Latest AI and machine learning research in genetics for healthcare professionals.
The integration of bioinformatics predictions and experimental validation plays a pivotal role in advancing biological research, from understanding molecular mechanisms to developing therapeutic strategies. Bioinformatics tools and methods offer powerful means for predicting gene functions, protein interactions, and regulatory networks, but these predictions must be validated through experimenta...
Decomposing a flow on a Directed Acyclic Graph (DAG) into a weighted sum of a small number of paths is an essential task in operations research and bioinformatics. This problem, referred to as Sparse Flow Decomposition (SFD), has gained significant interest, in particular for its application in RNA transcript multi-assembly, the identification of the multiple transcripts corresponding to a given...
Normalization is a critical step in quantitative analyses of biological processes. Recent works show that cross-platform integration and normalizati...
A mutation in the DNA of a single cell that compromises its function initiates leukemia,leading to the overproduction of immature white blood cells ...
RNA sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases that hinder the comprehensive detection and quantificati...
Since the completion of the human genome sequencing project in 2001, significant progress has been made in areas such as gene regulation editing and...
Cancer diagnosis and prognosis primarily depend on clinical parameters such as age and tumor grade, and are increasingly complemented by molecular d...
In commonly used sub-quadratic complexity modules, linear attention benefits from simplicity and high parallelism, making it promising for image syn...
Convolutional neural networks (CNNs) evaluate short-range correlations in input images which progress along the layers, whereas vision transformer (...
This paper describes Meta's ACH system for mutation-guided LLM-based test generation. ACH generates relatively few mutants (aka simulated faults), c...
Principal component analysis (PCA) is routinely used in population genetics to assess genetic structure. With chromosomal reference genomes and popu...
Analysis of single-cell RNA sequencing data is often conducted through network projections such as coexpression networks, primarily due to the abund...
Pre-trained large models attract widespread attention in recent years, but they face challenges in applications that require high interpretability o...
This study presents a dynamic Quantum-Inspired Genetic Algorithm (D-QIGA) for feature selection, leveraging quantum principles like superposition an...
OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learni...
Reverse engineering tools are required to handle the complexity of software products and the unique requirements of many different tasks, like softw...
Oncolytic viral therapy (OVT) is an emerging precision therapy for aggressive and recurrent cancers. However, its clinical efficacy is hindered by t...
Accurate molecular quantification is essential for advancing research and diagnostics in fields such as infectious diseases, cancer biology, and gen...
Neuroblastoma, is a highly heterogeneous pediatric tumour and is responsible for 15% of pediatric cancer-related deaths. The clinical outcomes can v...
Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock...