{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:51Z","timestamp":1772138091756,"version":"3.50.1"},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T00:00:00Z","timestamp":1547510400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"AMED","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Research Program for Brain Sciences","award":["JP18dm0107087"],"award-info":[{"award-number":["JP18dm0107087"]}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"AMED","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Practical Research Project for Rare \/ Intractable Diseases","award":["JP17ek0109281"],"award-info":[{"award-number":["JP17ek0109281"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are connected each other and affect behavior and cognitive processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce a novel and efficient framework, called a \u2018Network of Networks\u2019 approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning, a new sparse structural learning algorithm for network data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that propose Graph-Oriented SParsE Learning outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the \u2018Network of Networks\u2019 model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Our software is available from https:\/\/github.com\/infinite-point\/GOSPEL.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz032","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T07:11:12Z","timestamp":1547190672000},"page":"3092-3101","source":"Crossref","is-referenced-by-count":5,"title":["A network of networks approach for modeling interconnected brain tissue-specific networks"],"prefix":"10.1093","volume":"35","author":[{"given":"Hideko","family":"Kawakubo","sequence":"first","affiliation":[{"name":"Nagoya University Graduate School of Medicine Division of Systems of Biology, , Nagoya, Japan"}]},{"given":"Yusuke","family":"Matsui","sequence":"additional","affiliation":[{"name":"Nagoya University Graduate School of Medicine Laboratory of Intelligence Healthcare, , Nagoya, Japan"}]},{"given":"Itaru","family":"Kushima","sequence":"additional","affiliation":[{"name":"Institute for Advanced Research, Nagoya University Graduate School of Medicine , Nagoya, Japan"},{"name":"Nagoya University Graduate School of Medicine Department of Psychiatry, , Nagoya, Japan"}]},{"given":"Norio","family":"Ozaki","sequence":"additional","affiliation":[{"name":"Nagoya University Graduate School of Medicine Department of Psychiatry, , Nagoya, Japan"},{"name":"Nagoya University Graduate School of Medicine Department of Psychotherapy for parents and children, , Nagoya, Japan"}]},{"given":"Teppei","family":"Shimamura","sequence":"additional","affiliation":[{"name":"Nagoya University Graduate School of Medicine Division of Systems of Biology, , Nagoya, Japan"}]}],"member":"286","published-online":{"date-parts":[[2019,1,15]]},"reference":[{"key":"2023062711314298700_btz032-B1","volume-title":"Diagnostic and Statistical Manual of Mental Disorders: DSM-5","year":"2013"},{"key":"2023062711314298700_btz032-B2","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of scaling in random networks","volume":"286","author":"Barab\u00e1si","year":"1999","journal-title":"Science"},{"key":"2023062711314298700_btz032-B3","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.cels.2016.04.014","article-title":"Systematic functional annotation and visualization of biological networks","volume":"2","author":"Baryshnikova","year":"2016","journal-title":"Cell Syst"},{"key":"2023062711314298700_btz032-B4","volume-title":"Vehicles: Experiments in Synthetic Psycholog","author":"Braitenberg","year":"1986"},{"key":"2023062711314298700_btz032-B5","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1093\/cercor\/bhr084","article-title":"Functional and dysfunctional brain circuits underlying emotional processing of music in autism spectrum disorders","volume":"21","author":"Caria","year":"2011","journal-title":"Cereb. 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