Genetics

Separable integral neural networks.

Integral neural networks adopt continuous integral operators instead of conventional discrete convolutional operations to perform deep learning tasks. As this integral operator is the continuous representation of the regular convolutional operation, it is not suitable for representing the separable convolutional operations widely deployed on mobile devices. To address this issue, a separable integ...

EfficientNet-resDDSC: A Hybrid Deep Learning Model Integrating Residual Blocks and Dilated Convolutions for Inferring Gene Causality in Single-Cell Data.

Gene Regulatory Networks (GRNs) reveal complex interactions between genes in organisms, crucial for understanding the life system's operation. The rapid development of biotechnology, especially single-cell RNA sequencing (scRNA-seq), has generated a large amount of scRNA-seq data, which can be analyzed to explore the regulatory relationships between genes at the single-cell level. Previous models ...

Unsupervised Learning-Assisted Acoustic-Driven Nano-Lens Holography for the Ultrasensitive and Amplification-Free Detection of Viable Bacteria.

Bacterial infection is a crucial factor resulting in public health issues worldwide, often triggering epidemics and even fatalities. The accurate, rap...

Identification of Taihang-chicken-specific genetic markers using genome-wide SNPs and machine learning: BREED-SPECIFIC SNPS OF TAIHANG CHICKEN.

Taihang is an indigenous breed in Hebei Province and has a long history of evolution. To uncover the genetic basis and protect the genetic resources, ...

Using Machine Learning Models to Predict Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

PURPOSE: Neoadjuvant chemotherapy (NAC) is increasingly used in breast cancer. Predictive modeling is useful in predicting pathologic complete respons...

Oral health-related quality of life in Northland Māori children and adolescents with Polynesian amelogenesis imperfecta.

INTRODUCTION: Amelogenesis Imperfecta (AI) is a hereditary developmental disorder of tooth enamel with few known variants with differing characteristi...

Accurate RNA 3D structure prediction using a language model-based deep learning approach.

Accurate prediction of RNA three-dimensional (3D) structures remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding...

Leveraging Bioinformatics and Machine Learning for Identifying Prognostic Biomarkers and Predicting Clinical Outcomes in Lung Adenocarcinoma.

There exist significant challenges for lung adenocarcinoma (LUAD) due to its poor prognosis and limited treatment options, particularly in the advanc...

A multi-perspective deep learning framework for enhancer characterization and identification.

Enhancers are vital elements in the genome that boost the transcriptional activity of neighboring genes and are essential in regulating cell-specific ...

Applications of artificial intelligence to inherited retinal diseases: A systematic review.

Artificial intelligence(AI)-based methods have been extensively used for the detection and management of various common retinal conditions, but their ...

Deciphering Cas9 specificity: Role of domain dynamics and RNA:DNA hybrid interactions revealed through machine learning and accelerated molecular simulations.

CRISPR/Cas9 technology is widely used for gene editing, but off-targeting still remains a major concern in therapeutic applications. Although Cas9 var...

Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration.

BACKGROUND: RNA 5-methyluridine (m5U) modifications play a crucial role in biological processes, making their accurate identification a key focus in c...

Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning.

Leukemia is highly heterogeneous, meaning that different types of leukemia require different treatments and have different prognoses. Current clinical...

Magnetic soft microrobots for erectile dysfunction therapy.

Erectile dysfunction (ED) is a major threat to male fertility and quality of life, and mesenchymal stromal cells (MSCs) are a promising therapeutic op...

Analysis of public perceptions on the use of artificial intelligence in genomic medicine.

PURPOSE: Next generation sequencing has led to the creation of large pools of genomic data with analysis rather than data generation now the limiting ...

SDC4 protein action and related key genes in nonhealing diabetic foot ulcers based on bioinformatics analysis and machine learning.

Diabetic foot ulcers (DFU) is a complication associated with diabetes characterised by high morbidity, disability, and mortality, involving chronic in...

Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments.

Calcium oxalate (CaOx) nephrolithiasis constitutes approximately 75% of nephrolithiasis cases, resulting from the supersaturation and deposition of Ca...

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.

Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains ...

Response prediction for neoadjuvant treatment in locally advanced rectal cancer patients-improvement in decision-making: A systematic review.

BACKGROUND: Predicting pathological complete response (pCR) from pre or post-treatment features could be significant in improving the process of makin...

MuSE: A deep learning model based on multi-feature fusion for super-enhancer prediction.

Although bioinformatics-based methods accurately identify SEs (Super-enhancers), the results depend on feature design. It is foundational to represent...