AIMC Topic: Machine Learning

Clear Filters Showing 26461 to 26470 of 34417 articles

Classifying kinase conformations using a machine learning approach.

BMC bioinformatics
BACKGROUND: Signaling proteins such as protein kinases adopt a diverse array of conformations to respond to regulatory signals in signaling pathways. Perhaps the most fundamental conformational change of a kinase is the transition between active and ...

Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning.

ACS nano
Plasmonic sensors have been used for a wide range of biological and chemical sensing applications. Emerging nanofabrication techniques have enabled these sensors to be cost-effectively mass manufactured onto various types of substrates. To accompany ...

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

International journal of molecular sciences
Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of...

Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study.

Behavioural neurology
Subjects with Alzheimer's disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their daily life and that of their families and caregivers. A neuropsychological assessment plays a crucial...

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM.

Computational intelligence and neuroscience
Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal m...

Biologically plausible learning in neural networks with modulatory feedback.

Neural networks : the official journal of the International Neural Network Society
Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new ...

A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

Medical image analysis
We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combi...

Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.

Computers in biology and medicine
BACKGROUND: When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is o...

Bosco: Boosting Corrections for Genome-Wide Association Studies With Imbalanced Samples.

IEEE transactions on nanobioscience
In genome-wide association studies (GWAS), the acquired sequential data may exhibit imbalance structure: abundant control vs. limited case samples. Such sample imbalance issue is particularly serious when investigating rare diseases or common disease...