AIMC Topic: Machine Learning

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Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study.

The British journal of radiology
OBJECTIVE: To train a generic deep learning software (DLS) to classify breast cancer on ultrasound images and to compare its performance to human readers with variable breast imaging experience.

Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways.

Scientific reports
The war on cancer is progressing globally but slowly as researchers around the world continue to seek and discover more innovative and effective ways of curing this catastrophic disease. Organizing biological information, representing it, and making ...

Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning.

BMC neurology
BACKGROUND: Alzheimer's disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes i...

A wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer's disease.

Journal of neuroscience methods
BACKGROUND: AD is the most frequent neurodegenerative disease, severely impacting our society. Early diagnosis and prognosis are challenging tasks in the management of AD patients.

An ensemble boosting model for predicting transfer to the pediatric intensive care unit.

International journal of medical informatics
BACKGROUND: Early deterioration indicators have the potential to alert hospital care staff in advance of adverse events, such as patients requiring an increased level of care, or the need for rapid response teams to be called. Our work focuses on the...

Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner.

Cognition
Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which 'reverse engineering' language development, i.e...

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.

PLoS computational biology
This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules...

Identification of candidate serum biomarkers of childhood-onset growth hormone deficiency using SWATH-MS and feature selection.

Journal of proteomics
UNLABELLED: A typical clinical manifestation of growth hormone deficiency (GHD) is a short stature resulting from delayed growth, but GHD affects bone health, cardiovascular function and metabolic profile and therefore quality of life. Although early...

Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.

PloS one
Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a...

A general framework for sensor-based human activity recognition.

Computers in biology and medicine
Today's wearable devices like smartphones, smartwatches and intelligent glasses collect a large amount of data from their built-in sensors like accelerometers and gyroscopes. These data can be used to identify a person's current activity and in turn ...