AIMC Topic: Machine Learning

Clear Filters Showing 34131 to 34140 of 34417 articles

Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA.

SAR and QSAR in environmental research
The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of ...

Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts.

Journal of biomedical optics
Invasive brain cancer cells cannot be visualized during surgery and so they are often not removed. These residual cancer cells give rise to recurrences.

Genome wide predictions of miRNA regulation by transcription factors.

Bioinformatics (Oxford, England)
MOTIVATION: Reconstructing regulatory networks from expression and interaction data is a major goal of systems biology. While much work has focused on trying to experimentally and computationally determine the set of transcription-factors (TFs) and m...

AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields.

Bioinformatics (Oxford, England)
MOTIVATION: Protein intrinsically disordered regions (IDRs) play an important role in many biological processes. Two key properties of IDRs are (i) the occurrence is proteome-wide and (ii) the ratio of disordered residues is about 6%, which makes it ...

Complementary feature selection from alternative splicing events and gene expression for phenotype prediction.

Bioinformatics (Oxford, England)
MOTIVATION: A central task of bioinformatics is to develop sensitive and specific means of providing medical prognoses from biomarker patterns. Common methods to predict phenotypes in RNA-Seq datasets utilize machine learning algorithms trained via g...

Higher order methylation features for clustering and prediction in epigenomic studies.

Bioinformatics (Oxford, England)
MOTIVATION: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-under...

Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.

Brain imaging and behavior
The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be ef...

Combining satellite imagery and machine learning to predict poverty.

Science (New York, N.Y.)
Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumpt...

Application of semi-supervised deep learning to lung sound analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The analysis of lung sounds, collected through auscultation, is a fundamental component of pulmonary disease diagnostics for primary care and general patient monitoring for telemedicine. Despite advances in computation and algorithms, the goal of aut...

Principal component analysis can decrease neural networks performance for incipient falls detection: A preliminary study with hands and feet accelerations.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Fall-related accidents constitute a major problem for elderly people and a burden to the health-care national system. It is therefore important to design devices (e.g., accelerometers) and machine learning algorithms able to recognize incipient falls...