AIMC Topic: Machine Learning

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Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows.

Animal : an international journal of animal bioscience
Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to chang...

Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI).

Artificial intelligence in medicine
Over the years, there has been growing interest in using machine learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographi...

Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data.

Nature protocols
DNA methylation data-based precision cancer diagnostics is emerging as the state of the art for molecular tumor classification. Standards for choosing statistical methods with regard to well-calibrated probability estimates for these typically highly...

Machine learning derived input-function in a dynamic F-FDG PET study of mice.

Biomedical physics & engineering express
Tracer kinetic modelling, based on dynamic F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in humans and animals. Knowledge of the arterial input-function (AIF) is required for such measurements. O...

Transfer Learning in ECG Classification from Human to Horse Using a Novel Parallel Neural Network Architecture.

Scientific reports
Automatic or semi-automatic analysis of the equine electrocardiogram (eECG) is currently not possible because human or small animal ECG analysis software is unreliable due to a different ECG morphology in horses resulting from a different cardiac inn...

Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.

Breast cancer research : BCR
BACKGROUND: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification ...

Deep neural networks for human microRNA precursor detection.

BMC bioinformatics
BACKGROUND: MicroRNAs (miRNAs) play important roles in a variety of biological processes by regulating gene expression at the post-transcriptional level. So, the discovery of new miRNAs has become a popular task in biological research. Since the expe...

VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning.

PLoS computational biology
Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combin...

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment.

Journal of visualized experiments : JoVE
Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effect...