AIMC Topic: Machine Learning

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Advances and applications of machine learning and deep learning in environmental ecology and health.

Environmental pollution (Barking, Essex : 1987) Aug 9, 2023
Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecolog...
Ecology Algorithms Machine Learning Deep Learning Environmental Health
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Developing machine learning approaches to identify candidate persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure.

Water research Aug 9, 2023
Determining which substances on the global market could be classified as persistent, mobile and toxic (PMT) substances or very persistent, very mobile (vPvM) substances is essential to prevent or reduce drinking water contamination from them. This st...
Machine Learning Algorithms Environmental Pollutants Computer Simulation Molecular Structure
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Post-Processing Fairness Evaluation of Federated Models: An Unsupervised Approach in Healthcare.

IEEE/ACM transactions on computational biology and bioinformatics Aug 9, 2023
Modern Healthcare cyberphysical systems have begun to rely more and more on distributed AI leveraging the power of Federated Learning (FL). Its ability to train Machine Learning (ML) and Deep Learning (DL) models for the wide variety of medical field...
Delivery of Health Care Machine Learning Benchmarking
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Development and validation of machine learning models for the prediction of SH-2 containing protein tyrosine phosphatase 2 inhibitors.

Molecular diversity Aug 8, 2023
Discovery and development of a new drug to the market is a highly challenging and resource consuming process. Although, modern drug discovery technologies have enabled the rapid identification of lead compounds, translation of the lead compounds into...
Humans Drug Discovery Models, Molecular Machine Learning Reproducibility of Results Small Molecule Libraries Protein Tyrosine Phosphatase, Non-Receptor Type 11 Enzyme Inhibitors
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Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation.

Journal of computer-aided molecular design Aug 8, 2023
Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, re...
Machine Learning Computer Simulation Deep Learning Bayes Theorem Drug Design Drug Discovery
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Decoding movement kinematics from EEG using an interpretable convolutional neural network.

Computers in biology and medicine Aug 8, 2023
Continuous decoding of hand kinematics has been recently explored for the intuitive control of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural networks (DNNs) are emerging as powerful decoders, for their ability to au...
Electroencephalography Humans Brain-Computer Interfaces Machine Learning Neural Networks, Computer Biomechanical Phenomena Movement Algorithms
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Multidisciplinary considerations of fairness in medical AI: A scoping review.

International journal of medical informatics Aug 8, 2023
INTRODUCTION: Artificial Intelligence (AI) technology has been developed significantly in recent years. The fairness of medical AI is of great concern due to its direct relation to human life and health. This review aims to analyze the existing resea...
Artificial Intelligence Machine Learning Humans Consensus Algorithms Data Collection
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AggBERT: Best in Class Prediction of Hexapeptide Amyloidogenesis with a Semi-Supervised ProtBERT Model.

Journal of chemical information and modeling Aug 8, 2023
The prediction of peptide amyloidogenesis is a challenging problem in the field of protein folding. Large language models, such as the ProtBERT model, have recently emerged as powerful tools in analyzing protein sequences for applications, such as pr...
Heuristics Amyloid Machine Learning Peptides Supervised Machine Learning Amino Acid Sequence
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Interpretable Machine Learning of Amino Acid Patterns in Proteins: A Statistical Ensemble Approach.

Journal of chemical theory and computation Aug 8, 2023
Explainable and interpretable unsupervised machine learning helps one to understand the underlying structure of data. We introduce an ensemble analysis of machine learning models to consolidate their interpretation. Its application shows that restric...
Amino Acid Sequence Trypsin Amino Acids Machine Learning Peptide Fragments
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A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation.

Environmental science and pollution research international Aug 8, 2023
The sustainability of the earth depends on renewable energy. Forecasting the output of renewable energy has a big impact on how we operate and manage our power networks. Accurate forecasting of renewable energy generation is crucial to ensuring grid ...
Renewable Energy Deep Learning Artificial Intelligence Machine Learning Algorithms Forecasting
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