AIMC Topic: Machine Learning

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Comprehensive statistical and machine learning framework for identification of metabolomic biomarkers in breast cancer.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Breast cancer is the most common cancer among women, with its burden increasing over the past decades. Early diagnosis significantly improves survival rates and reduces lethality. Innovative technologies are being developed for early de...

Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.

Drug design, development and therapy
OBJECTIVE: Alzheimer's disease poses a significant threat to human health. Currenttherapeutic medicines, while alleviate symptoms, fail to reverse the disease progression or reduce its harmful effects, and exhibit toxicity and side effects such as ga...

Rapid visual authentication of high-temperature Daqu Baijiu using porphyrin signal amplification and smartphone-based cloud machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
High-temperature Daqu Baijiu is known for its superior flavor and value. However, the adulteration of high-grade bottled low-grade Baijiu has become a significant concern. To enhance detection portability, a multi-channel visual sensor array was deve...

Improvement method for tea leaf moisture content prediction using VIS-NIR spectrum based on transfer learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Moisture significantly affects tea plants' growth and quality. Traditional methods of leaf moisture detection are usually destructive to samples, slow and labour-intensive. In this study, visible-near infrared (VIS-NIR) spectroscopy was used to detec...

Dataset resulting from the user study on comprehensibility of explainable AI algorithms.

Scientific data
This paper introduces a dataset that is the result of a user study on the comprehensibility of explainable artificial intelligence (XAI) algorithms. The study participants were recruited from 149 candidates to form three groups representing experts i...

AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding.

Science advances
B cell epitope prediction tools are crucial for designing vaccines and disease diagnostics. However, predicting which antigens a specific antibody binds to and their exact binding sites (epitopes) remains challenging. Here, we present AbEpiTope-1.0, ...

On Selecting Robust Approaches for Learning Predictive Biomarkers in Metabolomics Data Sets.

Analytical chemistry
Metabolomics, the study of small molecules within biological systems, offers insights into metabolic processes and, consequently, holds great promise for advancing health outcomes. Biomarker discovery in metabolomics represents a significant challeng...

Noise-Consistent Hypergraph Autoencoder Based on Contrastive Learning for Cancer ceRNA Association Prediction in Complex Biological Regulatory Networks.

Journal of chemical information and modeling
Competitive endogenous RNA (ceRNA) regulatory networks (CENA) have advanced our understanding of noncoding RNAs' roles in complex diseases, providing a theoretical basis for disease mechanisms. Existing ceRNA-disease association prediction methods ar...

Generative prediction of causal gene sets responsible for complex traits.

Proceedings of the National Academy of Sciences of the United States of America
The relationship between genotype and phenotype remains an outstanding question for organism-level traits because these traits are generally . The challenge arises from complex traits being determined by a combination of multiple genes (or loci), whi...

A multimodal dataset for coronary microvascular disease biomarker discovery.

Scientific data
Coronary microvascular disease (CMD), particularly prevalent among women, is associated with increased morbidity and mortality, making clinical screening vital for effective management. However, limited publicly available screening-level data hinders...