AIMC Topic: Machine Learning

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Predicting odor profile of food from its chemical composition: Towards an approach based on artificial intelligence and flavorists expertise.

Mathematical biosciences and engineering : MBE
Odor is central to food quality. Still, a major challenge is to understand how the odorants present in a given food contribute to its specific odor profile, and how to predict this olfactory outcome from the chemical composition. In this proof-of-con...

Research hotspots and trends of artificial intelligence in rheumatoid arthritis: A bibliometric and visualized study.

Mathematical biosciences and engineering : MBE
Artificial intelligence (AI) applications on rheumatoid arthritis (RA) are becoming increasingly popular. In this bibliometric study, we aimed to analyze the characteristics of publications relevant to the research of AI in RA, thereby developing a t...

ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning.

Briefings in functional genomics
Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance o...

ASAP-CORPS: A Semi-Autonomous Platform for COntact-Rich Precision Surgery.

Military medicine
INTRODUCTION: Remote military operations require rapid response times for effective relief and critical care. Yet, the military theater is under austere conditions, so communication links are unreliable and subject to physical and virtual attacks and...

[A preliminary prediction model of depression based on whole blood cell count by machine learning method].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]
This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter stu...

Deep Learning for Epidemiologists: An Introduction to Neural Networks.

American journal of epidemiology
Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning f...

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.

Bioinformatics (Oxford, England)
SUMMARY: Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of dat...

Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective.

Water science and technology : a journal of the International Association on Water Pollution Research
This study investigates changes in river flow patterns, in the Hunza Basin, Pakistan, attributed to climate change. Given the anticipated rise in extreme weather events, accurate streamflow predictions are increasingly vital. We assess three machine ...

ReGeNNe: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Common human diseases result from the interplay of genes and their biologically associated pathways. Genetic pathway analyses provide more biological insight as compared to conventional gene-based analysis. In this article, we propose a f...