MOUSSE: Multi-Omics Using Subject-Specific SignaturEs
High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases...
Saved in:
| Published in | Cancers Vol. 13; no. 14; p. 3423 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
08.07.2021
MDPI |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival. |
|---|---|
| AbstractList | High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival. Simple SummaryModern profiling technologies have led to relevant progress toward precision medicine and disease management. A new trend in patient classification is to integrate multiple data types for the same subjects to increase the chance of identifying meaningful phenotype groups. However, these methodologies are still in their infancy, with their performance varying widely depending on the biological conditions analyzed. We developed MOUSSE, a new unsupervised and normalization-free tool for multi-omics integration able to maintain good clustering performance across a wide range of omics data. We verified its efficiency in clustering patients based on survival for ten different cancer types. The results we obtained show a higher average score in classification performance than ten other state-of-the-art algorithms. We have further validated the method by identifying a list of biological features potentially involved in patient survival, finding a high degree of concordance with the literature.AbstractHigh-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival. High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival. |
| Author | Marchetti, Luca Visintainer, Roberto Fiorentino, Giuseppe Lauria, Mario Domenici, Enrico |
| AuthorAffiliation | 1 Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; fiorentino@cosbi.eu (G.F.); visintainer@cosbi.eu (R.V.); domenici@cosbi.eu (E.D.); lauria@cosbi.eu (M.L.) 2 Department of Cellular, Computational, and Integrative Biology (CiBio), University of Trento, 38123 Povo, Italy 3 Department of Mathematics, University of Trento, 38123 Povo, Italy |
| AuthorAffiliation_xml | – name: 1 Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; fiorentino@cosbi.eu (G.F.); visintainer@cosbi.eu (R.V.); domenici@cosbi.eu (E.D.); lauria@cosbi.eu (M.L.) – name: 3 Department of Mathematics, University of Trento, 38123 Povo, Italy – name: 2 Department of Cellular, Computational, and Integrative Biology (CiBio), University of Trento, 38123 Povo, Italy |
| Author_xml | – sequence: 1 givenname: Giuseppe orcidid: 0000-0001-9513-608X surname: Fiorentino fullname: Fiorentino, Giuseppe – sequence: 2 givenname: Roberto surname: Visintainer fullname: Visintainer, Roberto – sequence: 3 givenname: Enrico orcidid: 0000-0001-7436-6919 surname: Domenici fullname: Domenici, Enrico – sequence: 4 givenname: Mario orcidid: 0000-0001-5983-7292 surname: Lauria fullname: Lauria, Mario – sequence: 5 givenname: Luca orcidid: 0000-0001-9043-7705 surname: Marchetti fullname: Marchetti, Luca |
| BookMark | eNp1kctLAzEQxoNUbK09ey148bI272Q9CFLqA1p6WHsO2Wy2pmyzNdkV_O_d0iJacC4zML_vYx6XoOdrbwG4RvCOkBROjPbGhogIooRicgYGGAqccJ7S3q-6D0YxbmAXhCDBxQXod3gqOUUDwBbLVZbN7seLtmpcstw6E8er6Px6nLX5xpomyXbWuNKZcebWXjdtmMUrcF7qKtrRMQ_B6mn2Nn1J5svn1-njPDEklU3CMWdMUlzCnAiNcksKaVNcpAWyHMFSyILLokyJESlGguS8REKyomDaSmJLMgQPB99dm29tYaxvgq7ULritDl-q1k797Xj3rtb1p5IEUoF5Z3B7NAj1R2tjo7YuGltV2tu6jQozxhDkmKEOvTlBN3UbfLfenqKUI9odeQgmB8qEOsZgy59hEFT7r6iTr3QKdqIwrtGNq_cTu-pf3TfNvJHI |
