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Biostatistics

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OntoStudyEdit: a new approach for ontology-based representation and management of metadata in clinical and epidemiological research.

Journal of biomedical semantics
BACKGROUND: The specification of metadata in clinical and epidemiological study projects absorbs significant expense. The validity and quality of the collected data depend heavily on the precise and semantical correct representation of their metadata...

The limitations of simple gene set enrichment analysis assuming gene independence.

Statistical methods in medical research
Since its first publication in 2003, the Gene Set Enrichment Analysis method, based on the Kolmogorov-Smirnov statistic, has been heavily used, modified, and also questioned. Recently a simplified approach using a one-sample t-test score to assess en...

Statistical Inference for Data Adaptive Target Parameters.

The international journal of biostatistics
Consider one observes n i.i.d. copies of a random variable with a probability distribution that is known to be an element of a particular statistical model. In order to define our statistical target we partition the sample in V equal size sub-samples...

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.

Scientific reports
Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs....

Random Bits Forest: a Strong Classifier/Regressor for Big Data.

Scientific reports
Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth),...

Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies.

Statistics in medicine
Binary classification problems are ubiquitous in health and social sciences. In many cases, one wishes to balance two competing optimality considerations for a binary classifier. For instance, in resource-limited settings, an human immunodeficiency v...