AIMC Topic: Machine Learning

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Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks.

Journal of thermal biology
PURPOSE: There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine lea...

Open data and algorithms for open science in AI-driven molecular informatics.

Current opinion in structural biology
Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformatio...

pH-dependent solubility prediction for optimized drug absorption and compound uptake by plants.

Journal of computer-aided molecular design
Aqueous solubility is the most important physicochemical property for agrochemical and drug candidates and a prerequisite for uptake, distribution, transport, and finally the bioavailability in living species. We here present the first-ever direct ma...

Federated learning for molecular discovery.

Current opinion in structural biology
Federated Learning enables machine learning across multiple sources of data and alleviates the risk of leaking private information between partners thereby encouraging knowledge sharing and collaborative modelling. Hence, Federated Learning opens the...

Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste.

Waste management (New York, N.Y.)
The combination of machine learning and infrared spectroscopy was reported as effective for fast characterization of biomass and waste (BW). However, this characterization process is lack of interpretability towards its chemical insights, leading to ...

Geometric Interaction Graph Neural Network for Predicting Protein-Ligand Binding Affinities from 3D Structures (GIGN).

The journal of physical chemistry letters
Predicting protein-ligand binding affinities (PLAs) is a core problem in drug discovery. Recent advances have shown great potential in applying machine learning (ML) for PLA prediction. However, most of them omit the 3D structures of complexes and ph...

Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts.

Seminars in diagnostic pathology
Machine learning (ML) is becoming an integral aspect of several domains in medicine. Yet, most pathologists and laboratory professionals remain unfamiliar with such tools and are unprepared for their inevitable integration. To bridge this knowledge g...

Learning Relationships Between Chemical and Physical Stability for Peptide Drug Development.

Pharmaceutical research
PURPOSE OR OBJECTIVE: Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. Moreover, fluorescent reporter Thiofl...

Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.

International journal of environmental research and public health
It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today's hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the IC...

Artificial intelligence and machine learning for hemorrhagic trauma care.

Military Medical Research
Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and c...