| CitedBy_id | crossref_primary_10_3390_medsci11030044 crossref_primary_10_3389_fmolb_2022_962799 crossref_primary_10_3390_info14090513 |
| Cites_doi | 10.1038/srep41942 10.1007/s40291-015-0148-1 10.1101/gr.215129.116 10.3389/fonc.2018.00374 10.1214/aoms/1177729694 10.7717/peerj.8312 10.1016/j.cell.2018.02.052 10.1159/000327947 10.1093/bioinformatics/btz058 10.18632/oncotarget.11732 10.1080/15384101.2018.1534511 10.1038/nmeth.2016 10.1016/j.compbiolchem.2019.107120 10.3390/cancers11010053 10.3390/ht8010004 10.1093/nar/gky889 10.1016/j.gene.2020.144648 10.1016/j.bbagrm.2016.04.005 10.1137/1.9781611972832.28 10.1038/s41434-018-0029-4 10.1039/C5SC02902C 10.1007/s12263-015-0508-9 10.3389/fgene.2017.00084 10.1093/biostatistics/kxx017 10.1007/s00109-011-0775-x 10.1039/C7MO00051K 10.1158/0008-5472.CAN-11-2632 10.1371/journal.pone.0194225 10.1038/bjc.2017.164 10.1038/nmeth.2810 10.1371/journal.pone.0182559 10.1093/nar/gkv449 10.1007/s00521-013-1439-2 10.5114/wo.2014.47136 10.1186/s12859-015-0857-9 10.1038/nrg3868 10.1016/j.bbrc.2015.03.088 10.1186/s13046-019-1311-z 10.1093/bioinformatics/bty235 10.12659/MSM.916648 10.1016/j.omtn.2019.09.033 10.1007/s12035-014-9079-9 10.1016/j.euroneuro.2019.11.004 10.1007/s13402-017-0355-3 10.4161/sysb.26324 10.18632/oncotarget.24897 10.1145/1852102.1852106 10.1093/nar/gkaa1027 10.1214/12-AOAS597 10.1093/bioinformatics/btv244 10.1186/s12864-015-2223-8 10.1016/j.bbrc.2013.03.021 10.1155/2017/3017632 10.1038/srep32583 10.1073/pnas.1208949110 10.2202/1544-6115.1470 10.1093/bioinformatics/btt492 10.1038/s41386-018-0117-6 10.1002/stem.15 10.1093/bib/bbx167 10.1186/s40364-020-0186-7 10.1074/jbc.M109.095950 |
| ContentType | Journal Article |
| Copyright | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 by the authors. 2021 |
| Copyright_xml | – notice: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2021 by the authors. 2021 |
| DBID | AAYXX CITATION 3V. 7T5 7TO 7XB 8FE 8FH 8FK 8G5 ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ GUQSH H94 HCIFZ LK8 M2O M7P MBDVC PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM |
| DOI | 10.3390/cancers13143423 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Immunology Abstracts Oncogenes and Growth Factors Abstracts ProQuest Central (purchase pre-March 2016) ProQuest SciTech Collection ProQuest Natural Science Journals ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Research Library ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection Text complet a ProQuest Central Natural Science Collection ProQuest One ProQuest Central ProQuest Central Student ProQuest Research Library AIDS and Cancer Research Abstracts ProQuest SciTech Premium Collection Biological Sciences Research Library Biological Science Database Research Library (Corporate) ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef Publicly Available Content Database Research Library Prep ProQuest Central Student Oncogenes and Growth Factors Abstracts ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Natural Science Collection ProQuest Central Korea Biological Science Collection AIDS and Cancer Research Abstracts ProQuest Research Library ProQuest Central (New) ProQuest Biological Science Collection ProQuest Central Basic ProQuest One Academic Eastern Edition Biological Science Database ProQuest SciTech Collection ProQuest One Academic UKI Edition Immunology Abstracts ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2072-6694 |
