AIMC Topic: Machine Learning

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Computational prediction of inter-species relationships through omics data analysis and machine learning.

BMC bioinformatics
BACKGROUND: Antibiotic resistance and its rapid dissemination around the world threaten the efficacy of currently-used medical treatments and call for novel, innovative approaches to manage multi-drug resistant infections. Phage therapy, i.e., the us...

Metabolomics predicts the pharmacological profile of new psychoactive substances.

Journal of psychopharmacology (Oxford, England)
BACKGROUND: The unprecedented proliferation of new psychoactive substances (NPS) threatens public health and challenges drug policy. Information on NPS pharmacology and toxicity is, in most cases, unavailable or very limited and, given the large numb...

Laplacian regularized low-rank representation for cancer samples clustering.

Computational biology and chemistry
Cancer samples clustering based on biomolecular data has been becoming an important tool for cancer classification. The recognition of cancer types is of great importance for cancer treatment. In this paper, in order to improve the accuracy of cancer...

Automated detection of imaging features of disproportionately enlarged subarachnoid space hydrocephalus using machine learning methods.

NeuroImage. Clinical
OBJECTIVE: Create an automated classifier for imaging characteristics of disproportionately enlarged subarachnoid space hydrocephalus (DESH), a neuroimaging phenotype of idiopathic normal pressure hydrocephalus (iNPH).

Evaluation of the bias and precision of regression techniques and machine learning approaches in total dissolved solids modeling of an urban aquifer.

Environmental science and pollution research international
TDS is modeled for an aquifer near an unlined landfill in Canada. Canadian Drinking Water Guidelines and other indices are used to evaluate TDS concentrations in 27 monitoring wells surrounding the landfill. This study aims to predict TDS concentrati...

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning.

Journal of visualized experiments : JoVE
We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatm...

Deep Semantic Segmentation of Kidney and Space-Occupying Lesion Area Based on SCNN and ResNet Models Combined with SIFT-Flow Algorithm.

Journal of medical systems
Renal segmentation is one of the most fundamental and challenging task in computer aided diagnosis systems. In order to overcome the shortcomings of automatic kidney segmentation based on deep network for abdominal CT images, a two-stage semantic seg...

Employing fingerprinting of medicinal plants by means of LC-MS and machine learning for species identification task.

Scientific reports
A dataset of liquid chromatography-mass spectrometry measurements of medicinal plant extracts from 74 species was generated and used for training and validating plant species identification algorithms. Various strategies for data handling and feature...

Deep transfer learning-based hologram classification for molecular diagnostics.

Scientific reports
Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microcopy. However, extensive computation is required to reconstruct object images fr...