AIMC Topic: Machine Learning

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Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.

Journal of medicinal chemistry
Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing synthetic planning into their overall appr...

Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning.

Molecules (Basel, Switzerland)
A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive cl...

Machine learning identifies 10 feature miRNAs for lung squamous cell carcinoma.

Gene
Lung squamous cell carcinoma (LUSC) is a common type of malignancy. The mechanism behind its tumor progression is not clear yet. The aim of this study is to use machine learning to identify the feature miRNAs, which can be reliably used as biomarkers...

Deep learning for survival outcomes.

Statistics in medicine
Deep learning is a class of machine learning algorithms that are popular for building risk prediction models. When observations are censored, the outcomes are only partially observed and standard deep learning algorithms cannot be directly applied. W...

Surgical spectral imaging.

Medical image analysis
Recent technological developments have resulted in the availability of miniaturised spectral imaging sensors capable of operating in the multi- (MSI) and hyperspectral imaging (HSI) regimes. Simultaneous advances in image-processing techniques and ar...

Prediction of physical violence in schizophrenia with machine learning algorithms.

Psychiatry research
Patients with schizophrenia have been shown to have an increased risk for physical violence. While certain features have been identified as risk factors, it has been difficult to integrate these variables to identify violent patients. The present stu...

A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning.

Water research
Predicting recreational water quality is one of the most difficult tasks in water management with major implications for humans and society. Many data-driven models have been used to predict water quality indicators to allow a real time assessment of...

Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification fa...

MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.

Molecular pharmaceutics
To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is ...

A New Machine-Learning Tool for Fast Estimation of Liquid Viscosity. Application to Cosmetic Oils.

Journal of chemical information and modeling
The viscosities of pure liquids are estimated at 25 °C, from their molecular structures, using three modeling approaches: group contributions, COSMO-RS σ-moment-based neural networks, and graph machines. The last two are machine-learning methods, whe...