AIMC Topic: Machine Learning

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ACID: Association Correction for Imbalanced Data in GWAS.

IEEE/ACM transactions on computational biology and bioinformatics
Genome-wide association study (GWAS) has been widely witnessed as a powerful tool for revealing suspicious loci from various diseases. However, real world GWAS tasks always suffer from the data imbalance problem of sufficient control samples and limi...

Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning.

Human brain mapping
Previous studies have suggested that disorders of consciousness (DOC) after severe brain injury may result from disconnections of the thalamo-cortical system. However, thalamo-cortical connectivity differences between vegetative state (VS), minimally...

A Self-Organizing Incremental Neural Network based on local distribution learning.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incr...

Unsupervised detection of cancer driver mutations with parsimony-guided learning.

Nature genetics
Methods are needed to reliably prioritize biologically active driver mutations over inactive passengers in high-throughput sequencing cancer data sets. We present ParsSNP, an unsupervised functional impact predictor that is guided by parsimony. ParsS...

Machine-Learning Based Channel Quality and Stability Estimation for Stream-Based Multichannel Wireless Sensor Networks.

Sensors (Basel, Switzerland)
Wireless sensor networks (WSNs) have become more and more diversified and are today able to also support high data rate applications, such as multimedia. In this case, per-packet channel handshaking/switching may result in inducing additional overhea...

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder.

NeuroImage
Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to diff...

Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System.

IEEE transactions on biomedical circuits and systems
We present results from a new approach to learning and plasticity in neuromorphic hardware systems: to enable flexibility in implementable learning mechanisms while keeping high efficiency associated with neuromorphic implementations, we combine a ge...

Classification of the fragrant styles and evaluation of the aromatic quality of flue-cured tobacco leaves by machine-learning methods.

Journal of bioinformatics and computational biology
During commercial transactions, the quality of flue-cured tobacco leaves must be characterized efficiently, and the evaluation system should be easily transferable across different traders. However, there are over 3000 chemical compounds in flue-cure...

Single NMR image super-resolution based on extreme learning machine.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
INTRODUCTION: The performance limitation of MRI equipment and higher resolution demand of NMR images from radiologists have formed a strong contrast. Therefore, it is important to study the super resolution algorithm suitable for NMR images, using lo...

Computer-assisted framework for machine-learning-based delineation of GTV regions on datasets of planning CT and PET/CT images.

Journal of radiation research
We have proposed a computer-assisted framework for machine-learning-based delineation of gross tumor volumes (GTVs) following an optimum contour selection (OCS) method. The key idea of the proposed framework was to feed image features around GTV cont...