AIMC Topic: Machine Learning

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Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards.

Artificial intelligence in medicine
BACKGROUND: Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records...

C-PUGP: A cluster-based positive unlabeled learning method for disease gene prediction and prioritization.

Computational biology and chemistry
Disease gene detection is an important stage in the understanding disease processes and treatment. Some candidate disease genes are identified using many machine learning methods Although there are some differences in these methods including feature ...

Patient Similarity Networks for Precision Medicine.

Journal of molecular biology
Clinical research and practice in the 21st century is poised to be transformed by analysis of computable electronic medical records and population-level genome-scale patient profiles. Genomic data capture genetic and environmental state, providing in...

Representation learning using event-based STDP.

Neural networks : the official journal of the International Neural Network Society
Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a challenging problem. This paper proposes an event-based method to train ...

Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department.

Journal of medical toxicology : official journal of the American College of Medical Toxicology
Adverse drug events (ADEs) are common and have serious consequences in older adults. ED visits are opportunities to identify and alter the course of such vulnerable patients. Current practice, however, is limited by inaccurate reporting of medication...

Nonlinear analysis and synthesis of video images using deep dynamic bottleneck neural networks for face recognition.

Neural networks : the official journal of the International Neural Network Society
Nonlinear components extracted from deep structures of bottleneck neural networks exhibit a great ability to express input space in a low-dimensional manifold. Sharing and combining the components boost the capability of the neural networks to synthe...

Redefining the Protein Kinase Conformational Space with Machine Learning.

Cell chemical biology
Protein kinases are dynamic, adopting different conformational states that are critical for their catalytic activity. We assess a range of structural features derived from the conserved αC helix and DFG motif to define the conformational space of the...

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

BMC bioinformatics
BACKGROUND: In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. T...

diploS/HIC: An Updated Approach to Classifying Selective Sweeps.

G3 (Bethesda, Md.)
Identifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective s...

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks.

Computational intelligence and neuroscience
Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional metho...