| ExternalDocumentID | PMC8304726 10_3390_cancers13143423 |
| GroupedDBID | --- 53G 5VS 8FE 8FH 8G5 AADQD AAFWJ AAYXX ABDBF ABUWG ACUHS ADBBV AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ CCPQU CITATION DIK DWQXO E3Z EBD ESX GNUQQ GUQSH GX1 HCIFZ HYE IAO IHR ITC KQ8 LK8 M2O M48 M7P MODMG M~E OK1 P6G PGMZT PHGZM PHGZT PIMPY PQQKQ PROAC RPM TUS 3V. 7T5 7TO 7XB 8FK H94 MBDVC PKEHL PQEST PQGLB PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c398t-62655842f0b37a1be3d8e92d9d1e610f78d68df93c792173b6f1785dd5ae83ef3 |
| IEDL.DBID | M48 |
| ISSN | 2072-6694 |
| IngestDate | Thu Aug 21 18:17:32 EDT 2025 Fri Jul 11 11:50:17 EDT 2025 Fri Jul 25 12:15:03 EDT 2025 Tue Jul 01 01:27:55 EDT 2025 Thu Apr 24 22:59:54 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 14 |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c398t-62655842f0b37a1be3d8e92d9d1e610f78d68df93c792173b6f1785dd5ae83ef3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-7436-6919 0000-0001-9513-608X 0000-0001-5983-7292 0000-0001-9043-7705 |
| OpenAccessLink | https://www.proquest.com/docview/2554461442?pq-origsite=%requestingapplication% |
| PMID | 34298641 |
| PQID | 2554461442 |
| PQPubID | 2032421 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_8304726 proquest_miscellaneous_2555106251 proquest_journals_2554461442 crossref_primary_10_3390_cancers13143423 crossref_citationtrail_10_3390_cancers13143423 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20210708 |
| PublicationDateYYYYMMDD | 2021-07-08 |
| PublicationDate_xml | – month: 7 year: 2021 text: 20210708 day: 8 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Cancers |
| PublicationYear | 2021 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Sahu (ref_64) 2016; 1859 Laks (ref_67) 2009; 27 Liu (ref_68) 2018; 173 Mo (ref_9) 2013; 110 Webber (ref_24) 2010; 28 Rappoport (ref_34) 2019; 35 Jia (ref_35) 2014; 24 Tian (ref_42) 2018; 25 Marchetti (ref_16) 2020; 31 Kim (ref_62) 2010; 285 Wani (ref_7) 2019; 83 ref_19 ref_18 Tarca (ref_38) 2013; 29 Liu (ref_56) 2019; 25 Bauer (ref_63) 2011; 27 Songsheng (ref_49) 2016; 7 Hu (ref_51) 2016; 20 Witten (ref_31) 2009; 8 Yang (ref_41) 2011; 89 Nguyen (ref_29) 2017; 27 ref_60 Carboni (ref_17) 2018; 43 Hoeng (ref_15) 2013; 1 ref_25 Fawzy (ref_54) 2015; 19 Wang (ref_47) 2016; 7 Nouri (ref_37) 2018; 34 ref_20 Ochoa (ref_69) 2021; 49 ref_28 ref_27 Li (ref_50) 2015; 460 ref_26 Kullback (ref_23) 1951; 22 Bersanelli (ref_4) 2016; 17 Takashima (ref_58) 2018; 9 Tini (ref_13) 2017; 20 Marbach (ref_39) 2012; 9 Wang (ref_11) 2014; 11 ref_36 Huang (ref_5) 2017; 8 Manvati (ref_40) 2020; 747 ref_32 Xin (ref_48) 2018; 16 Dougherty (ref_66) 2012; 72 Kim (ref_1) 2017; 14 Miao (ref_52) 2017; 7 Lacroix (ref_22) 2015; 10 Lauria (ref_14) 2015; 43 Ritchie (ref_6) 2015; 16 Rui (ref_44) 2020; 8 Lan (ref_53) 2018; 41 Jin (ref_65) 2013; 433 Zhou (ref_43) 2017; 117 Tang (ref_55) 2018; 8 Deng (ref_61) 2019; 38 Speicher (ref_30) 2015; 31 Zhang (ref_46) 2019; 18 Rappoport (ref_12) 2018; 46 Caberlotto (ref_21) 2016; 6 Mo (ref_33) 2018; 19 Zhang (ref_45) 2016; 53 Tomczak (ref_3) 2015; 1A ref_2 ref_8 Dai (ref_59) 2018; 17 Lock (ref_10) 2013; 7 Xiao (ref_57) 2020; 8 |
