AIMC Topic: Machine Learning

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Geographical origin differentiation of Philippine Robusta coffee (C. canephora) using X-ray fluorescence-based elemental profiling with chemometrics and machine learning.

Food chemistry
The increasing demand for authenticity and traceability in high-value crops underscores the need for reliable methods to verify the geographical origin of single-origin coffee and prevent fraud. This study explores a rapid and cost-effective approach...

Machine learning-based exploration of Umami peptides in Pixian douban: Insights from virtual screening, molecular docking, and post-translational modifications.

Food chemistry
Pixian Doubanjiang (PXDB)'s distinctive umami profile is primarily attributed to its unique peptides; however, their structural characteristics, sensory mechanisms, and biosynthetic pathways during aging remain poorly understood. This study employed ...

Machine learning models accurately predict clades of proteocephalidean tapeworms (Onchoproteocephalidea) based on host and biogeographical data.

Cladistics : the international journal of the Willi Hennig Society
Proteocephalids are a cosmopolitan and diverse group of tapeworms (Cestoda) that have colonized vertebrate hosts in freshwater and terrestrial environments. Despite the ubiquity of the group, key macroevolutionary processes that have driven the group...

Uncovering key sources of regional ozone simulation biases using machine learning and SHAP analysis.

Environmental pollution (Barking, Essex : 1987)
Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and efficiently identifying key sources of simulation biases is crucial for model improvement. However, tra...

An extensive review on infectious disease diagnosis using machine learning techniques and next generation sequencing: State-of-the-art and perspectives.

Computers in biology and medicine
UNLABELLED: Infectious diseases, including tuberculosis (TB), HIV/AIDS, and emerging pathogens like COVID-19 pose severe global health challenges due to their rapid spread and significant morbidity and mortality rates. Next-generation sequencing (NGS...

Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVE: To determine if hyperinflammatory and hypoinflammatory pediatric acute respiratory distress syndrome (PARDS) subphenotypes defined using serum biomarkers can be determined solely from electronic health record (EHR) data using machine learn...

Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Genotype, environment, and genotype-by-environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framewo...

A Knowledge-Guided Graph Learning Approach Bridging Phenotype- and Target-Based Drug Discovery.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Discovering therapeutic molecules requires the integration of both phenotype-based drug discovery (PDD) and target-based drug discovery (TDD). However, this integration remains challenging due to the inherent heterogeneity, noise, and bias present in...

Preoperative multiclass classification of thymic mass lesions based on radiomics and machine learning.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Apart from rare cases such as lymphomas, germ cell tumors, neuroendocrine neoplasms, and thymic hyperplasia, thymic mass lesions (TMLs) are typically categorized into cysts, and thymomas. However, the classification results cannot be dete...