Volume 13 Issue 8
Aug.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Pharmaceutical Analysis  > 2023  >  13(8): 836-850
Lance M. O'Connor, Blake A. O'Connor, Su Bin Lim, Jialiu Zeng, Chih Hung Lo. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective[J]. Journal of Pharmaceutical Analysis, 2023, 13(8): 836-850. doi: 10.1016/j.jpha.2023.06.011
Citation: Lance M. O'Connor, Blake A. O'Connor, Su Bin Lim, Jialiu Zeng, Chih Hung Lo. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective[J]. Journal of Pharmaceutical Analysis, 2023, 13(8): 836-850. doi: 10.1016/j.jpha.2023.06.011
shu

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

doi: 10.1016/j.jpha.2023.06.011

  • a. College of Biological Sciences, University of Minnesota, Minneapolis, MN, 55455, USA;

  • b. School of Pharmacy, University of Wisconsin, Madison, WI, 53705, USA;

  • c. Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, 16499, South Korea;

  • d. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore

Funds:

Chih Hung Lo is supported by a Lee Kong Chian School of Medicine Dean’s Postdoctoral Fellowship (021207-00001) from Nanyang Technological University (NTU) Singapore and a Mistletoe Research Fellowship (022522-00001) from the Momental Foundation USA. Jialiu Zeng is supported by a Presidential Postdoctoral Fellowship (021229-00001) from NTU Singapore and an Open Fund Young Investigator Research Grant (OF-YIRG) (MOH-001147) from the National Medical Research Council (NMRC) Singapore. Su Bin Lim is supported by the National Research Foundation (NRF) of Korea (Grant Nos.: 2020R1A6A1A03043539, 2020M3A9D8037604, and 2022R1C1C1004756) and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant No.: HR22C1734). The authors thank Jonathan Indajang from Cornell University for proofreading the manuscript.

  • Received Date: Nov. 29, 2022
  • Rev Recd Date: Jun. 20, 2023
    Abstract
  • Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
    • FullText(HTML)
  • loading
    • References(252)
  • J. Gauthier, A.T. Vincent, S.J. Charette, et al., A brief history of bioinformatics, Brief. Bioinform. 20 (2019) 1981-1996.
    V. Svensson, R. Vento-Tormo, S.A. Teichmann, Exponential scaling of single-cell RNA-seq in the past decade, Nat. Protoc. 13 (2018) 599-604.
    D.H. Geschwind, G. Konopka, Neuroscience in the era of functional genomics and systems biology, Nature 461 (2009) 908-915.
    X. Dong, C. Liu, M. Dozmorov, Review of multi-omics data resources and integrative analysis for human brain disorders, Brief. Funct. Genomics 20 (2021) 223-234.
    Y. Hasin, M. Seldin, A. Lusis, Multi-omics approaches to disease, Genome Biol. 18 (2017), 83.
    B. Tasic, Single cell transcriptomics in neuroscience: Cell classification and beyond, Curr. Opin. Neurobiol. 50 (2018) 242-249.
    E. Lein, L.E. Borm, S. Linnarsson, The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing, Science 358 (2017) 64-69.
    W.X. Wang, J.L. Lefebvre, Morphological pseudotime ordering and fate mapping reveal diversification of cerebellar inhibitory interneurons, Nat. Commun. 13 (2022), 3433.
    S.L. Wilson, G.P. Way, W. Bittremieux, et al., Sharing biological data: Why, when, and how, FEBS Lett. 595 (2021) 847-863.
    G.M. Shepherd, J.S. Mirsky, M.D. Healy, et al., The Human Brain Project: Neuroinformatics tools for integrating, searching and modeling multidisciplinary neuroscience data, Trends Neurosci. 21 (1998) 460-468.
    C. Villa, J.H. Yoon, Multi-omics for the understanding of brain diseases, Life 11 (2021), 1202.
    C. Clark, M. Rabl, L. Dayon, et al., The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer’s disease, Front. Aging Neurosci. 14 (2022), 1065904.
    F.V. Greco, A. Pandi, T.J. Erb, et al., Harnessing the central dogma for stringent multi-level control of gene expression, Nat. Commun. 12 (2021), 1738.
    A. Oulas, G. Minadakis, M. Zachariou, et al., Systems Bioinformatics: Increasing precision of computational diagnostics and therapeutics through network-based approaches, Brief. Bioinform. 20 (2019) 806-824.
    S. Grillner, A. Kozlov, J.H. Kotaleski, Integrative neuroscience: Linking levels of analyses, Curr. Opin. Neurobiol. 15 (2005) 614-621.
    T. Schneider-Poetsch, M. Yoshida, Along the central dogma-controlling gene expression with small molecules, Annu. Rev. Biochem. 87 (2018) 391-420.
    G. Calabrese, C. Molzahn, T. Mayor, Protein interaction networks in neurodegenerative diseases: From physiological function to aggregation, J. Biol. Chem. 298 (2022), 102062.
    C.H. Lo, J.N. Sachs, The role of wild-type tau in Alzheimer’s disease and related tauopathies, J. Life Sci. (Westlake Village) 2 (2020) 1-17.
    C.H. Lo, Heterogeneous tau oligomers as molecular targets for Alzheimer’s disease and related tauopathies, Biophysica 2 (2022) 440-451.
    C.H. Lo, Recent advances in cellular biosensor technology to investigate tau oligomerization, Bioeng. Transl. Med. 6 (2021), e10231.
    Y. Hou, X. Dan, M. Babbar, et al., Ageing as a risk factor for neurodegenerative disease, Nat. Rev. Neurol. 15 (2019) 565-581.
