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Networks inference problems are commonly found in multiple biomedical subfields such as genomics, metagenomics, neuroscience, and epidemiology. Networks are useful for representing a wide range of complex interactions ranging from those between molecular biomarkers, neurons, and microbial communities, to those found in human or animal populations. Recent technological advances have resulted in an...
There are several publicly accessible patient forums where patients can post questions related to their health conditions. The objective of this study was to develop a query-retrieval system that can mine such forums and identify existing questions most similar to the provided question. This pilot study based on a bag-of-words model with latent semantic analysis and cosine similarity suggests that...
A lack of recruitment of appropriate subjects plagues most clinical research trials. One barrier is an efficient way to identify eligible subjects. Researchers worked to harness computing power to improve automated identification of potential subjects for clinical trials with modest success. We use text classification to automatically identify patients for a hypothetical Acute Coronary Syndrome clinical...
ChaLearn is organizing the Automatic Machine Learning (AutoML) contest for IJCNN 2015, which challenges participants to solve classification and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction...
We present a feasibility study using text classification to classify tweets about alcohol use. Alcohol use is the most widely used substance in the US and is the leading risk factor for premature morbidity and mortality globally. Understanding use patterns and locations is an important step toward prevention, moderation, and control of alcohol outlets. Social media may provide an alternate way to...
We are organizing a challenge to reverse engineer the structure of neuronal networks from patterns of activity recorded with calcium fluorescence imaging. Unraveling the brain structure at the neuronal level at a large scale is an important step in brain science, with many ramifications in the comprehension of animal and human intelligence and learning capabilities, as well as understanding and curing...
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