AIMC Topic: Machine Learning

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Prediction of bioconcentration factors in fish and invertebrates using machine learning.

The Science of the total environment
The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different mode...

Using Machine Learning to Aid the Interpretation of Urine Steroid Profiles.

Clinical chemistry
BACKGROUND: Urine steroid profiles are used in clinical practice for the diagnosis and monitoring of disorders of steroidogenesis and adrenal pathologies. Machine learning (ML) algorithms are powerful computational tools used extensively for the reco...

Skin lesion classification with ensembles of deep convolutional neural networks.

Journal of biomedical informatics
Skin cancer is a major public health problem with over 123,000 newly diagnosed cases worldwide in every year. Melanoma is the deadliest form of skin cancer, responsible for over 9000 deaths in the United States each year. Thus, reliable automatic mel...

Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning.

EBioMedicine
Clinical prediction of advanced hepatic fibrosis (HF) and cirrhosis has long been challenging due to the gold standard, liver biopsy, being an invasive approach with certain limitations. Less invasive blood test tandem with a cutting-edge machine lea...

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis.

International journal of molecular sciences
Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and c...

Structural neuroimaging as clinical predictor: A review of machine learning applications.

NeuroImage. Clinical
In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literatu...

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.

NeuroImage
The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is importan...

General Method for the Identification of Crystal Faces Using Raman Spectroscopy Combined with Machine Learning and Application to the Epitaxial Growth of Acetaminophen.

Langmuir : the ACS journal of surfaces and colloids
Crystal morphology is one of the key crystallographic characteristics that governs the macroscopic properties of crystalline materials. The identification of crystal faces, or face indexing, is an important technique that is used to get information r...

Development of Machine Learning Algorithms for Prediction of 5-Year Spinal Chordoma Survival.

World neurosurgery
BACKGROUND: Chordomas are locally invasive slow-growing tumors that are difficult to study because of the rarity of the tumors and the lack of significant volumes of patients with longitudinal follow-up. As such, there are currently no machine learni...

Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To automatically detect and classify geographic atrophy (GA) in fundus autofluorescence (FAF) images using a deep learning algorithm.