{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:22:46Z","timestamp":1743049366335,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9781071639887"},{"type":"electronic","value":"9781071639894"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-1-0716-3989-4_15","type":"book-chapter","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T08:02:38Z","timestamp":1715846558000},"page":"235-252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Inferring Metabolic States from\u00a0Single Cell Transcriptomic Data via\u00a0Geometric Deep Learning"],"prefix":"10.1007","author":[{"given":"Holly R.","family":"Steach","sequence":"first","affiliation":[]},{"given":"Siddharth","family":"Viswanath","sequence":"additional","affiliation":[]},{"given":"Yixuan","family":"He","sequence":"additional","affiliation":[]},{"given":"Xitong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Natalia","family":"Ivanova","sequence":"additional","affiliation":[]},{"given":"Matthew","family":"Hirn","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Perlmutter","sequence":"additional","affiliation":[]},{"given":"Smita","family":"Krishnaswamy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Brunk, E., et al.: Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 36(3), 272\u2013281 (2018)","DOI":"10.1038\/nbt.4072"},{"issue":"1","key":"15_CR2","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.acha.2006.04.004","volume":"21","author":"RR Coifman","year":"2006","unstructured":"Coifman, R.R., Maggioni, M.: Diffusion wavelets. Appl. Comput. Harmon. Anal. 21(1), 53\u201394 (2006)","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"15_CR3","unstructured":"Cucuringu, M., Li, H., Sun, H., Zanetti, L.: Hermitian matrices for clustering directed graphs: insights and applications. In: International Conference on Artificial Intelligence and Statistics, pp. 983\u2013992. PMLR (2020)"},{"key":"15_CR4","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, vol. 29, pp. 3844\u20133852 (2016)"},{"key":"15_CR5","doi-asserted-by":"publisher","unstructured":"Fabregat, A., et al.: The reactome pathway knowledgebase. Nucleic Acids Res. 46(D1), D649\u2013D655 (2017). https:\/\/doi.org\/10.1093\/nar\/gkx1132","DOI":"10.1093\/nar\/gkx1132"},{"key":"15_CR6","doi-asserted-by":"publisher","unstructured":"Feist, A.M., Palsson, B.O.: The biomass objective function. Curr. Opin. Microbiol. 13(3), 344\u2013349 (2010). https:\/\/doi.org\/10.1016\/j.mib.2010.03.003","DOI":"10.1016\/j.mib.2010.03.003"},{"key":"15_CR7","unstructured":"Gama, F., Ribeiro, A., Bruna, J.: Diffusion scattering transforms on graphs. In: 7th International Conference on Learning Representations, ICLR 2019 (2019)"},{"key":"15_CR8","unstructured":"Gao, F., Wolf, G., Hirn, M.: Geometric scattering for graph data analysis. In: International Conference on Machine Learning, pp. 2122\u20132131. PMLR (2019)"},{"key":"15_CR9","doi-asserted-by":"publisher","DOI":"10.3389\/fnmol.2021.732120","volume":"14","author":"D Garc\u00eda-Rodr\u00edguez","year":"2021","unstructured":"Garc\u00eda-Rodr\u00edguez, D., Gim\u00e9nez-Cassina, A.: Ketone bodies in the brain beyond fuel metabolism: from excitability to gene expression and cell signaling. Front. Mol. Neurosci. 14, 732120 (2021)","journal-title":"Front. Mol. Neurosci."},{"key":"15_CR10","doi-asserted-by":"publisher","unstructured":"Heirendt, L., et al.: Creation and analysis of biochemical constraint-based models using the COBRA toolbox v.3.0. Nat. Protoc. 