AIMC Topic: Machine Learning

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Global-local least-squares support vector machine (GLocal-LS-SVM).

PloS one
This study introduces the global-local least-squares support vector machine (GLocal-LS-SVM), a novel machine learning algorithm that combines the strengths of localised and global learning. GLocal-LS-SVM addresses the challenges associated with decen...

A novel performance scoring quantification framework for stress test set-ups.

PloS one
Stress tests, e.g., the cardiac stress test, are standard clinical screening tools aimed to unmask clinical pathology. As such stress tests indirectly measure physiological reserves. The term reserve has been developed to account for the dis-junction...

Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges.

Critical care clinics
The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent innovations in computing architecture and machine learning (ML) algorithms have made rapid consumptio...

Machine learning models for estimating contamination across different curbside collection strategies.

Journal of environmental management
Contaminated recyclables, which are frequently discarded as waste, pose a significant challenge to the implementation of a circular economy. These contaminated recyclables impede the circulation of resources, resulting in higher processing costs at m...

Prediction of NH and HCN yield from biomass fast pyrolysis: Machine learning modeling and evaluation.

The Science of the total environment
Rapid pyrolysis is a promising technique to convert biomass into fuel oil, where NO emission remains a substantial environmental risk. NH and HCN are top precursors for NO emission. In order to clarify their migration path and provide appropriate str...

Differential diagnosis of secondary hypertension based on deep learning.

Artificial intelligence in medicine
Secondary hypertension is associated with higher risks of target organ damage and cardiovascular and cerebrovascular disease events. Early aetiology identification can eliminate aetiologies and control blood pressure. However, inexperienced doctors o...

Small Data Can Play a Big Role in Chemical Discovery.

Angewandte Chemie (International ed. in English)
The chemistry community is currently witnessing a surge of scientific discoveries in organic chemistry supported by machine learning (ML) techniques. Whereas many of these techniques were developed for big data applications, the nature of experimenta...

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives.

Journal of chemical information and modeling
Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is esti...

Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy.

International journal of molecular sciences
Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein-protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions an...

Magicmol: a light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration.

BMC bioinformatics
The flourishment of machine learning and deep learning methods has boosted the development of cheminformatics, especially regarding the application of drug discovery and new material exploration. Lower time and space expenses make it possible for sci...