| References_xml | – volume: 7 start-page: 41942 year: 2017 ident: ref_52 article-title: MicroRNA-130b targets PTEN to mediate drug resistance and proliferation of breast cancer cells via the PI3K/Akt signaling pathway publication-title: Sci. Rep. doi: 10.1038/srep41942 – volume: 19 start-page: 213 year: 2015 ident: ref_54 article-title: Circulating miRNA-122, miRNA-199a, and miRNA-16 as Biomarkers for Early Detection of Hepatocellular Carcinoma in Egyptian Patients with Chronic Hepatitis C Virus Infection publication-title: Mol. Diagn. Ther. doi: 10.1007/s40291-015-0148-1 – volume: 27 start-page: 2025 year: 2017 ident: ref_29 article-title: A novel approach for data integration and disease subtyping publication-title: Genome Res. doi: 10.1101/gr.215129.116 – volume: 16 start-page: 1439 year: 2018 ident: ref_48 article-title: miR-204 functions as a tumor suppressor gene, at least partly by suppressing CYP27A1 in glioblastoma publication-title: Oncol. Lett. – volume: 8 start-page: 374 year: 2018 ident: ref_55 article-title: Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis publication-title: Front. Oncol. doi: 10.3389/fonc.2018.00374 – volume: 22 start-page: 79 year: 1951 ident: ref_23 article-title: “On Information and Sufficiency,” The Annals of Mathematical Statistics publication-title: Ann. Math. Statist. doi: 10.1214/aoms/1177729694 – ident: ref_26 – volume: 8 start-page: e8312 year: 2020 ident: ref_57 article-title: Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma publication-title: PeerJ doi: 10.7717/peerj.8312 – volume: 173 start-page: 400 year: 2018 ident: ref_68 article-title: Abstract 3287: An integrated TCGA pan-cancer clinical data resource to drive high quality survival outcome analytics publication-title: Cell doi: 10.1016/j.cell.2018.02.052 – volume: 27 start-page: 217 year: 2011 ident: ref_63 article-title: Inhibition of Collagen XVI Expression Reduces Glioma Cell Invasiveness publication-title: Cell. Physiol. Biochem. doi: 10.1159/000327947 – volume: 35 start-page: 3348 year: 2019 ident: ref_34 article-title: NEMO: Cancer subtyping by integration of partial multi-omic data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz058 – volume: 7 start-page: 70058 year: 2016 ident: ref_49 article-title: miR-204 suppresses the development and progression of human glioblastoma by targeting ATF2 publication-title: Oncotarget doi: 10.18632/oncotarget.11732 – volume: 17 start-page: 2386 year: 2018 ident: ref_59 article-title: EXO1 overexpression is associated with poor prognosis of hepatocellular carcinoma patients publication-title: Cell Cycle doi: 10.1080/15384101.2018.1534511 – volume: 9 start-page: 796 year: 2012 ident: ref_39 article-title: Wisdom of crowds for robust gene network inference publication-title: Nat. Methods doi: 10.1038/nmeth.2016 – volume: 83 start-page: 107120 year: 2019 ident: ref_7 article-title: Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2019.107120 – ident: ref_60 doi: 10.3390/cancers11010053 – ident: ref_8 doi: 10.3390/ht8010004 – volume: 46 start-page: 10546 year: 2018 ident: ref_12 article-title: Multi-omic and multi-view clustering algorithms: Review and cancer benchmark publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky889 – volume: 747 start-page: 144648 year: 2020 ident: ref_40 article-title: Association of miR-760 with cancer: An overview publication-title: Gene doi: 10.1016/j.gene.2020.144648 – volume: 1859 start-page: 833 year: 2016 ident: ref_64 article-title: TOX3 regulates neural progenitor identity publication-title: Biochim. Biophys. Acta Gene