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Related Concept Videos

Ranks01:02

Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Sex Linked Disorders01:43

Sex Linked Disorders

Like autosomes, sex chromosomes contain a variety of genes necessary for normal body function. When a mutation in one of these genes results in biological deficits, the disorder is considered sex-linked.

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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DNorm: disease name normalization with pairwise learning to rank.

Robert Leaman1, Rezarta Islamaj Dogan, Zhiyong Lu

  • 1National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA and Department of Biomedical Informatics, Arizona State University, 13212 East Shea Blvd, Scottsdale, AZ 85259, USA.

Bioinformatics (Oxford, England)
|August 24, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine learning approach for disease name normalization (DNorm), achieving significant improvements over existing methods. The developed DNorm tool enhances biomedical text mining by accurately identifying diseases in research literature.

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Area of Science:

  • Biomedical text mining
  • Natural Language Processing
  • Computational Biology

Background:

  • Disease name normalization (DNorm) is crucial for biomedical research but has fewer automated solutions compared to other text mining tasks.
  • Accurate identification of diseases in text is essential for data mining and knowledge discovery.

Purpose of the Study:

  • To develop the first machine learning approach for automated disease name normalization (DNorm).
  • To improve the accuracy and efficiency of identifying disease mentions in biomedical literature.

Main Methods:

  • Utilized the NCBI disease corpus and the MEDIC vocabulary (MeSH® and OMIM) for training.
  • Implemented a pairwise learning to rank framework, a novel approach for DNorm.
  • Developed a mathematically principled method for learning similarities between text mentions and disease concepts.

Main Results:

  • Achieved a micro-averaged F-measure of 0.782 and a macro-averaged F-measure of 0.809.
  • Demonstrated significant performance gains over lexical matching, MetaMap, and Lucene-based methods.
  • Outperformed the best baseline by 0.121 (micro-averaged) and 0.098 (macro-averaged) F-measure.

Conclusions:

  • The proposed machine learning approach represents a significant advancement in disease name normalization.
  • The DNorm tool offers a high-performing and reliable solution for biomedical text mining.
  • The source code and a web demonstration are publicly available for further research and application.