    L. Gan, M.R. Cookson, L. Petrucelli, et al., Converging pathways in neurodegeneration, from genetics to mechanisms, Nat. Neurosci. 21 (2018) 1300-1309.
    R. Ghosh, S.J. Tabrizi, Gene suppression approaches to neurodegeneration, Alzheimer’s Res. Ther. 9 (2017), 82.
    I.A. Qureshi, M.F. Mehler, Advances in epigenetics and epigenomics for neurodegenerative diseases, Curr. Neurol. Neurosci. Rep. 11 (2011) 464-473.
    G. Yu, Q. Su, Y. Chen, et al., Epigenetics in neurodegenerative disorders induced by pesticides, Genes Environ. 43 (2021), 55.
    P. Ghosh, A. Saadat, Neurodegeneration and epigenetics: A review, Neurologia (Engl Ed), 38 (2023) e62-e68.
    F. Coppede, Targeting the epigenome to treat neurodegenerative diseases or delay their onset: A perspective, Neural Regen. Res. 17 (2022) 1745-1747.
    J.Y. Hwang, K.A. Aromolaran, R.S. Zukin, The emerging field of epigenetics in neurodegeneration and neuroprotection, Nat. Rev. Neurosci. 18 (2017) 347-361.
    A. Jowaed, I. Schmitt, O. Kaut, et al., Methylation regulates alpha-synuclein expression and is decreased in Parkinson’s disease patients’ brains, J. Neurosci. 30 (2010) 6355-6359.
    L. Matsumoto, H. Takuma, A. Tamaoka, et al., CpG demethylation enhances alpha-synuclein expression and affects the pathogenesis of Parkinson’s disease, PLoS One 5 (2010), e15522.
    F. Albrecht, M. List, C. Bock, et al., DeepBlue epigenomic data server: Programmatic data retrieval and analysis of epigenome region sets, Nucleic Acids Res. 44 (2016) W581-W586.
    Z. Xiong, F. Yang, M. Li, et al., EWAS Open Platform: Integrated data, knowledge and toolkit for epigenome-wide association study, Nucleic Acids Res. 50 (2022) D1004-D1009.
    Y. Kodama, J. Mashima, T. Kosuge, et al., DDBJ update: The Genomic Expression Archive (GEA) for functional genomics data, Nucleic Acids Res. 47 (2019) D69-D73.
    E. Sollis, A. Mosaku, A. Abid, et al., The NHGRI-EBI GWAS Catalog: Knowledgebase and deposition resource, Nucleic Acids Res. 51 (2023) D977-D985.
    D. Bujold, R. Gregoire, D. Brownlee, et al., IHEC data portal. I. Abugessaisa, T. Kasukawa, Practical Guide to Life Science Databases, first ed., Springer Nature, Singapore, 2011, pp. 77-94.
    National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD), https://ncrad.iu.edu/. (Accessed 18 June 2023).
    R.E. Consortium, A. Kundaje, W. Meuleman, et al., Integrative analysis of 111 reference human epigenomes, Nature 518 (2015) 317-330.
    T. Barrett, S.E. Wilhite, P. Ledoux, et al., NCBI GEO: Archive for functional genomics data sets: Update, Nucleic Acids Res. 41 (2013) D991-D995.
    C. Huttenhower, O. Hofmann, A quick guide to large-scale genomic data mining, PLoS Comput. Biol. 6 (2010), e1000779.
    F. Shi, Y. He, Y. Chen, et al., Comparative analysis of multiple neurodegenerative diseases based on advanced epigenetic aging brain, Front. Genet. 12 (2021), 657636.
    S. Mallik, Z. Zhao, Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise, Sci. Rep. 10 (2020), 22164.
    C. Pellegrini, C. Pirazzini, C. Sala, et al., A meta-analysis of brain DNA methylation across sex, age, and Alzheimer’s disease points for accelerated epigenetic aging in neurodegeneration, Front. Aging Neurosci. 13 (2021), 639428.
    H.U. Klein, C. McCabe, E. Gjoneska, et al., Epigenome-wide study uncovers large-scale changes in histone acetylation driven by tau pathology in aging and Alzheimer’s human brains, Nat. Neurosci. 22 (2019) 37-46.
    P.L. De Jager, Deconstructing the epigenomic architecture of human neurodegeneration, Neurobiol. Dis. 153 (2021), 105331.
    L.F. MacBean, A.R. Smith, K. Lunnon, Exploring beyond the DNA sequence: A review of epigenomic studies of DNA and histone modifications in dementia, Curr. Genet. Med. Rep. 8 (2020) 79-92.
    T. Lu, C.E. Ang, X. Zhuang, Spatially resolved epigenomic profiling of single cells in complex tissues, Cell 185 (2022) 4448-4464.e17.
    Y. Deng, M. Bartosovic, P. Kukanja, et al., Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level, Science 375 (2022) 681-686.
    Y. Deng, M. Bartosovic, S. Ma, et al., Spatial profiling of chromatin accessibility in mouse and human tissues, Nature 609 (2022) 375-383.
    R. Fan, D. Zhang, G. Su, Spatially resolved epigenome-transcriptome co-profiling of mammalian tissues at the cellular level, Res. Sq. (2022) 1-26.
    I.A. Qureshi, M.F. Mehler, Understanding neurological disease mechanisms in the era of epigenetics, JAMA Neurol. 70 (2013) 703-710.
    M.S. Rao, T.R. Van Vleet, R. Ciurlionis, et al., Comparison of RNA-seq and microarray gene expression platforms for the toxicogenomic evaluation of liver from short-term rat toxicity studies, Front. Genet. 9 (2018), 636.
