A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.
| Title: | A deep neural network approach to predicting clinical outcomes of neuroblastoma patients. |
|---|---|
| Authors: | Tranchevent, Léon-Charles1,2 (AUTHOR), Azuaje, Francisco1,3 (AUTHOR), Rajapakse, Jagath C.4 (AUTHOR) asjagath@ntu.edu.sg |
| Source: | BMC Medical Genomics. 12/20/2019 Supplement 8, Vol. 12, p1-11. 11p. |
| Subject Terms: | *ARTIFICIAL neural networks, *FEATURE extraction, *SUPPORT vector machines, *FEATURE selection, *PROGNOSIS, *DEEP learning |
| Abstract: | Background: The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the "small n large p" problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. Methods: We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients' omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers. Results: We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. Conclusions: Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes. [ABSTRACT FROM AUTHOR] |
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| Items | – Name: Title Label: Title Group: Ti Data: A deep neural network approach to predicting clinical outcomes of neuroblastoma patients. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tranchevent%2C+Léon-Charles%22">Tranchevent, Léon-Charles</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Azuaje%2C+Francisco%22">Azuaje, Francisco</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rajapakse%2C+Jagath+C%2E%22">Rajapakse, Jagath C.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> asjagath@ntu.edu.sg</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22BMC+Medical+Genomics%22">BMC Medical Genomics</searchLink>. 12/20/2019 Supplement 8, Vol. 12, p1-11. 11p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22ARTIFICIAL+neural+networks%22">ARTIFICIAL neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22FEATURE+extraction%22">FEATURE extraction</searchLink><br />*<searchLink fieldCode="DE" term="%22SUPPORT+vector+machines%22">SUPPORT vector machines</searchLink><br />*<searchLink fieldCode="DE" term="%22FEATURE+selection%22">FEATURE selection</searchLink><br />*<searchLink fieldCode="DE" term="%22PROGNOSIS%22">PROGNOSIS</searchLink><br />*<searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the "small n large p" problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. Methods: We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients' omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers. Results: We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. Conclusions: Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of BMC Medical Genomics is the property of BioMed Central and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s12920-019-0628-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: ARTIFICIAL neural networks Type: general – SubjectFull: FEATURE extraction Type: general – SubjectFull: SUPPORT vector machines Type: general – SubjectFull: FEATURE selection Type: general – SubjectFull: PROGNOSIS Type: general – SubjectFull: DEEP learning Type: general Titles: – TitleFull: A deep neural network approach to predicting clinical outcomes of neuroblastoma patients. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tranchevent, Léon-Charles – PersonEntity: Name: NameFull: Azuaje, Francisco – PersonEntity: Name: NameFull: Rajapakse, Jagath C. IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 12 Text: 12/20/2019 Supplement 8 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 17558794 Numbering: – Type: volume Value: 12 Titles: – TitleFull: BMC Medical Genomics Type: main |
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