Regul. Mech. doi: 10.1016/j.bbagrm.2016.04.005 – ident: ref_32 doi: 10.1137/1.9781611972832.28 – ident: ref_27 – volume: 25 start-page: 312 year: 2018 ident: ref_42 article-title: Downreguation of FoxM1 by miR-214 inhibits proliferation and migration in hepatocellular carcinoma publication-title: Gene Ther. doi: 10.1038/s41434-018-0029-4 – volume: 7 start-page: 1440 year: 2016 ident: ref_47 article-title: N6-Methyladenine hinders RNA- and DNA-directed DNA synthesis: Application in human rRNA methylation analysis of clinical specimens publication-title: Chem. Sci. doi: 10.1039/C5SC02902C – volume: 10 start-page: 1 year: 2015 ident: ref_22 article-title: Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes publication-title: Genes Nutr. doi: 10.1007/s12263-015-0508-9 – volume: 20 start-page: 5002 year: 2016 ident: ref_51 article-title: miR-760 mediates chemoresistance through inhibition of epithelial mesenchymal transition in breast cancer cells publication-title: Eur. Rev. Med. Pharmacol. Sci. – volume: 8 start-page: 84 year: 2017 ident: ref_5 article-title: More Is Better: Recent Progress in Multi-Omics Data Integration Methods publication-title: Front. Genet. doi: 10.3389/fgene.2017.00084 – volume: 19 start-page: 71 year: 2018 ident: ref_33 article-title: A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data publication-title: Biostatistics doi: 10.1093/biostatistics/kxx017 – volume: 89 start-page: 1037 year: 2011 ident: ref_41 article-title: MiR-106a inhibits glioma cell growth by targeting E2F1 independent of p53 status publication-title: J. Mol. Med. doi: 10.1007/s00109-011-0775-x – volume: 14 start-page: 8 year: 2017 ident: ref_1 article-title: Data integration and predictive modeling methods for multi-omics datasets publication-title: Mol. Omics doi: 10.1039/C7MO00051K – volume: 72 start-page: 4856 year: 2012 ident: ref_66 article-title: Candidate Pathways for Promoting Differentiation or Quiescence of Oligodendrocyte Progenitor-like Cells in Glioma publication-title: Cancer Res. doi: 10.1158/0008-5472.CAN-11-2632 – ident: ref_18 doi: 10.1371/journal.pone.0194225 – volume: 117 start-page: 233 year: 2017 ident: ref_43 article-title: MicroRNA-199b-5p attenuates TGF-β1-induced epithelial–mesenchymal transition in hepatocellular carcinoma publication-title: Br. J. Cancer doi: 10.1038/bjc.2017.164 – volume: 11 start-page: 333 year: 2014 ident: ref_11 article-title: Similarity network fusion for aggregating data types on a genomic scale publication-title: Nat. Methods doi: 10.1038/nmeth.2810 – ident: ref_20 doi: 10.1371/journal.pone.0182559 – volume: 43 start-page: W188 year: 2015 ident: ref_14 article-title: SCUDO: A tool for signature-based clustering of expression profiles publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv449 – volume: 24 start-page: 1477 year: 2014 ident: ref_35 article-title: The latest research progress on spectral clustering publication-title: Neural Comput. Appl. doi: 10.1007/s00521-013-1439-2 – volume: 1A start-page: A68 year: 2015 ident: ref_3 article-title: The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge publication-title: Wspolczesna Onkol. doi: 10.5114/wo.2014.47136 – volume: 17 start-page: 167 year: 2016 ident: ref_4 article-title: Methods for the integration of multi-omics data: Mathematical aspects publication-title: BMC Bioinform. doi: 10.1186/s12859-015-0857-9 – volume: 16 start-page: 