    J. Verheijen, K. Sleegers, Understanding Alzheimer disease at the interface between genetics and transcriptomics, Trends Genet. 34 (2018) 434-447.
    S. Han, K. Nho, Y. Lee, Alternative splicing regulation of an Alzheimer’s risk variant in CLU, Int. J. Mol. Sci. 21 (2020), 7079.
    M. Wang, N.D. Beckmann, P. Roussos, et al., The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease, Sci. Data 5 (2018), 180185.
    D.A. Bennett, A.S. Buchman, P.A. Boyle, et al., Religious orders study and rush memory and aging project, J. Alzheimers Dis. 64 (2018) S161-S189.
    A. Lachmann, D. Torre, A.B. Keenan, et al., Massive mining of publicly available RNA-seq data from human and mouse, Nat. Commun. 9 (2018), 1366.
    Y. Nakamura, Y. Kodama, S. Saruhashi, et al., DDBJ sequence read archive/DDBJ omics archive, Nat. Preced. (2010). https://doi.org/10.1038/npre.2010.5085.1.
    SYNAPSE. https://www.synapse.org/. (Accessed 18 June 2023).
    J.I. Satoh, Y. Yamamoto, N. Asahina, et al., RNA-Seq data mining: Downregulation of NeuroD6 serves as a possible biomarker for Alzheimer’s disease brains, Dis. Markers 2014 (2014), 123165.
    S. Mukherjee, L. Heath, C. Preuss, et al., Molecular estimation of neurodegeneration pseudotime in older brains, Nat. Commun. 11 (2020), 5781.
    K. Hooshmand, G.M. Halliday, S.S. Pineda, et al., Overlap between central and peripheral transcriptomes in Parkinson’s disease but not Alzheimer’s disease, Int. J. Mol. Sci. 23 (2022), 5200.
    M.B. Hossain, M.K. Islam, A. Adhikary, et al., Bioinformatics approach to identify significant biomarkers, drug targets shared between Parkinson’s disease and bipolar disorder: A pilot study, Bioinform. Biol. Insights 16 (2022), 11779322221079232.
    S. Batchu, Progressive multiple sclerosis transcriptome deconvolution indicates increased M2 macrophages in inactive lesions, Eur. Neurol. 83 (2020) 433-435.
    B. Hwang, J.H. Lee, D. Bang, Single-cell RNA sequencing technologies and bioinformatics pipelines, Exp. Mol. Med. 50 (2018) 1-14.
    O.B. Poirion, X. Zhu, T. Ching, et al., Single-cell transcriptomics bioinformatics and computational challenges, Front. Genet. 7 (2016), 163.
    S. Slovin, A. Carissimo, F. Panariello, et al., Single-cell RNA sequencing analysis: A step-by-step overview, Methods Mol. Biol. 2284 (2021) 343-365.
    V. Svensson, E. da Veiga Beltrame, L. Pachter, A curated database reveals trends in single-cell transcriptomics, Database 2020 (2020), baaa073.
    S. Ma, S.B. Lim, Single-cell RNA sequencing in Parkinson’s disease, Biomedicines 9 (2021), 368.
    J. Jiang, C. Wang, R. Qi, et al., scREAD: A single-cell RNA-seq database for Alzheimer’s disease, iScience 23 (2020), 101769.
    Q. Wang, S. Ding, Y. Li, et al., The Allen mouse brain common coordinate framework: A 3D reference atlas, Cell 181 (2020) 936-953.e20.
    Y. Hu, S.G. Tattikota, Y. Liu, et al., DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species, Comput. Struct. Biotechnol. J. 19 (2021) 2018-2026.
    P.N. Pushparaj, G. Kalamegam, K.H. Wali Sait, et al., Decoding the role of astrocytes in the entorhinal cortex in Alzheimer’s disease using high-dimensional single-nucleus RNA sequencing data and next-generation knowledge discovery methodologies: Focus on drugs and natural product remedies for dementia, Front. Pharmacol. 12 (2021), 720170.
    A.P. Tsai, C. Dong, P.B. Lin, et al., PLCG2 is associated with the inflammatory response and is induced by amyloid plaques in Alzheimer’s disease, Genome Med. 14 (2022), 17.
    A. Grubman, G. Chew, J.F. Ouyang, et al., A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation, Nat. Neurosci. 22 (2019) 2087-2097.
    S.F. Lau, H. Cao, A.K.Y. Fu, et al., Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease, Proc. Natl. Acad. Sci. U. S. A. 117 (2020) 25800-25809.
    H. Mathys, J. Davila-Velderrain, Z. Peng, et al., Single-cell transcriptomic analysis of Alzheimer’s disease, Nature 570 (2019) 332-337.
    X. Wang, L. Li, Cell type-specific potential pathogenic genes and functional pathways in Alzheimer’s Disease, BMC Neurol. 21 (2021), 381.
    G. Pei, B. Fernandes, Y. Wang, et al., A single-cell atlas of the human brain in Alzheimer’s disease and its implications for personalized drug repositioning, bioRxiv. 2022. https://doi.org/10.1101/2022.06.14.496100
    M. Wilhelm, J. Schlegl, H. Hahne, et al., Mass-spectrometry-based draft of the human proteome, Nature 509 (2014) 582-587.
    X. Wang, Q. Liu, B. Zhang, Leveraging the complementary nature of RNA-Seq and shotgun proteomics data, Proteomics 14 (2014) 2676-2687.
    T. Maier, M. Guell, L. Serrano, Correlation of mRNA and protein in complex biological samples, FEBS Lett. 583 (2009) 3966-3973.
