Genetics

Machine learning selection of basement membrane-associated genes and development of a predictive model for kidney fibrosis.

This study investigates the role of basement membrane-related genes in kidney fibrosis, a significant factor in the progression of chronic kidney disease that can lead to end-stage renal failure. The authors aim to develop a predictive model using machine learning techniques due to the limitations of existing diagnostic methods, which often lack sensitivity and specificity. Utilizing gene expressi...

Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications.

BACKGROUND: Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility....

Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.

Protein sequence design is a highly challenging task, aimed at discovering new proteins that are more functional and producible under laboratory condi...

Spatial recognition and semi-quantification of epigenetic events in pancreatic cancer subtypes with multiplexed molecular imaging and machine learning.

Genomic alterations are the driving force behind pancreatic cancer (PC) tumorigenesis, but they do not fully account for its diverse phenotypes. Inves...

Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell techno...

Genetic association studies using disease liabilities from deep neural networks.

The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort st...

RNA structure prediction using deep learning - A comprehensive review.

In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of RNA functions an...

Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics.

We have recently developed a machine learning classifier that enables fast, accurate, and affordable classification of brain tumors based on genome-wi...

Coal and gas outburst prediction based on data augmentation and neuroevolution.

Coal and gas outburst (CGO) is a complicated natural disaster in underground coal mine production. In constructing smart mines, predicting CGO risks e...

Architecture Knowledge Distillation for Evolutionary Generative Adversarial Network.

Generative Adversarial Networks (GANs) are effective for image generation, but their unstable training limits broader applications. Additionally, neur...

Unveiling the potential of Brachiaria ruziziensis: Comparative analysis of multivariate and machine learning models for biomass and NPK prediction using Vis-NIR-SWIR spectroscopy.

This study investigated the development and validation of predictive models for estimating foliar nitrogen (N), phosphorus (P), and potassium (K) cont...

Integration of 101 machine learning algorithm combinations to unveil m6A/m1A/m5C/m7G-associated prognostic signature in colorectal cancer.

Colorectal cancer (CRC) is the most common malignancy in the digestive system, with a lower 5-year overall survival rate. There is increasing evidence...

Predicting cell properties with AI from 3D imaging flow cytometer data.

Predicting the properties of tissues or organisms from the genomics data is widely accepted by the medical community. Here we ask a question: can we p...

Realizing the promise of machine learning in precision oncology: expert perspectives on opportunities and challenges.

BACKGROUND: The ability of machine learning (ML) to process and learn from large quantities of heterogeneous patient data is gaining attention in the ...

Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation.

BACKGROUND: The changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue i...

Key RNA-binding proteins in renal fibrosis: a comprehensive bioinformatics and machine learning framework for diagnostic and therapeutic insights.

BACKGROUND: Renal fibrosis is a critical factor in chronic kidney disease progression, with limited diagnostic and therapeutic options. Emerging evide...

RNA-protein interaction prediction using network-guided deep learning.

Accurate computational determination of RNA-protein interactions remains challenging, particularly when encountering unknown RNAs and proteins. The li...

Breaking barriers: noninvasive AI model for BRAF mutation identification.

OBJECTIVE: BRAF is the most common mutation found in thyroid cancer and is particularly associated with papillary thyroid carcinoma (PTC). Currently, ...

Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models.

RNAs are emerging as promising therapeutic targets, yet identifying small molecules that bind to them remains a significant challenge in drug discover...

Cloning, Characterization, and Computer-Aided Evolution of a Thermostable Laccase of the DUF152 Family From Klebsiella michiganensis.

Bacterial laccases exhibit relatively high optimal reaction temperatures and possess a broad substrate spectrum, thereby expanding the range of potent...