Cargando…

Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia

OBJECTIVE: Computing patients’ similarity is of great interest in precision oncology since it supports clustering and subgroup identification, eventually leading to tailored therapies. The availability of large amounts of biomedical data, characterized by large feature sets and sparse content, motiv...

Descripción completa

Detalles Bibliográficos
Autores principales: Vitali, F, Marini, S, Pala, D, Demartini, A, Montoli, S, Zambelli, A, Bellazzi, R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951984/
https://www.ncbi.nlm.nih.gov/pubmed/31984320
http://dx.doi.org/10.1093/jamiaopen/ooy008
_version_ 1783486369719386112
author Vitali, F
Marini, S
Pala, D
Demartini, A
Montoli, S
Zambelli, A
Bellazzi, R
author_facet Vitali, F
Marini, S
Pala, D
Demartini, A
Montoli, S
Zambelli, A
Bellazzi, R
author_sort Vitali, F
collection PubMed
description OBJECTIVE: Computing patients’ similarity is of great interest in precision oncology since it supports clustering and subgroup identification, eventually leading to tailored therapies. The availability of large amounts of biomedical data, characterized by large feature sets and sparse content, motivates the development of new methods to compute patient similarities able to fuse heterogeneous data sources with the available knowledge. MATERIALS AND METHODS: In this work, we developed a data integration approach based on matrix trifactorization to compute patient similarities by integrating several sources of data and knowledge. We assess the accuracy of the proposed method: (1) on several synthetic data sets which similarity structures are affected by increasing levels of noise and data sparsity, and (2) on a real data set coming from an acute myeloid leukemia (AML) study. The results obtained are finally compared with the ones of traditional similarity calculation methods. RESULTS: In the analysis of the synthetic data set, where the ground truth is known, we measured the capability of reconstructing the correct clusters, while in the AML study we evaluated the Kaplan-Meier curves obtained with the different clusters and measured their statistical difference by means of the log-rank test. In presence of noise and sparse data, our data integration method outperform other techniques, both in the synthetic and in the AML data. DISCUSSION: In case of multiple heterogeneous data sources, a matrix trifactorization technique can successfully fuse all the information in a joint model. We demonstrated how this approach can be efficiently applied to discover meaningful patient similarities and therefore may be considered a reliable data driven strategy for the definition of new research hypothesis for precision oncology. CONCLUSION: The better performance of the proposed approach presents an advantage over previous methods to provide accurate patient similarities supporting precision medicine.
format Online
Article
Text
id pubmed-6951984
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-69519842020-01-24 Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia Vitali, F Marini, S Pala, D Demartini, A Montoli, S Zambelli, A Bellazzi, R JAMIA Open Research and Applications OBJECTIVE: Computing patients’ similarity is of great interest in precision oncology since it supports clustering and subgroup identification, eventually leading to tailored therapies. The availability of large amounts of biomedical data, characterized by large feature sets and sparse content, motivates the development of new methods to compute patient similarities able to fuse heterogeneous data sources with the available knowledge. MATERIALS AND METHODS: In this work, we developed a data integration approach based on matrix trifactorization to compute patient similarities by integrating several sources of data and knowledge. We assess the accuracy of the proposed method: (1) on several synthetic data sets which similarity structures are affected by increasing levels of noise and data sparsity, and (2) on a real data set coming from an acute myeloid leukemia (AML) study. The results obtained are finally compared with the ones of traditional similarity calculation methods. RESULTS: In the analysis of the synthetic data set, where the ground truth is known, we measured the capability of reconstructing the correct clusters, while in the AML study we evaluated the Kaplan-Meier curves obtained with the different clusters and measured their statistical difference by means of the log-rank test. In presence of noise and sparse data, our data integration method outperform other techniques, both in the synthetic and in the AML data. DISCUSSION: In case of multiple heterogeneous data sources, a matrix trifactorization technique can successfully fuse all the information in a joint model. We demonstrated how this approach can be efficiently applied to discover meaningful patient similarities and therefore may be considered a reliable data driven strategy for the definition of new research hypothesis for precision oncology. CONCLUSION: The better performance of the proposed approach presents an advantage over previous methods to provide accurate patient similarities supporting precision medicine. Oxford University Press 2018-05-14 /pmc/articles/PMC6951984/ /pubmed/31984320 http://dx.doi.org/10.1093/jamiaopen/ooy008 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Research and Applications
Vitali, F
Marini, S
Pala, D
Demartini, A
Montoli, S
Zambelli, A
Bellazzi, R
Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia
title Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia
title_full Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia
title_fullStr Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia
title_full_unstemmed Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia
title_short Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia
title_sort patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951984/
https://www.ncbi.nlm.nih.gov/pubmed/31984320
http://dx.doi.org/10.1093/jamiaopen/ooy008
work_keys_str_mv AT vitalif patientsimilaritybyjointmatrixtrifactorizationtoidentifysubgroupsinacutemyeloidleukemia
AT marinis patientsimilaritybyjointmatrixtrifactorizationtoidentifysubgroupsinacutemyeloidleukemia
AT palad patientsimilaritybyjointmatrixtrifactorizationtoidentifysubgroupsinacutemyeloidleukemia
AT demartinia patientsimilaritybyjointmatrixtrifactorizationtoidentifysubgroupsinacutemyeloidleukemia
AT montolis patientsimilaritybyjointmatrixtrifactorizationtoidentifysubgroupsinacutemyeloidleukemia
AT zambellia patientsimilaritybyjointmatrixtrifactorizationtoidentifysubgroupsinacutemyeloidleukemia
AT bellazzir patientsimilaritybyjointmatrixtrifactorizationtoidentifysubgroupsinacutemyeloidleukemia