    E.M. Schoof, B. Furtwangler, N. Uresin, et al., Quantitative single-cell proteomics as a tool to characterize cellular hierarchies, Nat. Commun. 12 (2021), 3341.
    K.M. Verrou, I. Tsamardinos, G. Papoutsoglou, Learning pathway dynamics from single-cell proteomic data: A comparative study, Cytometry A 97 (2020) 241-252.
    V.S. Rao, K. Srinivas, G.N. Sujini, et al., Protein-protein interaction detection: Methods and analysis, Int. J. Proteom. 2014 (2014), 147648.
    A. Zhang, Protein interaction networks: computational analysis. Cambridge University Press, 2009, pp. 1-292.
    M.W. Gonzalez, M.G. Kann, Chapter 4: Protein interactions and disease, PLoS Comput. Biol. 8 (2012), e1002819.
    R. Craig, J.P. Cortens, R.C. Beavis, Open source system for analyzing, validating, and storing protein identification data, J. Proteome Res. 3 (2004) 1234-1242.
    S. Okuda, Y. Watanabe, Y. Moriya, et al., jPOSTrepo: An international standard data repository for proteomes, Nucleic Acids Res. 45 (2017) D1107-D1111.
    M. Choi, J. Carver, C. Chiva, et al., MassIVE.quant: A community resource of quantitative mass spectrometry-based proteomics datasets, Nat. Meth. 17 (2020) 981-984.
    Proteomic Data Commons, https://proteomic.datacommons.cancer.gov/pdc/. (Accessed 18 June 2023).
    J.A. Vizcaino, E.W. Deutsch, R. Wang, et al., ProteomeXchange provides globally coordinated proteomics data submission and dissemination, Nat. Biotechnol. 32 (2014) 223-226.
    L. Martens, H. Hermjakob, P. Jones, et al., PRIDE: The proteomics identifications database, Proteomics 5 (2005) 3537-3545.
    P. Samaras, T. Schmidt, M. Frejno, et al., ProteomicsDB: A multi-omics and multi-organism resource for life science research, Nucleic Acids Res. 48 (2020) D1153-D1163.
    A. Freitas, M. Aroso, S. Rocha, et al., Bioinformatic analysis of the human brain extracellular matrix proteome in neurodegenerative disorders, Eur. J. Neurosci. 53 (2021) 4016-4033.
    S.C. Deolankar, A.H. Patil, D.A.B. Rex, et al., Mapping post-translational modifications in brain regions in Alzheimer’s disease using proteomics data mining, Omics 25 (2021) 525-536.
    H. Haytural, R. Benfeitas, S. Schedin-Weiss, et al., Insights into the changes in the proteome of Alzheimer disease elucidated by a meta-analysis, Sci. Data 8 (2021), 312.
    M.V. Kuleshov, M.R. Jones, A.D. Rouillard, et al., Enrichr: A comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Res. 44 (2016) W90-W97.
    Y. Kinoshita, T. Uo, S. Jayadev, et al., Potential applications and limitations of proteomics in the study of neurological disease, Arch. Neurol. 63 (2006) 1692-1696.
    A.D. Brunner, M. Thielert, C. Vasilopoulou, et al., Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation, Mol. Syst. Biol. 18 (2022), e10798.
    J.M. Perkel, Single-cell proteomics takes centre stage, Nature 597 (2021) 580-582.
    I. Paul, C. White, I. Turcinovic, et al., Imaging the future: The emerging era of single-cell spatial proteomics, FEBS J. 288 (2021) 6990-7001.
    H. Gallart-Ayala, T. Teav, J. Ivanisevic, Metabolomics meets lipidomics: Assessing the small molecule component of metabolism, BioEssays 42 (2020), e2000052.
    R. Wang, B. Li, S.M. Lam, et al., Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression, Yi Chuan Xue Bao 47 (2020) 69-83.
    M.A. Alves, S. Lamichhane, A. Dickens, et al., Systems biology approaches to study lipidomes in health and disease, Biochim. Biophys. Acta Mol. Cell. Biol. Lipids. 1866 (2021), 158857.
    D.B. Castellanos, C.A. Martin-Jimenez, F. Rojas-Rodriguez, et al., Brain lipidomics as a rising field in neurodegenerative contexts: Perspectives with Machine Learning approaches, Front. Neuroendocrinol. 61 (2021), 100899.
    B.B. Misra, C. Langefeld, M. Olivier, et al., Integrated omics: Tools, advances and future approaches, J. Mol. Endocrinol. 62 (2019) R21-R45.
    A. Schumacher-Schuh, A. Bieger, W.V. Borelli, et al., Advances in proteomic and metabolomic profiling of neurodegenerative diseases, Front. Neurol. 12 (2021), 792227.
    Y. Shao, W. Le, Recent advances and perspectives of metabolomics-based investigations in Parkinson’s disease, Mol. Neurodegener. 14 (2019), 3.
    P. Reveglia, C. Paolillo, G. Ferretti, et al., Challenges in LC-MS-based metabolomics for Alzheimer’s disease early detection: Targeted approaches versus untargeted approaches, Metabolomics 17 (2021), 78.
    R.C. Petersen, P.S. Aisen, L.A. Beckett, et al., Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization, Neurology 74 (2010) 201-209.
    D.S. Wishart, M.J. Lewis, J.A. Morrissey, et al., The human cerebrospinal fluid metabolome, J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 871 (2008) 164-173.
    D.S. Wishart, D. Tzur, C. Knox, et al., HMDB: The human metabolome database, Nucleic Acids Res. 35 (2007) D521-D526.
