AIMC Topic: Machine Learning

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Identification of caveolin-1 domain signatures via machine learning and graphlet analysis of single-molecule super-resolution data.

Bioinformatics (Oxford, England)
MOTIVATION: Network analysis and unsupervised machine learning processing of single-molecule localization microscopy of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distin...

refineD: improved protein structure refinement using machine learning based restrained relaxation.

Bioinformatics (Oxford, England)
MOTIVATION: Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraint...

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases.

Science translational medicine
Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical i...

Deep Learning Approaches Outperform Conventional Strategies in De-Identification of German Medical Reports.

Studies in health technology and informatics
One of the major obstacles for research on German medical reports is the lack of de-identified medical corpora. Previous de-identification tasks focused on non-German medical texts, which raised the demand for an in-depth evaluation of de-identificat...

Analysis of machine learning algorithms as integrative tools for validation of next generation sequencing data.

European review for medical and pharmacological sciences
OBJECTIVE: While next generation sequencing (NGS) has become the technology of choice for clinical diagnostics, most genetic laboratories still use Sanger sequencing for orthogonal confirmation of NGS results. Previous studies have shown that when th...

Sound source ranging using a feed-forward neural network trained with fitting-based early stopping.

The Journal of the Acoustical Society of America
When a feed-forward neural network (FNN) is trained for acoustic source ranging in an ocean waveguide, it is difficult evaluating the FNN ranging accuracy of unlabeled test data. The label is the distance between source and receiver array. A fitting-...

Knee exoskeleton enhanced with artificial intelligence to provide assistance-as-needed.

The Review of scientific instruments
Robotic therapy is a useful method applied during rehabilitation of stroke patients (to regain motor functions). To ensure active participation of the patient, assistance-as-needed is provided during robotic training. However, most existing studies a...

Percept-related EEG classification using machine learning approach and features of functional brain connectivity.

Chaos (Woodbury, N.Y.)
Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature extraction and selection techniques reducing the dimensionality of input data. Dimensionality reduct...