AIMC Topic: Machine Learning

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DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins.

Drug discovery today
Application of computational methods in drug discovery has received increased attention in recent years as a way to accelerate drug target prediction. Based on 443 sequence-derived protein features, we applied the most commonly used machine learning ...

Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity.

PloS one
At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. H...

Training shortest-path tractography: Automatic learning of spatial priors.

NeuroImage
Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the stu...

Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

Ultrasound in medicine & biology
This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs catego...

Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition.

Journal of medical systems
Ventricular tachycardia (VT) and ventricular fibrillation (VF) are shockable ventricular cardiac ailments. Detection of VT/VF is one of the important step in both automated external defibrillator (AED) and implantable cardioverter defibrillator (ICD)...

Learning ECOC Code Matrix for Multiclass Classification with Application to Glaucoma Diagnosis.

Journal of medical systems
Classification of different mechanisms of angle closure glaucoma (ACG) is important for medical diagnosis. Error-correcting output code (ECOC) is an effective approach for multiclass classification. In this study, we propose a new ensemble learning m...

A novel behavioral model of the pasture-based dairy cow from GPS data using data mining and machine learning techniques.

Journal of dairy science
A better understanding of the behavior of individual grazing dairy cattle will assist in improving productivity and welfare. Global positioning systems (GPS) applied to cows could provide a means of monitoring grazing herds while overcoming the subst...

Cross-trial prediction of treatment outcome in depression: a machine learning approach.

The lancet. Psychiatry
BACKGROUND: Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific ...

A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington's Disease Patients.

Sensors (Basel, Switzerland)
Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, the objective of this work is to propose and validate a general probabilistic modeling approach for the classi...

Prototype-based models in machine learning.

Wiley interdisciplinary reviews. Cognitive science
An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the contex...