AIMC Topic: Machine Learning

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Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".

Science (New York, N.Y.)
We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-...

Deep learning model for ultrafast multifrequency optical property extractions for spatial frequency domain imaging.

Optics letters
Spatial frequency domain imaging (SFDI) is emerging as an important new method in biomedical imaging due to its ability to provide label-free, wide-field tissue optical property maps. Most prior SFDI studies have utilized two spatial frequencies (2-f...

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

Bioinformatics (Oxford, England)
MOTIVATION: MicroRNAs (miRNAs) are small non-coding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRN...

PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging.

Aging
Aging biomarkers are the qualitative and quantitative indicators of the aging processes of the human body. Estimation of biological age is important for assessing the physiological state of an organism. The advent of machine learning lead to the deve...

Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes.

Science (New York, N.Y.)
Identifying the animal origins of RNA viruses requires years of field and laboratory studies that stall responses to emerging infectious diseases. Using large genomic and ecological datasets, we demonstrate that animal reservoirs and the existence an...

Detecting central hypovolemia in simulated hypovolemic shock by automated feature extraction with principal component analysis.

Physiological reports
Assessment of the volume status by blood pressure (BP) monitoring is difficult, since baroreflex control of BP makes it insensitive to blood loss up to about one liter. We hypothesized that a machine learning model recognizes the progression of centr...

Leukocyte recognition in human fecal samples using texture features.

Journal of the Optical Society of America. A, Optics, image science, and vision
Unlike urine or blood samples with a single background, human fecal samples contain large amounts of food debris, amorphous particles, and undigested plant cells. It is difficult to segment such impurities when mixed with leukocytes. Cell degradation...

Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders.

Neurosurgical focus
OBJECTIVEIf not anticipated and prearranged, hospital stay can be prolonged while the patient awaits placement in a rehabilitation unit or skilled nursing facility following elective spine surgery. Preoperative prediction of the likelihood of postope...