Evidence map›Paper›PMID 30853547›Full record

ArticleMethods (San Diego, Calif.)2019

Unsupervised classification of multi-omics data during cardiac remodeling using deep learning.

Neo Christopher Chung, Bilal Mirza, Howard Choi, Jie Wang, Ding Wang, Peipei Ping, Wei Wang

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Methods (San Diego, Calif.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
2.7field-weighted citation impact, top 9% of its field
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

24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 56 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Guidelines for mechanistic modeling and analysis in cardiovascular research.American journal of physiology. Heart and circulatory physiology · 2024
    Review
  5. Article
  6. Uncertainty-aware dynamic integration for multi-omics classification of tumors.Journal of cancer research and clinical oncology · 2023
    Article
  7. Article
  8. Article
  9. Review
  10. Big Data in Gastroenterology Research.International journal of molecular sciences · 2023
    Review
  11. Review
  12. Article
  13. Review
  14. Review
  15. Deep Mining from Omics Data.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  16. Article
  17. Review
  18. Review
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Neo Christopher ChungNIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland. Electronic address: nchchung@gmail.com.
Bilal MirzaNIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA.
Howard ChoiNIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA.
Jie WangNIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA.
Ding WangNIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA.
Peipei PingNIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA; Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine (Cardiology), University of California Los Angeles, Los Angeles, CA 90095, USA.
Wei WangNIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA; Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA. Electronic address: weiwang@cs.ucla.edu.
University of California, Los Angeles · USUniversity of Warsaw · PL

Funding

TRAININGU54GM114833 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LINDSEY, MERRY L., PING, PEIPEI · 2014 to 2017
$13.8M
Omics Phenotyping for Identifying Molecular Signatures of the Healthy and Failing Heart: An Integrated Data Science PlatformR35HL135772 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PING, PEIPEI · 2017 to 2023
$6.4M
iDISCOVER: Integrated Data Science Training in CardioVascular MedicineT32HL139450 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUI, ALEX, PING, PEIPEI · 2018 to 2022
$1.3M
Fast and Robust Methods for Large Scale Genotype Phenotype Association StudyR01GM115833 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WANG, WEI · 2015 to 2017
$933k
NHLBI NIH HHS R35 HL135772NHLBI NIH HHS T32 HL139450NIGMS NIH HHS R01 GM115833NIGMS NIH HHS U54 GM114833
6 · The paper itself

Abstract

Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were previously unidentifiable. However, to effectively perform integrative analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)-based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering, partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics.

Indexed as

Deep LearningAlgorithmsCluster AnalysisComputational BiologyHeart VentriclesImage Processing, Computer-AssistedVentricular RemodelingCardiovascularClusteringIntegrative analysisMulti-omicsTime-seriesUnsupervised deep learning

Identifiers

PMID30853547
PMCPMC6708480
OpenAlexW2921144944

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.