AIMC Topic: Biostatistics
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Online cross-validation-based ensemble learning.
Statistics in medicine
May 4, 2017
Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinit...
Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies.
Statistics in medicine
Apr 6, 2017
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...
Random Bits Forest: a Strong Classifier/Regressor for Big Data.
Scientific reports
Jul 22, 2016
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),...
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.
Scientific reports
May 17, 2016
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....
OntoStudyEdit: a new approach for ontology-based representation and management of metadata in clinical and epidemiological research.
Journal of biomedical semantics
Dec 18, 2015
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
Oct 14, 2012
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...
BAMBI integrates biostatistical and artificial intelligence methods to improve RNA biomarker discovery.
Briefings in bioinformatics
Mar 4, 2025
RNA biomarkers enable early and precise disease diagnosis, monitoring, and prognosis, facilitating personalized medicine and targeted therapeutic strategies. However, identification of RNA biomarkers is hindered by the challenge of analyzing relative...
An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.
Briefings in bioinformatics
Sep 23, 2024
The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatia...
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