AIMC Topic: Machine Learning

Clear Filters Showing 26271 to 26280 of 34417 articles

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

Neuroscience letters
Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-feature...

Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois.

American journal of public health
OBJECTIVES: To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services.

Metastasis detection from whole slide images using local features and random forests.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in cos...

Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository.

Journal of biomedical informatics
OBJECTIVE: Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the...

Learning from biomedical linked data to suggest valid pharmacogenes.

Journal of biomedical semantics
BACKGROUND: A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based ...

Deep Count: Fruit Counting Based on Deep Simulated Learning.

Sensors (Basel, Switzerland)
Recent years have witnessed significant advancement in computer vision research based on deep learning. Success of these tasks largely depends on the availability of a large amount of training samples. Labeling the training samples is an expensive pr...

Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis.

PloS one
A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipola...

Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations.

IEEE/ACM transactions on computational biology and bioinformatics
Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation da...

Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection.

Computers in biology and medicine
Different types of breast cancer are affecting lives of women across the world. Common types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular carcinoma, Medullary carcinoma, and Invasive lobular carcinoma (ILC). While...