Genetics

Donor-specific digital twin for living donor liver transplant recovery.

The remarkable capacity of the liver to regenerate its lost mass after resection makes living donor liver transplantation a successful treatment option. However, donor heterogeneity significantly influences recovery trajectories, highlighting the need for individualized monitoring. With the rising incidence of liver diseases, safer transplant procedures and improved donor care are urgently needed....

Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma.

INTRODUCTION: Increasing evidence highlights the pivotal role of RNA methylation and miRNAs in hepatocellular carcinoma (HCC). However, the risk associated with RNA methylation-related miRNAs (RMRMs) in the HCC immune microenvironment remains largely unknown. Here, we predicted the correlation between RMRM risk and immune cell infiltration in HCC using machine learning.

Pyroptosis-Related Gene Signatures Enable Robustly Diagnosis, Prognosis and Immune Responses Prediction in Uterine Corpus Endometrial Carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignancy with poor prognosis and high lethality rates. Pyroptosis, a pro-inflammator...

Leveraging molecular-QTL co-association to predict novel disease-associated genetic loci using a graph convolutional neural network.

Genome-wide association studies (GWAS) have successfully uncovered numerous associations between genetic variants and disease traits to date. Yet, ide...

A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information.

Understanding the behavior of microbial consortia is crucial for predicting metabolite production by microorganisms. Genome-scale network reconstructi...

Advanced multi-objective trajectory planning for robotic arms using a multi-strategy enhanced NSGA-II algorithm.

Facing the problems of large-scale rapid and disorderly loading, the robotic arm has the problems of large start-stop impact, easy to shake, and reduc...

Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights.

BACKGROUND: Acute Myeloid Leukemia (AML) exhibits significant heterogeneity in clinical outcomes, yet current prognostic stratification systems based ...

Identification of hub gene for the pathogenic mechanism and diagnosis of MASLD by enhanced bioinformatics analysis and machine learning.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a heterogeneous disease caused by multiple etiologies. It is characterized by exce...

Leveraging transformers and explainable AI for Alzheimer's disease interpretability.

Alzheimer's disease (AD) is a progressive brain ailment that causes memory loss, cognitive decline, and behavioral changes. It is quite concerning tha...

Hybrid deep learning model for accurate and efficient android malware detection using DBN-GRU.

The rapid growth of Android applications has led to an increase in security threats, while traditional detection methods struggle to combat advanced m...

Adaptive mechanism-based grey wolf optimizer for feature selection in high-dimensional classification.

Feature Selection (FS) is a crucial component of machine learning and data mining. Its goal is to eliminate redundant and irrelevant features from a d...

iProtDNA-SMOTE: Enhancing protein-DNA binding sites prediction through imbalanced graph neural networks.

Protein-DNA interactions play a crucial role in cellular biology, essential for maintaining life processes and regulating cellular functions. We propo...

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. Thi...

Machine and Deep Learning Methods for Predicting 3D Genome Organization.

Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B...

Machine Learning for RNA Design: LEARNA.

Machine learning algorithms, and in particular deep learning approaches, have recently garnered attention in the field of molecular biology due to rem...

gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.

Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a v...

Generative Modeling of RNA Sequence Families with Restricted Boltzmann Machines.

In this chapter, we discuss the potential application of Restricted Boltzmann machines (RBM) to model sequence families of structured RNA molecules. R...

The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.

Nucleic acid tests (NATs) are considered as gold standard in molecular diagnosis. To meet the demand for onsite, point-of-care, specific and sensitive...

Measuring Cell Dimensions in Fission Yeast Using Machine Learning.

In fission yeast (Schizosaccharomyces pombe), cell length is a crucial indicator of cell cycle progression. Microscopy screens that examine the effect...

Understanding TCR T cell knockout behavior using interpretable machine learning.

Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T cell performance to create more powerful cancer imm...