AIMC Topic: Machine Learning

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Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.

BioMed research international
Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of...

Recursive Random Forests Enable Better Predictive Performance and Model Interpretation than Variable Selection by LASSO.

Journal of chemical information and modeling
Variable selection is of crucial significance in QSAR modeling since it increases the model predictive ability and reduces noise. The selection of the right variables is far more complicated than the development of predictive models. In this study, e...

An electronic medical record system with treatment recommendations based on patient similarity.

Journal of medical systems
As the core of health information technology (HIT), electronic medical record (EMR) systems have been changing to meet health care demands. To construct a new-generation EMR system framework with the capability of self-learning and real-time feedback...

Language-Model Assisted Brain Computer Interface for Typing: A Comparison of Matrix and Rapid Serial Visual Presentation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Noninvasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) popularly utilize event-related potential (ERP) for intent detection. Specifically, for EEG-based BCI typing systems, different symbol presentation paradigms have been ut...

Towards the automatic classification of neurons.

Trends in neurosciences
The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting growth of information about morphological, physiologic...

A rational model of function learning.

Psychonomic bulletin & review
Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We pr...

A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association
BACKGROUND: Postpartum depression (PPD) is a disorder that often goes undiagnosed. The development of a screening program requires considerable and careful effort, where evidence-based decisions have to be taken in order to obtain an effective test w...

Classification of hemodynamic responses associated with force and speed imagery for a brain-computer interface.

Journal of medical systems
Functional near-infrared spectroscopy (fNIRS) is an emerging optical technique, which can assess brain activities associated with tasks. In this study, six participants were asked to perform three imageries of hand clenching associated with force and...

Graph Embedded Extreme Learning Machine.

IEEE transactions on cybernetics
In this paper, we propose a novel extension of the extreme learning machine (ELM) algorithm for single-hidden layer feedforward neural network training that is able to incorporate subspace learning (SL) criteria on the optimization process followed f...

Efficient method for analyzing MR real-time cines: Toward accurate quantification of left ventricular function.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: To develop and assess an efficient method to identify end-expiratory end-diastolic (ED) and end-systolic (ES) images for accurate quantification of left ventricular (LV) function in real-time cine imaging.