85 year: 2015 ident: ref_6 article-title: Methods of integrating data to uncover genotype–phenotype interactions publication-title: Nat. Rev. Genet. doi: 10.1038/nrg3868 – volume: 460 start-page: 670 year: 2015 ident: ref_50 article-title: miR-340 inhibits glioblastoma cell proliferation by suppressing CDK6, cyclin-D1 and cyclin-D2 publication-title: Biochem. Biophys. Res. Commun. doi: 10.1016/j.bbrc.2015.03.088 – volume: 38 start-page: 316 year: 2019 ident: ref_61 article-title: Nek2 augments sorafenib resistance by regulating the ubiquitination and localization of β-catenin in hepatocellular carcinoma publication-title: J. Exp. Clin. Cancer Res. doi: 10.1186/s13046-019-1311-z – volume: 34 start-page: i341 year: 2018 ident: ref_37 article-title: A spectral clustering-based method for identifying clones from high-throughput B cell repertoire sequencing data publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty235 – volume: 25 start-page: 3624 year: 2019 ident: ref_56 article-title: Identification of SEC61G as a Novel Prognostic Marker for Predicting Survival and Response to Therapies in Patients with Glioblastoma publication-title: Med. Sci. Monit. doi: 10.12659/MSM.916648 – volume: 18 start-page: 871 year: 2019 ident: ref_46 article-title: LncRNA LINC00963 Promotes Tumorigenesis and Radioresistance in Breast Cancer by Sponging miR-324-3p and Inducing ACK1 Expression publication-title: Mol. Ther. Nucleic Acids doi: 10.1016/j.omtn.2019.09.033 – volume: 53 start-page: 1452 year: 2016 ident: ref_45 article-title: Plasma miR-221/222 Family as Novel Descriptive and Prognostic Biomarkers for Glioma publication-title: Mol. Neurobiol. doi: 10.1007/s12035-014-9079-9 – volume: 31 start-page: 69 year: 2020 ident: ref_16 article-title: Gene expression signature of antidepressant treatment response/non-response in Flinders Sensitive Line rats subjected to maternal separation publication-title: Eur. Neuropsychopharmacol. doi: 10.1016/j.euroneuro.2019.11.004 – volume: 41 start-page: 25 year: 2018 ident: ref_53 article-title: Serum exosomal miR-301a as a potential diagnostic and prognostic biomarker for human glioma publication-title: Cell. Oncol. doi: 10.1007/s13402-017-0355-3 – volume: 1 start-page: 193 year: 2013 ident: ref_15 article-title: sbv IMPROVER Diagnostic Signature Challenge publication-title: Syst. Biomed. doi: 10.4161/sysb.26324 – volume: 9 start-page: 19065 year: 2018 ident: ref_58 article-title: Correlation between lower balance of Th2 helper T-cells and expression of PD-L1/PD-1 axis genes enables prognostic prediction in patients with glioblastoma publication-title: Oncotarget doi: 10.18632/oncotarget.24897 – volume: 28 start-page: 1 year: 2010 ident: ref_24 article-title: A similarity measure for indefinite rankings publication-title: ACM Trans. Inf. Syst. doi: 10.1145/1852102.1852106 – volume: 49 start-page: D1302 year: 2021 ident: ref_69 article-title: Open Targets Platform: Supporting systematic drug–target identification and prioritisation publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkaa1027 – volume: 7 start-page: 523 year: 2013 ident: ref_10 article-title: Joint and individual variation explained (JIVE) for integrated analysis of multiple data types publication-title: Ann. Appl. Stat. doi: 10.1214/12-AOAS597 – ident: ref_25 – volume: 31 start-page: i268 year: 2015 ident: ref_30 article-title: Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery publication-title: Bioinformatics doi: 10.1093/bioinformatics/btv244 – ident: ref_28 doi: 10.1186/s12864-015-2223-8 – ident: ref_2 – volume: 433 start-page: 496 year: 2013 ident: ref_65 article-title: Cell surface Nestin is a biomarker for glioma stem cells publication-title: Biochem. Biophys. Res. Commun. doi: 10.1016/j.bbrc.2013.03.021 – ident: ref_19 doi: 10.1155/2017/3017632 – volume: 6 start-page: 32583 year: 2016 ident: ref_21 article-title: Integration of transcriptomic and genomic data suggests candidate mechanisms for APOE4-mediated pathogenic action in Alzheimer’s disease publication-title: Sci. Rep. doi: 10.1038/srep32583 – volume: 110 start-page: 4245 year: 2013 ident: ref_9 article-title: Pattern discovery and cancer gene identification in integrated cancer genomic data publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1208949110 – volume: 8 start-page: 28 year: 2009 ident: ref_31 article-title: Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data publication-title: Stat. Appl. Genet. Mol. Biol. doi: 10.2202/1544-6115.1470 – volume: 29 start-page: 2892 year: 2013 ident: ref_38 article-title: Strengths and limitations of microarray-based phenotype prediction: Lessons learned from the IMPROVER Diagnostic Signature Challenge publication-title: Bioinformatics doi: 10.1093/bioinformatics/btt492 – volume: 43 start-page: 2134 year: 2018 ident: ref_17 article-title: Cross-species evidence from human and rat brain transcriptome for growth factor signaling pathway dysregulation in major depression publication-title: Neuropsychopharmacology doi: 10.1038/s41386-018-0117-6 – volume: 27 start-page: 980 year: 2009 ident: ref_67 article-title: Neurosphere Formation Is an Independent Predictor of Clinical Outcome in Malignant Glioma publication-title: Stem Cells doi: 10.1002/stem.15 – volume: 20 start-page: 1269 year: 2017 ident: ref_13 article-title: Multi-omics integration—a comparison of unsupervised clustering methodologies publication-title: Briefings Bioinform. doi: 10.1093/bib/bbx167 – ident: ref_36 – volume: 8 start-page: 7 year: 2020 ident: ref_44 article-title: The mir-767-105 cluster: A crucial factor related to the poor prognosis of hepatocellular carcinoma publication-title: Biomark. Res. doi: 10.1186/s40364-020-0186-7 – volume: 285 start-page: 25957 year: 2010 ident: ref_62 article-title: Cancer/testis antigen CAGE exerts negative regulation on p53 expression through HDAC2 and confers resistance to anti-cancer drugs publication-title: J. Biol. Chem. doi: 10.1074/jbc.M109.095950 |
| SSID | ssj0000331767 |
| Score | 2.251588 |
| Snippet | High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics,... Simple SummaryModern profiling technologies have led to relevant progress toward precision medicine and disease management. A new trend in patient... |
| SourceID | pubmedcentral proquest crossref |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 3423 |
| SubjectTerms | Algorithms Biomarkers Cancer Classification Clustering Data analysis Datasets Gene expression Genomes Integration Medical prognosis Metabolomics Molecular modelling Phenotypes Pipelines Precision medicine Proteomics Survival Survival analysis |
