AIMC Topic: Machine Learning

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Applications of artificial intelligence in dentistry: A comprehensive review.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
OBJECTIVE: To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, pa...

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data.

Analytical chemistry
Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a...

GoVec: Gene Ontology Representation Learning Using Weighted Heterogeneous Graph and Meta-Path.

Journal of computational biology : a journal of computational molecular cell biology
Biomedical knowledge graphs are crucial to support data-intensive applications in the life sciences and health care. These graphs can be extended by generating a heterogeneous graph that contains both ontology terms and biomedical entities. However, ...

A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy.

PloS one
AIMS: Noncompaction cardiomyopathy (NCC) is considered a genetic cardiomyopathy with unknown pathophysiological mechanisms. We propose to evaluate echocardiographic predictors for rigid body rotation (RBR) in NCC using a machine learning (ML) based m...

Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study.

PloS one
The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant bre...

Machine learning predicts the effect of food on orally administered medicines.

International journal of pharmaceutics
Food-mediated changes to drug absorption, termed the food effect, are hard to predict and can have significant implications for the safety and efficacy of oral drug products in patients. Mimicking the prandial states of the human gastrointestinal tra...

Convolution-Based Encoding of Depth Images for Transfer Learning in RGB-D Scene Classification.

Sensors (Basel, Switzerland)
Classification of indoor environments is a challenging problem. The availability of low-cost depth sensors has opened up a new research area of using depth information in addition to color image (RGB) data for scene understanding. Transfer learning o...

A Simulation Study to Compare the Predictive Performance of Survival Neural Networks with Cox Models for Clinical Trial Data.

Computational and mathematical methods in medicine
BACKGROUND: Studies focusing on prediction models are widespread in medicine. There is a trend in applying machine learning (ML) by medical researchers and clinicians. Over the years, multiple ML algorithms have been adapted to censored data. However...

A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy.

Bioorganic & medicinal chemistry
Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully trans...

An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management.

Sensors (Basel, Switzerland)
Global competition among businesses imposes a more effective and low-cost supply chain allowing firms to provide products at a desired quality, quantity, and time, with lower production costs. The latter include holding cost, ordering cost, and backo...