AIMC Topic: Machine Learning

Clear Filters Showing 27331 to 27340 of 34417 articles

Binding Activity Prediction of Cyclin-Dependent Inhibitors.

Journal of chemical information and modeling
The Cyclin-Dependent Kinases (CDKs) are the core components coordinating eukaryotic cell division cycle. Generally the crystal structure of CDKs provides information on possible molecular mechanisms of ligand binding. However, reliable and robust est...

Optimal combination of feature selection and classification via local hyperplane based learning strategy.

BMC bioinformatics
BACKGROUND: Classifying cancers by gene selection is among the most important and challenging procedures in biomedicine. A major challenge is to design an effective method that eliminates irrelevant, redundant, or noisy genes from the classification,...

Automated generation of directed graphs from vascular segmentations.

Journal of biomedical informatics
Automated feature extraction from medical images is an important task in imaging informatics. We describe a graph-based technique for automatically identifying vascular substructures within a vascular tree segmentation. We illustrate our technique us...

Machine learning approach for pooled DNA sample calibration.

BMC bioinformatics
BACKGROUND: Despite ongoing reduction in genotyping costs, genomic studies involving large numbers of species with low economic value (such as Black Tiger prawns) remain cost prohibitive. In this scenario DNA pooling is an attractive option to reduce...

Pairwise Constraint-Guided Sparse Learning for Feature Selection.

IEEE transactions on cybernetics
Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much atten...

Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

Psychiatry research
Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to ad...

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution.

Computer methods and programs in biomedicine
Breast cancer is one of the most perilous diseases among women. Breast screening is a method of detecting breast cancer at a very early stage which can reduce the mortality rate. Mammography is a standard method for the early diagnosis of breast canc...

Computational assignment of cell-cycle stage from single-cell transcriptome data.

Methods (San Diego, Calif.)
The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in para...

High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

ISA transactions
In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is re...