    D.S. Wishart, C. Knox, A.C. Guo, et al., HMDB: A knowledgebase for the human metabolome, Nucleic Acids Res. 37 (2009) D603-D610.
    D.S. Wishart, T. Jewison, A.C. Guo, et al., HMDB 3.0-The human metabolome database in 2013, Nucleic Acids Res. 41 (2013) D801-D807.
    D.S. Wishart, Y.D. Feunang, A. Marcu, et al., HMDB 4.0: The human metabolome database for 2018, Nucleic Acids Res. 46 (2018) D608-D617.
    D.S. Wishart, A.C. Guo, E. Oler, et al., HMDB 5.0: The human metabolome database for 2022, Nucleic Acids Res. 50 (2022) D622-D631.
    K. Watanabe, E. Yasugi, M. Oshima, How to search the glycolipid data in “LIPIDBANK for Web” the newly developed lipid database in Japan, Trends Glycosci. Glycotechnol. 12 (2000) 175-184.
    T. Kind, K.H. Liu, D.Y. Lee, et al., LipidBlast in silico tandem mass spectrometry database for lipid identification, Nat. Meth. 10 (2013) 755-758.
    D. Cotter, A. Maer, C. Guda, et al., LMPD: LIPID MAPS proteome database, Nucleic Acids Res. 34 (2006) D507-D510.
    T.B. Mracica, A. Anghel, C.F. Ion, et al., MetaboAge DB: A repository of known ageing-related changes in the human metabolome, Biogerontology 21 (2020) 763-771.
    K. Haug, R.M. Salek, P. Conesa, et al., MetaboLights: An open-access general-purpose repository for metabolomics studies and associated meta-data, Nucleic Acids Res. 41 (2013) D781-D786.
    M. Sud, E. Fahy, D. Cotter, et al., Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools, Nucleic Acids Res. 44 (2016) D463-D470.
    MetabolomeXchange, http://www.metabolomexchange.org/site/. (Accessed 18 June 2023)
    N. Psychogios, D.D. Hau, J. Peng, et al., The human serum metabolome, PLoS One 6 (2011), e16957.
    D.K. Barupal, R. Baillie, S. Fan, et al., Sets of coregulated serum lipids are associated with Alzheimer’s disease pathophysiology, Alzheimers Dement. (Amst) 11 (2019) 619-627.
    Y. Tang, S. Shah, K.S. Cho, et al., Metabolomics in primary open angle glaucoma: A systematic review and meta-analysis, Front. Neurosci. 16 (2022), 835736.
    Y. Perez-Riverol, M. Bai, F. da Veiga Leprevost, et al., Discovering and linking public omics data sets using the Omics Discovery Index, Nat. Biotechnol. 35 (2017) 406-409.
    J. Pu, Y. Yu, Y. Liu, et al., MENDA: A comprehensive curated resource of metabolic characterization in depression, Brief Bioinform. 21 (2020) 1455-1464.
    X. Han, R. Wang, Y. Zhou, et al., Mapping the mouse cell atlas by microwell-seq, Cell 172 (2018) 1091-1107.e17.
    The Tabula Muris Consortium, Overall coordination, Logistical coordination, et al., Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris, Nature 562 (2018) 367-372.
    J. Liao, X. Lu, X. Shao, et al., Uncovering an organ’s molecular architecture at single-cell resolution by spatially resolved transcriptomics, Trends Biotechnol. 39 (2021) 43-58.
    J. Cheng, J. Liao, X. Shao, et al., Multiplexing methods for simultaneous large-scale transcriptomic profiling of samples at single-cell resolution, Adv. Sci. (Weinh) 8 (2021), e2101229.
    K.H. Chen, A.N. Boettiger, J.R. Moffitt, et al., RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells, Science 348 (2015), aaa6090.
    P.L. Stahl, F. Salmen, S. Vickovic, et al., Visualization and analysis of gene expression in tissue sections by spatial transcriptomics, Science 353 (2016) 78-82.
    S.G. Rodriques, R.R. Stickels, A. Goeva, et al., Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution, Science 363 (2019) 1463-1467.
    C.L. Eng, M. Lawson, Q. Zhu, et al., Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH, Nature 568 (2019) 235-239.
    R. Satija, J.A. Farrell, D. Gennert, et al., Spatial reconstruction of single-cell gene expression data, Nat. Biotechnol. 33 (2015) 495-502.
    K. Achim, J.B. Pettit, L.R. Saraiva, et al., High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin, Nat. Biotechnol. 33 (2015) 503-509.
    D. Franjic, M. Skarica, S. Ma, et al., Transcriptomic taxonomy and neurogenic trajectories of adult human, macaque, and pig hippocampal and entorhinal cells, Neuron 110 (2022) 452-469.e14.
    A.M. Femino, F.S. Fay, K. Fogarty, et al., Visualization of single RNA transcripts in situ, Science 280 (1998) 585-590.
    C. Giesen, H.A.O. Wang, D. Schapiro, et al., Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry, Nat. Meth. 11 (2014) 417-422.
    G. Gut, M.D. Herrmann, L. Pelkmans, Multiplexed protein maps link subcellular organization to cellular states, Science 361 (2018), eaar7042.
    W. Chen, A. Lu, K. Craessaerts, et al., Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease, Cell 182 (2020) 976-991.e19.
    S. Prokop, K.R. Miller, S.R. Labra, et al., Impact of TREM2 risk variants on brain region-specific immune activation and plaque microenvironment in Alzheimer’s disease patient brain samples, Acta Neuropathol. 138 (2019) 613-630.