| SummonAdditionalLinks | – databaseName: Text complet a ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LS8NAEF60BfEiPrFaJYIHL6tJNo9dL6KSUoS2Yi30FrIvLWham_b_O5tuUyvoeTcszGxmvnnsNwhdhpnmrowU9lUcY8Moh2lGXMxlQJnLJOPMvB3udKP2IHgahkObcCtsW-XSJpaGWo6FyZHfAPSFyAXgv383-cJmapSprtoRGpuoDiaYQvBVf0i6zy9VlsUl4B-jeMHpQyC-vxFGmNPCIwAUAp-su6MVxlzvkPzhclq7aMdiRed-odw9tKHyfbTVsdXwAxR2eoN-P7l1yle0uPc5EoVT9gA4YA9MggWX4-X1SDj90VvJ4ZkUh2jQSl4f29iOQcCCMDrDEHKEABN87XISZx5XRFLFfMmkpwD86JjKiErNiIgZBBiER9qLaShlmClKlCZHqJaPc3WMHClYIFwFeuBuAOvc05TpkHKSKZ4FtIGul9JIheUIN6MqPlKIFYz40l_ia6Cr6oPJgh7j763NpXhT-58U6UqrDXRRLcMNN2WLLFfjebkHDAfEaV4DxWtqqY40HNnrK_noveTKpqas6Ecn_x9-irZ906ti0ra0iWqz6VydAdiY8XN7o74BfHHW9Q priority: 102 providerName: ProQuest |
| Title | MOUSSE: Multi-Omics Using Subject-Specific SignaturEs |
| URI | https://www.proquest.com/docview/2554461442 https://www.proquest.com/docview/2555106251 https://pubmed.ncbi.nlm.nih.gov/PMC8304726 |
| Volume | 13 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3fS8MwED50gvgi_sTpHBV88CXaNm2TCCIqUxGm4hzsrTRNooNZdZug_72XrJtOFHxOmsJdkvu-5PIdwG6cGemrRJNQM0asohzhGfWJVBEXvlBCCvt2uHmdXLajq07c-SoHVBpw8Cu1s_Wk2v3e_vvrxzEu-CPLOJGyH-TWPv1BQDH2IzqYhTkMS8yu0maJ9d22TDFUuoqyoc9CkiQiGkn9_DbGdJT6gp7TiZPfItH5EiyWENI7Gfl8GWZ0sQLzzfKSfBXi5k271Woceu5xLbl56uYDz6UGeLhN2HMX4qrOm27utboPTtqzMViD9nnj_uySlNURSE4FHxJkIjGih9D4krIskJoqrkWohAo0YiLDuEq4MoLmTCDvoDIxAeOxUnGmOdWGrkOleC70BngqF1Hua3SP9CNsl4HhwsRc0kzLLOJV2B9bI81L6XBbwaKXIoWw5kt_mK8Ke5MPXkaqGX93rY3Nm469nyLPQZqKXC-sws6kGSe-vc3ICv385vrgfoL0LagCm3LL5JdWOnu6peg-Ogltbm8bw2TzH6NvwUJo81jskS6vQWXYf9PbCESGsg5zp43r27s6zF50grqbbp80y9_Z |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1ZT9tAEB7RILV9qYC2aiiHkajUly32ro_dSghxBIUjATVE4s31XiVS61AchPhT_EZmHTsQJHjjeddeaWZ25ptjZwDWo8xKX8eGUJMkxHWUIzxjPpE65MIXWkjh3g53unG7Hx6eR-czcFe_hXFllbVOLBW1HioXI99A6IueC8J_unX5n7ipUS67Wo_QGIvFkbm9QZet2DzYQ_5-o3S_dbbbJtVUAaKY4COCCD5Cq0utL1mSBdIwzY2gWujAIJawCdcx11YwlQjE60zG1k2w1zrKDGfGMvzvG5gNGboyDZjdaXVPf02iOj5Dexwn4x5CjAl_QznmXRUBQ2ASUjZt_h4w7XRF5iMTtz8HHyps6m2PhWkeZky-AG87Vfb9I0Sdk36v1_rpla92ycm_gSq8subAQ_3jAjqkHGdvB8rrDf6UPUNbxSfovwqBPkMjH-bmC3haiVD5Bvku_RDXZWC5sBGXLDMyC3kTftTUSFXVk9yNxvibom_iyJc-IV8Tvk8-uBy343h-61JN3rS6l0X6IEVNWJss441yaZIsN8Prcg8qKvQLgyYkU2yZHOl6ck-v5IOLsjc3d2lMGi--fPgqvGufdY7T44Pu0Vd4T12djAsZ8yVojK6uzTICnZFcqaTLg9-vLdD3guMTmw |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3dTxNBEJ9ASQgvRlRCEfVIIPFl7d7ufeyaGKPSBoQWYmnC23H7pU30ilyJ8V_zr2P2elcsCbzxvHu3yezszG--AXbj3ClqEkuYTVPiO8oRkXNKlImEpNJIJX3tcH-QHIyir-fx-RL8a2phfFplIxMrQW0m2vvIOwh90XJB-M86rk6LON3vfbz8TfwEKR9pbcZpzFjkyP79g-Zb-eFwH-96j7Fe9-zLAaknDBDNpZgSRPMxamDmqOJpHirLjbCSGWlCi7jCpcIkwjjJdSoRu3OVOD_N3pg4t4Jbx_G_y7CSolVEW7DyuTs4_Tb38FCOujlJZ_2EOJe0o_1FXpUhR5ASMb6oCm_x7WJ25n_qrvcUntQ4Nfg0Y6x1WLLFM1jt15H45xD3T0bDYfd9UFXwkpNfY10GVf5BgLLIO3dINdrejXUwHH-v-od2yxcwehQCbUCrmBR2EwKjZaSpRR5QNMJ1FTohXSwUz63KI9GGdw01Ml33J_djMn5maKd48mV3yNeGt_MPLmetOe7fut2QN6vfaJndclQbdubL-Lp8yCQv7OS62oNCC23EsA3pwrXMj_T9uRdXivGPqk-38CFNlmw9fPgbWEVGzo4PB0cvYY35lBnvPRbb0JpeXdtXiHmm6nXNXAFcPDY_3wD7LBfQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=MOUSSE%3A+Multi-Omics+Using+Subject-Specific+SignaturEs&rft.jtitle=Cancers&rft.au=Fiorentino%2C+Giuseppe&rft.au=Visintainer%2C+Roberto&rft.au=Domenici%2C+Enrico&rft.au=Lauria%2C+Mario&rft.date=2021-07-08&rft.issn=2072-6694&rft.eissn=2072-6694&rft.volume=13&rft.issue=14&rft_id=info:doi/10.3390%2Fcancers13143423&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-6694&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-6694&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-6694&client=summon |