Evidence map›Paper›PMID 29669699›Full record

ArticleEBioMedicine2018

A Systems Approach to Refine Disease Taxonomy by Integrating Phenotypic and Molecular Networks.

Xuezhong Zhou, Lei Lei, Jun Liu, Arda Halu, Yingying Zhang, Bing Li, Zhili Guo, Guangming Liu, Changkai Sun, Joseph Loscalzo and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed.

  1. Review
  2. Patient stratification reveals the molecular basis of disease co-occurrences.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  12. Diversity and molecular network patterns of symptom phenotypes.NPJ systems biology and applications · 2021
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  20. Observational
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Xuezhong ZhouSchool of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China.
Lei LeiInstitute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, No.16, Nanxiaojie, Dongzhimennei, Dongcheng District, Beijing 100700, China.
Jun LiuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No.16, Nanxiaojie, Dongzhimennei, Dongcheng District, Beijing 100700, China.
Arda HaluChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA.
Yingying ZhangInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No.16, Nanxiaojie, Dongzhimennei, Dongcheng District, Beijing 100700, China; Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700,China.
Bing LiInstitute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, No.16, Nanxiaojie, Dongzhimennei, Dongcheng District, Beijing 100700, China; Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No.16, Nanxiaojie, Dongzhimennei, Dongcheng District, Beijing 100700, China.
Zhili GuoInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No.16, Nanxiaojie, Dongzhimennei, Dongcheng District, Beijing 100700, China; Jiaxing Traditional Chinese Medicine Affiliated Hospital of Zhejiang Chinese Medical University, No. 1501, Zhongshan East Road, Jiaxing, Zhejiang 314000, China.
Guangming LiuSchool of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China.
Changkai SunSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China; Research Center for the Control Engineering of Translational Precision Medicine, Dalian University of Technology, Dalian 116024, China; State Key Laboratory of Fine Chemicals, Dalian R&D Center for Stem Cell and Tissue Engineering, Dalian University of Technology, Dalian 116024, China; Liaoning Provincial Key Laboratory of Cerebral Diseases, Institute for Brain Disorders, Dalian Medical University, Dalian 116044, China.
Joseph LoscalzoDepartment of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.
Amitabh SharmaChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. Electronic address: amitabh.sharma@channing.harvard.edu.
Zhong WangInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No.16, Nanxiaojie, Dongzhimennei, Dongcheng District, Beijing 100700, China. Electronic address: wangzh@mail.cintcm.ac.cn.

Funding

Systems Biology of Airway DiseaseP01HL132825 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI WEISS, SCOTT T · 2016 to 2020
$12.6M
NHLBI NIH HHS P01 HL132825
6 · The paper itself

Abstract

The International Classification of Diseases (ICD) relies on clinical features and lags behind the current understanding of the molecular specificity of disease pathobiology, necessitating approaches that incorporate growing biomedical data for classifying diseases to meet the needs of precision medicine. Our analysis revealed that the heterogeneous molecular diversity of disease chapters and the blurred boundary between disease categories in ICD should be further investigated. Here, we propose a new classification of diseases (NCD) by developing an algorithm that predicts the additional categories of a disease by integrating multiple networks consisting of disease phenotypes and their molecular profiles. With statistical validations from phenotype-genotype associations and interactome networks, we demonstrate that NCD improves disease specificity owing to its overlapping categories and polyhierarchical structure. Furthermore, NCD captures the molecular diversity of diseases and defines clearer boundaries in terms of both phenotypic similarity and molecular associations, establishing a rational strategy to reform disease taxonomy.

Indexed as

AlgorithmsGenotypeModels, BiologicalPhenotypeClassificationDiseaseHumansDisease phenotypesDisease taxonomyMolecular profilesNetwork medicinePrecision medicine

Identifiers

PMID29669699
PMCPMC6013753

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.