14(3), 639\u2013702 (2019). https:\/\/doi.org\/10.1038\/s41596-018-0098-2","DOI":"10.1038\/s41596-018-0098-2"},{"key":"15_CR11","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings (2017)"},{"key":"15_CR12","doi-asserted-by":"publisher","unstructured":"Krishnaswamy, S., et al.: Conditional density-based analysis of t cell signaling in single-cell data. Science 346(6213) (2014). https:\/\/doi.org\/10.1126\/science.1250689","DOI":"10.1126\/science.1250689"},{"key":"15_CR13","unstructured":"Kyoto Encyclopedia of Genes and Genomes (KEGG): Kyoto encyclopedia of genes and genomes. https:\/\/www.genome.jp\/kegg\/"},{"issue":"1","key":"15_CR14","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/TSP.2018.2879624","volume":"67","author":"R Levie","year":"2019","unstructured":"Levie, R., Monti, F., Bresson, X., Bronstein, M.M.: Cayleynets: graph convolutional neural networks with complex rational spectral filters. IEEE Trans. Signal Process. 67(1), 97\u2013109 (2019). https:\/\/doi.org\/10.1109\/TSP.2018.2879624","journal-title":"IEEE Trans. Signal Process."},{"key":"15_CR15","doi-asserted-by":"publisher","unstructured":"Li, F., Chen, Y., Anton, M., Nielsen, J.: Gotenzymes: an extensive database of enzyme parameter predictions. Nucleic Acids Res. 51(D1), D583\u2013D586 (2022). https:\/\/doi.org\/10.1093\/nar\/gkac831","DOI":"10.1093\/nar\/gkac831"},{"key":"15_CR16","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-662-10018-9_28","volume-title":"Statistical Mechanics","author":"EH Lieb","year":"1993","unstructured":"Lieb, E.H., Loss, M.: Fluxes, Laplacians, and Kasteleyn\u2019s theorem. In: Nachtergaele, B., Solovej, J.P., Yngvason, J. (eds.) Statistical Mechanics, pp. 457\u2013483. Springer, Heidelberg (1993). https:\/\/doi.org\/10.1007\/978-3-662-10018-9_28"},{"key":"15_CR17","doi-asserted-by":"publisher","first-page":"2483","DOI":"10.1007\/s00018-019-03430-9","volume":"77","author":"C Maffezzini","year":"2020","unstructured":"Maffezzini, C., Calvo-Garrido, J., Wredenberg, A., Freyer, C.: Metabolic regulation of neurodifferentiation in the adult brain. Cell. Mol. Life Sci. 77, 2483\u20132496 (2020)","journal-title":"Cell. Mol. Life Sci."},{"key":"15_CR18","doi-asserted-by":"publisher","unstructured":"Mattson, M.P., Moehl, K., Ghena, N., Schmaedick, M., Cheng, A.: Intermittent metabolic switching, neuroplasticity and brain health. Nat. Rev. Neurosci. 19(2), 81\u201394 (2018). https:\/\/doi.org\/10.1038\/nrn.2017.156","DOI":"10.1038\/nrn.2017.156"},{"key":"15_CR19","unstructured":"MetaboAnalyst: Metaboanalyst: A comprehensive tool suite for metabolomic data analysis. https:\/\/www.metaboanalyst.ca\/"},{"key":"15_CR20","unstructured":"Min, Y., Wenkel, F., Perlmutter, M., Wolf, G.: Can hybrid geometric scattering networks help solve the maximum clique problem? In: NeurIPS (2022). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/8ec88961d36d9a87ac24baf45402744f-Abstract-Conference.html"},{"key":"15_CR21","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.laa.2019.09.024","volume":"584","author":"B Mohar","year":"2020","unstructured":"Mohar, B.: A new kind of Hermitian matrices for digraphs. Linear Algebra Appl. 584, 343\u2013352 (2020)","journal-title":"Linear Algebra Appl."},{"key":"15_CR22","doi-asserted-by":"publisher","unstructured":"Moon, K.R., et al.: Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37(12), 1482\u20131492 (2019). https:\/\/doi.org\/10.1038\/s41587-019-0336-3","DOI":"10.1038\/s41587-019-0336-3"},{"key":"15_CR23","doi-asserted-by":"publisher","unstructured":"Orth, J.D., Fleming, R.M.T., Palsson, B.\u00d8.: Reconstruction and use of microbial metabolic networks: the core Escherichia coli metabolic model as an educational guide. EcoSal Plus 4(1) (2010). https:\/\/doi.org\/10.1128\/ecosalplus.10.2.1","DOI":"10.1128\/ecosalplus.10.2.1"},{"issue":"3","key":"15_CR24","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/nbt.1614","volume":"28","author":"JD Orth","year":"2010","unstructured":"Orth, J.D., Thiele, I., Palsson, B.\u00d8.: What is flux balance analysis? Nat. Biotechnol. 28(3), 245\u2013248 (2010). https:\/\/doi.org\/10.1038\/nbt.1614","journal-title":"Nat. Biotechnol."