    J.F. Navarro, D.L. Croteau, A. Jurek, et al., Spatial transcriptomics reveals genes associated with dysregulated mitochondrial functions and stress signaling in Alzheimer disease, iScience 23 (2020), 101556.
    J. Aguila, S. Cheng, N. Kee, et al., Spatial RNA sequencing identifies robust markers of vulnerable and resistant human midbrain dopamine neurons and their expression in Parkinson’s disease, Front. Mol. Neurosci. 14 (2021), 699562.
    T. Kamath, A. Abdulraouf, S.J. Burris, et al., Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson’s disease, Nat. Neurosci. 25 (2022) 588-595.
    S. Smajic, C.A. Prada-Medina, Z. Landoulsi, et al., Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state, Brain 145 (2022) 964-978.
    C.H. Lo, M. Skarica, M. Mansoor, et al., Astrocyte heterogeneity in multiple sclerosis: Current understanding and technical challenges, Front. Cell. Neurosci. 15 (2021), 726479.
    L. Schirmer, D. Velmeshev, S. Holmqvist, et al., Neuronal vulnerability and multilineage diversity in multiple sclerosis, Nature 573 (2019) 75-82.
    M. Kaufmann, H. Evans, A.L. Schaupp, et al., Identifying CNS-colonizing T cells as potential therapeutic targets to prevent progression of multiple sclerosis, Med 2 (2021) 296-312.e8.
    M. Absinta, D. Maric, M. Gharagozloo, et al., A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis, Nature 597 (2021) 709-714.
    M. Kaufmann, A.L. Schaupp, R. Sun, et al., Identification of early neurodegenerative pathways in progressive multiple sclerosis, Nat. Neurosci. 25 (2022) 944-955.
    B. Pardo, A. Spangler, L.M. Weber, et al., spatialLIBD: An R/Bioconductor package to visualize spatially-resolved transcriptomics data, BMC Genomics 23 (2022), 434.
    D. Righelli, L.M. Weber, H.L. Crowell, et al., SpatialExperiment: Infrastructure for spatially-resolved transcriptomics data in R using Bioconductor, Bioinformatics 38 (2022) 3128-3131.
    C.K. Mah, N. Ahmed, N. Lopez, et al., Bento: A toolkit for subcellular analysis of spatial transcriptomics data, bioRxiv. 2023. https://10.1101/2022.06.10.495510
    L.M. Breckels, C.M. Mulvey, K.S. Lilley, et al., A Bioconductor workflow for processing and analysing spatial proteomics data, F1000Research 5 (2016), 2926.
    G. Palla, H. Spitzer, M. Klein, et al., Squidpy: A scalable framework for spatial omics analysis, Nat. Meth. 19 (2022) 171-178.
    S. Vickovic, B. Lotstedt, J. Klughammer, et al., SM-Omics is an automated platform for high-throughput spatial multi-omics, Nat. Commun. 13 (2022), 795.
    A. Martinelli, M. Rapsomaniki, ATHENA: Analysis of tumor heterogeneity from spatial omics measurements, Bioinformatics 38 (2022) 3151-3153.
    M.A. Kennedy, W.A. Hofstadter, I.M. Cristea, TRANSPIRE: A computational pipeline to elucidate intracellular protein movements from spatial proteomics data sets, J. Am. Soc. Mass Spectrom. 31 (2020) 1422-1439.
    J.A. Christopher, A. Geladaki, C.S. Dawson, et al., Subcellular transcriptomics and proteomics: A comparative methods review, Mol. Cell. Proteom. 21 (2022), 100186.
    A. Regev, S.A. Teichmann, E.S. Lander, et al., The human cell atlas, eLife 6 (2017), e27041.
    HuBMAP Consortium, The human body at cellular resolution: The NIH Human Biomolecular Atlas Program, Nature 574 (2019) 187-192.
    M. Frenkel-Morgenstern, A.A. Cohen, N. Geva-Zatorsky, et al., Dynamic proteomics: A database for dynamics and localizations of endogenous fluorescently-tagged proteins in living human cells, Nucleic Acids Res. 38 (2010) D508-D512.
    R. Dries, Q. Zhu, R. Dong, et al., Giotto: A toolbox for integrative analysis and visualization of spatial expression data, Genome Biol. 22 (2021), 78.
    Z. Xu, W. Wang, T. Yang, et al., STOmicsDB: A database of Spatial Transcriptomic data, bioRxiv. 2022. https://doi.org/10.1101/2022.03.11.481421.
    Z. Fan, R. Chen, X. Chen, SpatialDB: A database for spatially resolved transcriptomes, Nucleic Acids Res. 48 (2020) D233-D237.
    J. Qian, J. Liao, Z. Liu, et al., Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace, Nat. Commun. 14 (2023), 2484.
    J. Liao, J. Qian, Y. Fang, et al., De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution, Nat. Commun. 13 (2022), 6498.
    A. Zeisel, A.B. Munoz-Manchado, S. Codeluppi, et al., Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq, Science 347 (2015) 1138-1142.
    F. Bao, Y. Deng, S. Wan, et al., Integrative spatial analysis of cell morphologies and transcriptional states with MUSE, Nat. Biotechnol. 40 (2022) 1200-1209.
    V. Marx, Method of the year: Spatially resolved transcriptomics, Nat. Meth. 18 (2021) 9-14.
    S.K. Longo, M.G. Guo, A.L. Ji, et al., Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics, Nat. Rev. Genet. 22 (2021) 627-644.
    X. Shao, X. Lu, J. Liao, et al., New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data, Protein Cell 11 (2020) 866-880.
