AI Medical Compendium Journal:
International journal of molecular sciences

Showing 61 to 70 of 423 articles

AEmiGAP: AutoEncoder-Based miRNA-Gene Association Prediction Using Deep Learning Method.

International journal of molecular sciences
MicroRNAs (miRNAs) play a crucial role in gene regulation and are strongly linked to various diseases, including cancer. This study presents AEmiGAP, an advanced deep learning model that integrates autoencoders with long short-term memory (LSTM) netw...

Uncertainty Quantification and Flagging of Unreliable Predictions in Predicting Mass Spectrometry-Related Properties of Small Molecules Using Machine Learning.

International journal of molecular sciences
Mass spectral identification (in particular, in metabolomics) can be refined by comparing the observed and predicted properties of molecules, such as chromatographic retention. Significant advancements have been made in predicting these values using ...

Precision Thermostability Predictions: Leveraging Machine Learning for Examining Laccases and Their Associated Genes.

International journal of molecular sciences
Laccases, multi-copper oxidases, play pivotal roles in the oxidation of a variety of substrates, impacting numerous biological functions and industrial processes. However, their industrial adoption has been limited by challenges in thermostability. T...

Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status.

International journal of molecular sciences
Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently. Breast cancer subtyping based on estrogen receptor (ER) status is crucial for determining prognosis...

RNA Editing Signatures Powered by Artificial Intelligence: A New Frontier in Differentiating Schizophrenia, Bipolar, and Schizoaffective Disorders.

International journal of molecular sciences
Mental health disorders are devastating illnesses, often misdiagnosed due to overlapping clinical symptoms. Among these conditions, bipolar disorder, schizophrenia, and schizoaffective disorder are particularly difficult to distinguish, as they share...

DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification.

International journal of molecular sciences
Enhancers are short genomic segments located in non-coding regions of the genome that play a critical role in regulating the expression of target genes. Despite their importance in transcriptional regulation, effective methods for classifying enhance...

Integrating Metabolomics Domain Knowledge with Explainable Machine Learning in Atherosclerotic Cardiovascular Disease Classification.

International journal of molecular sciences
Metabolomic data often present challenges due to high dimensionality, collinearity, and variability in metabolite concentrations. Machine learning (ML) application in metabolomic analyses is enabling the extraction of meaningful information from comp...

Special Issue "Machine Learning and Bioinformatics in Human Health and Disease"-Chances and Challenges.

International journal of molecular sciences
Machine learning (ML) and bioinformatics are catalyzing a new era in biomedical research, enabling unprecedented insights into the complex systems that govern human health and disease [...].

Machine Learning Integration with Single-Cell Transcriptome Sequencing Datasets Reveals the Impact of Tumor-Associated Neutrophils on the Immune Microenvironment and Immunotherapy Outcomes in Gastric Cancer.

International journal of molecular sciences
The characteristics of neutrophils play a crucial role in defining the tumor inflammatory environment. However, the function of tumor-associated neutrophils (TANs) in tumor immunity and their response to immune checkpoint inhibitors (ICIs) remains in...

Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression.

International journal of molecular sciences
In oncology, there is a critical need for robust biomarkers that can be easily translated into the clinic. We introduce a novel approach using paired differential gene expression analysis for biological feature selection in machine learning models, e...