AIMC Topic: Machine Learning

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Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model.

Molecular diversity
DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, development of DGAT1 inhibitors with novel chemical scaffolds is desired and important in the drug discov...

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.

Molecular diversity
The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to ...

Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values.

PLoS computational biology
Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guid...

Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion.

Clinical neuroradiology
PURPOSE: The objective of this study was to predict hematoma expansion (HE) by radiomic models based on different machine learning methods and determine the best radiomic model through the comparison.

Characterizing the Clinical Features and Atrophy Patterns of -Related Frontotemporal Dementia With Disease Progression Modeling.

Neurology
BACKGROUND AND OBJECTIVE: Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of mutations have grouped all different mutations together and shown an association with focal at...

Learning the Exciton Properties of Azo-dyes.

The journal of physical chemistry letters
The determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine lear...

Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding.

Sensors (Basel, Switzerland)
A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today's 4th industrial revolution. Generally accepted roles and implementation recipes of cyber...

A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks.

Sensors (Basel, Switzerland)
Cyber-physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troub...

CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method.

BMC bioinformatics
BACKGROUND: Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined...

A Typology of Existing Machine Learning-Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis.

Journal of medical Internet research
BACKGROUND: Considerable effort has been devoted to the development of artificial intelligence, including machine learning-based predictive analytics (MLPA) for use in health care settings. The growth of MLPA could be fueled by payment reforms that h...