    X. Shao, J. Liao, C. Li, et al., CellTalkDB: A manually curated database of ligand-receptor interactions in humans and mice, Brief. Bioinform. 22 (2021), bbaa269.
    X. Shao, C. Li, H. Yang, et al., Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk, Nat. Commun. 13 (2022), 4429.
    Y. Fangma, M. Liu, J. Liao, et al., Dissecting the brain with spatially resolved multi-omics, J. Pharm. Anal. 2023. https://doi.org/10.1016/j.jpha.2023.04.003.
    T.J. Sejnowski, P.S. Churchland, J.A. Movshon, Putting big data to good use in neuroscience, Nat. Neurosci. 17 (2014) 1440-1441.
    H. Lee, J.J. Lee, N.Y. Park, et al., Multi-omic analysis of selectively vulnerable motor neuron subtypes implicates altered lipid metabolism in ALS, Nat. Neurosci. 24 (2021) 1673-1685.
    L. Xicota, F. Ichou, F.X. Lejeune, et al., Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer’s disease: The INSIGHT-preAD study, EBioMedicine 47 (2019) 518-528.
    E. Puris, S. Kouril, L. Najdekr, et al., Metabolomic, lipidomic and proteomic characterisation of lipopolysaccharide-induced inflammation mouse model, Neuroscience 496 (2022) 165-178.
    C. Clark, L. Dayon, M. Masoodi, et al., An integrative, hypothesis-free, multi-omics approach uncovers biological pathway alterations in Alzheimer’s disease, Alzheimers. Dement. 16 (2020), e038563.
    S. Lee, N.A. Devanney, L.R. Golden, et al., APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge, Cell Rep. 42 (2023), 112196.
    M.B. O’Rourke, S.E.L. Town, P.V. Dalla, et al., What is normalization? the strategies employed in top-down and bottom-up proteome analysis workflows, Proteomes 7 (2019), 29.
    A. Conesa, P. Madrigal, S. Tarazona, et al., A survey of best practices for RNA-seq data analysis, Genome Biol. 17 (2016), 13.
    P. Yang, H. Huang, C. Liu, Feature selection revisited in the single-cell era, Genome Biol. 22 (2021), 321.
    A. Torres-Martos, M. Bustos-Aibar, A. Ramirez-Mena, et al., Omics data preprocessing for machine learning: A case study in childhood obesity, Genes 14 (2023), 248.
    S. Nataf, M. Guillen, L. Pays, TGFB1-mediated gliosis in multiple sclerosis spinal cords is favored by the regionalized expression of HOXA5 and the age-dependent decline in androgen receptor ligands, Int. J. Mol. Sci. 20 (2019), 5934.
    M.E. Garcia-Segura, B.R. Durainayagam, S. Liggi, et al., Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer’s disease mouse model and risk loci carriers, J. Neurochem. 164 (2023) 57-76.
    G. Zhou, S. Li, J. Xia, Network-based approaches for multi-omics integration, Methods Mol. Biol. 2104 (2020) 469-487.
    M. Kanehisa, S. Goto, KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acids Res. 28 (2000) 27-30.
    M. Kanehisa, M. Furumichi, Y. Sato, et al., KEGG for taxonomy-based analysis of pathways and genomes, Nucleic Acids Res. 51 (2023) D587-D592.
    M. Ashburner, C.A. Ball, J.A. Blake, et al., Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium Nat. Genet. 25 (2000) 25-29.
    Gene Ontology Consortium, The Gene Ontology resource: Enriching a GOld mine., Nucleic Acids Res. 49 (2021) D325-D334.
    G. Dennis Jr, B.T. Sherman, D.A. Hosack, et al., DAVID: Database for annotation, visualization, and integrated discovery, Genome Biol. 4 (2003), P3.
    P.D. Thomas, D. Ebert, A. Muruganujan, et al., PANTHER: Making genome-scale phylogenetics accessible to all, Protein Sci. 31 (2022) 8-22.
    H. Mi, A. Muruganujan, X. Huang, et al., Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0), Nat. Protoc. 14 (2019) 703-721.
    A. Subramanian, P. Tamayo, V.K. Mootha, et al., Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles, Proc. Natl. Acad. Sci. U. S. A. 102 (2005) 15545-15550.
    A. Kramer, J. Green, J. Pollard Jr, et al., Causal analysis approaches in Ingenuity Pathway Analysis, Bioinformatics 30 (2014) 523-530.
    D. Szklarczyk, A.L. Gable, D. Lyon, et al., STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res. 47 (2019) D607-D613.
    P. Shannon, A. Markiel, O. Ozier, et al., Cytoscape: A software environment for integrated models of biomolecular interaction networks, Genome Res. 13 (2003) 2498-2504.
    F. Martin, T.M. Thomson, A. Sewer, et al., Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks, BMC Syst. Biol. 6 (2012), 54.
    A.L. Tarca, S. Draghici, P. Khatri, et al., A novel signaling pathway impact analysis, Bioinformatics 25 (2009) 75-82.
    L. Cottret, C. Frainay, M. Chazalviel, et al., MetExplore: Collaborative edition and exploration of metabolic networks, Nucleic Acids Res. 46 (2018) W495-W502.
    T.C. Kuo, T.F. Tian, Y.J. Tseng, 3Omics: A web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data, BMC Syst. Biol. 7 (2013), 64.
    T. Liu, P. Salguero, M. Petek, et al., PaintOmics 4: New tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases, Nucleic Acids Res. 50 (2022) W551-W559.
