Mining influential genes based on deep learning Article Swipe
Lingpeng Kong
,
Yuanyuan Chen
,
Fengjiao Xu
,
Mingmin Xu
,
Zutan Li
,
Jingya Fang
,
Liangyun Zhang
,
Cong Pian
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1186/s12859-021-03972-5
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1186/s12859-021-03972-5
Background Currently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling method called L1000 was proposed, with which one million profiles were produced. Although a set of ~ 1000 carefully chosen landmark genes that can capture ~ 80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to infer target genes is not satisfactory. Therefore, more efficient computational methods are still needed to deep mine the influential genes in the genome. Results Here, we propose a computational framework based on deep learning to mine a subset of genes that can cover more genomic information. Specifically, an AutoEncoder framework is first constructed to learn the non-linear relationship between genes, and then DeepLIFT is applied to calculate gene importance scores. Using this data-driven approach, we have re-obtained a landmark gene set. The result shows that our landmark genes can predict target genes more accurately and robustly than that of L1000 based on two metrics [mean absolute error (MAE) and Pearson correlation coefficient (PCC)]. This reveals that the landmark genes detected by our method contain more genomic information. Conclusions We believe that our proposed framework is very suitable for the analysis of biological big data to reveal the mysteries of life. Furthermore, the landmark genes inferred from this study can be used for the explosive amplification of gene expression profiles to facilitate research into functional connections.
Related Topics
Concepts
Landmark
Gene
Autoencoder
Computational biology
Genome
Computer science
DNA microarray
Gene expression profiling
Robustness (evolution)
Profiling (computer programming)
Data mining
Artificial intelligence
Deep learning
Biology
Genetics
Gene expression
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12859-021-03972-5
- https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-021-03972-5
- OA Status
- gold
- Cited By
- 11
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3118581792
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3118581792Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s12859-021-03972-5Digital Object Identifier
- Title
-
Mining influential genes based on deep learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-22Full publication date if available
- Authors
-
Lingpeng Kong, Yuanyuan Chen, Fengjiao Xu, Mingmin Xu, Zutan Li, Jingya Fang, Liangyun Zhang, Cong PianList of authors in order
- Landing page
-
https://doi.org/10.1186/s12859-021-03972-5Publisher landing page
- PDF URL
-
https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-021-03972-5Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-021-03972-5Direct OA link when available
- Concepts
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Landmark, Gene, Autoencoder, Computational biology, Genome, Computer science, DNA microarray, Gene expression profiling, Robustness (evolution), Profiling (computer programming), Data mining, Artificial intelligence, Deep learning, Biology, Genetics, Gene expression, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 3, 2022: 3, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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