Genetics

Predicting the Efficacy of Breast Cancer Neoadjuvant Chemotherapy Using Ultrasonography and Machine Learning.

OBJECTIVE: This study aimed to develop a machine learning model based on ultrasonography (US) and clinicopathological features to predict pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) in patients with breast cancer. The goal was to establish a non-invasive prediction tool to facilitate individualized treatment planning. METHODS: A retrospective analysis was conducte...

The role of low-complexity repeats in RNA-RNA interactions and a deep learning framework for duplex prediction.

RNA-RNA interactions (RRIs) are fundamental to gene regulation and RNA processing, yet their molecular determinants remain unclear. In this work, we analyze several large-scale RRI datasets and identify low-complexity repeats (LCRs), including simple tandem repeats, as key drivers of RRIs. Our findings reveal that LCRs enable thermodynamically stable interactions with multiple partners, positionin...

Coriandrum sativum improves prognosis in clear cell renal cell carcinoma by targeting NEK6 to modulate the immune microenvironment: a predictive study based on network pharmacology and multi-omics analysis.

Coriandrum sativum L. (coriander) is a medicinal herb with diverse pharmacological properties, but its molecular mechanism in clear cell renal cell ca...

Spatial transcriptional profiling of CHB liver biopsies reveals an undetected population of zonally biased HBV-integrated cells.

BACKGROUND & AIMS: HBV covalently closed circular DNA (cccDNA) and HBV-integrated DNA (iDNA) are features of chronic HBV (CHB). Elimination of both ce...

scACAN: An Adaptive Learning Framework Aggregating Local Graph Structure Context for Rare Cell Type Identification.

Single-cell RNA sequencing (scRNA-seq) technology has become an essential tool for dissecting cellular heterogeneity and elucidating complex biologica...

Radiogenomics: Current Understandings and Future Perspectives.

Radiogenomics is a rapidly developing field that links radiological image features (radiomics) to genomic-level data (genomics, transcriptomics, and e...

Cancer-Like Fragmentomic Characteristics of Somatic Variants in Cell-Free DNA.

Cell-free DNA (cfDNA) in plasma consists of short DNA fragments resulting from a non-random fragmentation process, with distinct fragmentomic characte...

Extracellular matrix stiffness drives post-mitotic nuclear pore complex assembly to promote neuroblastoma pathogenesis.

Nuclear pore complex (NPC) undergoes dynamic changes in physiology and pathology, yet its roles in neuroblastoma (NB) remain unclear. We demonstrated ...

The Future of Non-muscle-invasive Bladder Cancer: Towards a Molecular-Digital Paradigm for Personalized Management.

The management of non-muscle-invasive bladder cancer (NMIBC) is undergoing a major paradigm shift driven by molecular biomarkers, artificial intellige...

Automatic cell type identification methods for single-cell RNA sequencing based on coordinate convolutional neural network.

Cell type identification is a fundamental step in the analysis of single-cell RNA sequencing (scRNA-seq) data. Among the various classification tools,...

Recent advances in the analysis of the evolutionary RNA based therapeutics; a review.

The evolution of RNA-based therapies is bringing in a new era for the pharmaceutical industry. As a result of their crucial role in regulation of gene...

DeepDBPI: DNA-Binding Protein Identifier Using a Deep Learning Model with Transformed Denoised Features.

Motivation: DNA-binding proteins (DBPs) play a significant role in the entire biological system. Many DNA-related studies actively investigate to unde...

Machine learning-based meat freshness evaluation: principle, pipeline and application.

Ensuring the freshness of meat is crucial for food safety and consumer trust. Traditional methods for evaluating meat freshness, such as sensory analy...

Community participatory Jingchu folk pattern generation platform construction and user co-creation mechanism analysis.

In the context of the digital era, the protection and inheritance of traditional cultural heritage face unprecedented opportunities and challenges. Th...

Protein buffering of aneuploidy is driven by coordinated factors identified through machine learning.

Aneuploidy, a hallmark of cancer, alters chromosome copy numbers and with that the abundance of hundreds of proteins. Evidence suggests that levels of...

Integrative multi-omics and machine learning framework identifies PRDX4 as a redox-EMT regulator and predictive marker in bone-metastatic breast cancer.

Bone metastasis is a major cause of morbidity and mortality in breast cancer, yet effective prognostic models and targeted therapies remain limited. H...

Machine learning for individual epigenetic fingerprints as predictors of well-being in young adults.

A screening strategy based on machine learning for diagnostic biomarkers in small cell lung cancer.

Small cell lung cancer (SCLC) is the most aggressive subtype with high mortality rates due to the lack of specific diagnostic biomarkers to delay the ...

The role of the kynurenine pathway in the pathophysiology of autism-like phenotype induced by maternal inflammation in male mice.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with core symptoms that may include deficits in communication, social challenges, and ...

Integrative analysis identifies lactylation-related biomarkers in sepsis via bioinformatics and machine learning.

BACKGROUND: Protein lactylation is a novel post-translational modification driven by lactate and has emerged as a critical link between cellular metab...