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Data Accuracy

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Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems.

Computational intelligence and neuroscience
Accurate prediction of the short time series with highly irregular behavior is a challenging task found in many areas of modern science. Such data fluctuations are not systematic and hardly predictable. In recent years, artificial neural networks hav...

Deep learning for high-throughput quantification of oligodendrocyte ensheathment at single-cell resolution.

Communications biology
High-throughput quantification of oligodendrocyte myelination is a challenge that, if addressed, would facilitate the development of therapeutics to promote myelin protection and repair. Here, we established a high-throughput method to assess oligode...

An automated data verification approach for improving data quality in a clinical registry.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The quality of data is crucial for clinical registry studies as it impacts credibility. In the regular practice of most such studies, a vulnerability arises from researchers recording data on paper-based case report forms (C...

Application of machine learning in rheumatic disease research.

The Korean journal of internal medicine
Over the past decade, there has been a paradigm shift in how clinical data are collected, processed and utilized. Machine learning and artificial intelligence, fueled by breakthroughs in high-performance computing, data availability and algorithmic i...

Validation of a UPLC-PDA method to study the content and stability of 5-chloro 8-hydroxyquinoline and 5,7-dichloro 8-hydroxyquinoline in medicated feed used in swine farming.

Journal of pharmaceutical and biomedical analysis
A new, rapid, simple and specific method to determine 5-chloro 8-hydroxyquinoline (5-HQ) and 5,7-dichloro 8-hydroxyquinoline (5,7-HQ) stability in swine feed was optimized and validated. A system consisting of an ACQUITY UPLC BEH C column (1.7 μm, 2....

The future is coming: promising perspectives regarding the use of machine learning in renal transplantation.

Jornal brasileiro de nefrologia
INTRODUCTION: The prediction of post transplantation outcomes is clinically important and involves several problems. The current prediction models based on standard statistics are very complex, difficult to validate and do not provide accurate predic...

Predictive models for charitable giving using machine learning techniques.

PloS one
Private giving represents more than three fourths of all U.S. charitable donations, about 2% of total Gross Domestic Product (GDP). Private giving is a significant factor in funding the nonprofit sector of the U.S. economy, which accounts for more th...

Identification of synthetic lethality based on a functional network by using machine learning algorithms.

Journal of cellular biochemistry
Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 ge...