AIMC Topic: Machine Learning

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Sfcnn: a novel scoring function based on 3D convolutional neural network for accurate and stable protein-ligand affinity prediction.

BMC bioinformatics
BACKGROUND: Computer-aided drug design provides an effective method of identifying lead compounds. However, success rates are significantly bottlenecked by the lack of accurate and reliable scoring functions needed to evaluate binding affinities of p...

Deep learning features encode interpretable morphologies within histological images.

Scientific reports
Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains...

Analysis of Human Exercise Health Monitoring Data of Smart Bracelet Based on Machine Learning.

Computational intelligence and neuroscience
The smart bracelet has become a hot-selling commodity, according to a daily consumption survey. Based on people's interest and concern for their health, the smart bracelet, as a design and application for achieving healthy weight loss monitoring, is ...

Design of Machine Learning Algorithm for Tourism Demand Prediction.

Computational and mathematical methods in medicine
Unused hotel rooms, unused event tickets, and unsold items are all examples of wasted expenses and earnings. Governments require accurate tourism demand forecasting in order to make informed decisions on topics such as infrastructure development and ...

Radiomics and artificial intelligence in malignant uterine body cancers: Protocol for a systematic review.

PloS one
INTRODUCTION: Uterine body cancers (UBC) are represented by endometrial carcinoma (EC) and uterine sarcoma (USa). The clinical management of both is hindered by the complex classification of patients into risk classes. This problem could be simplifie...

Quantum Neural Networks and Topological Quantum Field Theories.

Neural networks : the official journal of the International Neural Network Society
Our work intends to show that: (1) Quantum Neural Networks (QNNs) can be mapped onto spin-networks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theory (TQFT); (2) A nu...

Critical features identification for chemical chronic toxicity based on mechanistic forecast models.

Environmental pollution (Barking, Essex : 1987)
Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cau...

Tell me something interesting: Clinical utility of machine learning prediction models in the ICU.

Journal of biomedical informatics
In recent years, extensive resources are dedicated to the development of machine learning (ML) based clinical prediction models for intensive care unit (ICU) patients. These models are transforming patient care into a collaborative human-AI task, yet...

Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy.

Journal of applied clinical medical physics
PURPOSE: Accurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient ...

Machine Learning for Chemical Reactivity: The Importance of Failed Experiments.

Angewandte Chemie (International ed. in English)
Assessing the outcomes of chemical reactions in a quantitative fashion has been a cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data-driven modelling bears an enormous potential to streamline thi...