-fusion models with genomic, topological and pathomic features to predict response to radiation therapy for non-small cell lung cancer patients.

Artificial intelligence models with biomarkers to predict treatment responses to radiation would be necessary to maximise the treatment outcomes of in...

Analysis and characterization of extrachromosomal circular DNA in prostate cancer: Potential biomarker discovery from urine, plasma, and tumor samples.

Extrachromosomal circular DNA (eccDNA) may contribute to genomic rearrangements and tumor heterogeneity, playing a role in cancer development and prog...

Integrated machine learning based on cuproptosis and RNA methylation regulators to explore the molecular model of prostate cancer and provide novel insights to immunotherapy.

As a highly prevalent tumor in males, prostate cancer (PCa) needs newly developed biomarkers to guide prognosis and treatment. However, few researche...

Integrative multi-omics profiling deciphers tumor microenvironment heterogeneity and immunotherapy vulnerabilities in lung neuroendocrine carcinomas.

INTRODUCTION: Lung neuroendocrine carcinomas (Lu-NECs) are rare, highly aggressive lung tumors with poor prognosis and limited therapeutic options. Un...

Split DNA Tetrahedron-Mediated Spatiotemporal-Hierarchy CRISPR Cascade Integrated with Au@Pt Nanolabels and Artificial Intelligence for a Cervical Cancer MicroRNA Bioassay.

The screening and monitoring of microRNAs as cancer molecular biomarkers is clinically significant, but traditional methods lack sufficient sensitivit...