},{"key":"15_CR25","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511790515","volume-title":"Systems Biology: Properties of Reconstructed Networks","author":"BO Palsson","year":"2006","unstructured":"Palsson, B.O.: Systems Biology: Properties of Reconstructed Networks. Cambridge University Press, New York (2006)"},{"key":"15_CR26","doi-asserted-by":"publisher","unstructured":"Sahu, A., Bl\u00e4tke, M.A., Szyma\u0144ski, J.J., T\u00f6pfer, N.: Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput. Struct. Biotechnol. J. 19, 4626\u20134640 (2021). https:\/\/doi.org\/10.1016\/j.csbj.2021.08.004","DOI":"10.1016\/j.csbj.2021.08.004"},{"key":"15_CR27","doi-asserted-by":"publisher","unstructured":"Sastry, A.V., et al.: The Escherichia coli transcriptome mostly consists of independently regulated modules. Nat. Commun. 10(1) (2019). https:\/\/doi.org\/10.1038\/s41467-019-13483-w","DOI":"10.1038\/s41467-019-13483-w"},{"key":"15_CR28","doi-asserted-by":"publisher","unstructured":"Schuetz, R., Zamboni, N., Zampieri, M., Heinemann, M., Sauer, U.: Multidimensional optimality of microbial metabolism. Science 336(6081), 601\u2013604 (2012). https:\/\/doi.org\/10.1126\/science.1216882","DOI":"10.1126\/science.1216882"},{"issue":"43","key":"15_CR29","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","volume":"102","author":"A Subramanian","year":"2005","unstructured":"Subramanian, A., et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. 102(43), 15545\u201315550 (2005). https:\/\/doi.org\/10.1073\/pnas.0506580102","journal-title":"Proc. Natl. Acad. Sci."},{"key":"15_CR30","doi-asserted-by":"publisher","unstructured":"Wagner, A., et al.: Metabolic modeling of single TH17 cells reveals regulators of autoimmunity. Cell 184(16), 4168\u20134185.e21 (2021). https:\/\/doi.org\/10.1016\/j.cell.2021.05.045","DOI":"10.1016\/j.cell.2021.05.045"},{"key":"15_CR31","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019 (2019)"},{"issue":"1\u20132","key":"15_CR32","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.neuint.2005.04.014","volume":"47","author":"M Yudkoff","year":"2005","unstructured":"Yudkoff, M., et al.: Response of brain amino acid metabolism to ketosis. Neurochem. Int. 47(1\u20132), 119\u2013128 (2005)","journal-title":"Neurochem. Int."},{"key":"15_CR33","first-page":"27003","volume":"34","author":"X Zhang","year":"2021","unstructured":"Zhang, X., He, Y., Brugnone, N., Perlmutter, M., Hirn, M.: Magnet: a neural network for directed graphs. Adv. Neural. Inf. Process. Syst. 34, 27003\u201327015 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"3","key":"15_CR34","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1016\/j.acha.2019.06.003","volume":"49","author":"D Zou","year":"2020","unstructured":"Zou, D., Lerman, G.: Graph convolutional neural networks via scattering. Appl. Comput. Harmon. Anal. 49(3), 1046\u20131074 (2020). https:\/\/doi.org\/10.1016\/j.acha.2019.06.003","journal-title":"Appl. Comput. Harmon. Anal."}],"container-title":["Lecture Notes in Computer Science","Research in Computational Molecular Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-0716-3989-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T16:06:00Z","timestamp":1728921960000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-1-0716-3989-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9781071639887","9781071639894"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-1-0716-3989-4_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"17 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RECOMB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Research in Computational Molecular Biology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge, MA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"recomb2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/recomb.org\/recomb2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}