    I. Subramanian, S. Verma, S. Kumar, et al., Multi-omics data integration, interpretation, and its application, Bioinform. Biol. Insights 14 (2020), 1177932219899051.
    Y. Xu, R.P. McCord, Diagonal integration of multimodal single-cell data: Potential pitfalls and paths forward, Nat. Commun. 13 (2022), 3505.
    C. Stark, B.J. Breitkreutz, T. Reguly, et al., BioGRID: A general repository for interaction datasets, Nucleic Acids Res. 34 (2006) D535-D539.
    H. Fanaee-T, M. Thoresen, Multi-insight visualization of multi-omics data via ensemble dimension reduction and tensor factorization, Bioinformatics. 35 (2019) 1625-1633.
    N. Vahabi, G. Michailidis, Unsupervised multi-omics data integration methods: A comprehensive review, Front. Genet. 13 (2022), 854752.
    D.D. Lee, H.S. Seung, Algorithms for Non-negative Matrix Factorization, Proceedings of Adv. Neural Inf. Process, Dec 3 - 8, 2001, Vancouver, Canada, 2001.
    M. Pierre-Jean, J.F. Deleuze, E. le Floch, et al., Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration, Brief. Bioinform. 21 (2020) 2011-2030.
    Z. Yang, G. Michailidis, A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data, Bioinformatics 32 (2016) 1-8.
    S. MacEachern, P. Muller, Efficient MCMC Schemes for Robust Model Extensions Using Encompassing Dirichlet Process Mixture Models - Robust Bayesian Analysis. P. Diggle, S. Zeger, Lecture Notes in Statistics, first ed., Springer, New York, 2000, pp. 295-315.
    L. Cowen, T. Ideker, B.J. Raphael, et al., Network propagation: A universal amplifier of genetic associations, Nat. Rev. Genet. 18 (2017) 551-562.
    W. Ye, G. Ji, P. Ye, et al., scNPF: An integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data, BMC Genomics 20 (2019), 347.
    P. Langfelder, S. Horvath, WGCNA: An R package for weighted correlation network analysis, BMC Bioinformatics 9 (2008), 559.
    S. Doledec, D. Chessel, Co-inertia analysis: an alternative method for studying species-environment relationships, Freshw. Biol. 31 (1994) 277-294.
    K. Sankaran, S.P. Holmes, Multitable methods for microbiome data integration, Front. Genet. 10 (2019), 627.
    R. Shen, A.B. Olshen, M. Ladanyi, Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis, Bioinformatics 25 (2009) 2906-2912.
    Q. Mo, S. Wang, V.E. Seshan, et al., Pattern discovery and cancer gene identification in integrated cancer genomic data, Proc. Natl. Acad. Sci. U. S. A. 110 (2013) 4245-4250.
    E.F. Lock, K.A. Hoadley, J.S. Marron, et al., Joint and individual variation explained (jive) for integrated analysis of multiple data types, Ann. Appl. Stat. 7 (2013) 523-542.
    R. Louhimo, S. Hautaniemi, CNAmet: An R package for integrating copy number, methylation and expression data, Bioinformatics 27 (2011) 887-888.
    C.J. Vaske, S.C. Benz, J.Z. Sanborn, et al., Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM, Bioinformatics 26 (2010) i237-i245.
    J.-C. Park, N. Barahona-Torres, S.-Y. Jang, et al., Multi-omics-based autophagy-related untypical subtypes in patients with cerebral amyloid pathology, Adv. Sci (Weinh). 9 (2022), e2201212.
    A. Catanese, S. Rajkumar, D. Sommer, et al., Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis, Brain (2023), awad075.
    S. Lovestone, P. Francis, I. Kloszewska, et al., AddNeuroMed: The European collaboration for the discovery of novel biomarkers for Alzheimer’s disease, Ann. N. Y. Acad. Sci. 1180 (2009) 36-46.
    A. Hye, S. Lynham, M. Thambisetty, et al., Proteome-based plasma biomarkers for Alzheimer’s disease, Brain 129 (2006) 3042-3050.
    J. Xu, G. Bankov, M. Kim, et al., Integrated lipidomics and proteomics network analysis highlights lipid and immunity pathways associated with Alzheimer’s disease, Transl. Neurodegener. 9 (2020), 36.
    N.T. Seyfried, E.B. Dammer, V. Swarup, et al., A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease, Cell Syst. 4 (2017) 60-72.e4.
    J.B. Toledo, M. Arnold, G. Kastenmuller, et al., Metabolic network failures in Alzheimer’s disease: A biochemical roadmap, Alzheimers. Dement. 13 (2017) 965-984.
    E. Bonnet, L. Calzone, T. Michoel, Integrative multi-omics module network inference with Lemon-Tree, PLoS Comput. Biol. 11 (2015), e1003983.
    S. Jamal, S. Goyal, A. Shanker, et al., Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes, BMC Genomics 17 (2016), 807.
    J. Menche, E. Guney, A. Sharma, et al., Integrating personalized gene expression profiles into predictive disease-associated gene pools, NPJ Syst. Biol. Appl. 3 (2017), 10.
    P.T. Ram, J. Mendelsohn, G.B. Mills, Bioinformatics and systems biology, Mol. Oncol. 6 (2012) 147-154.
    N.S. Buchan, D.K. Rajpal, Y. Webster, et al., The role of translational bioinformatics in drug discovery, Drug Discov. Today 16 (2011) 426-434.
    J. Leipzig, A review of bioinformatic pipeline frameworks, Brief Bioinform. 18 (2017) 530-536.
    A. Gupta, S. Gupta, S.K. Jatawa, et al., A simplest bioinformatics pipeline for whole transcriptome sequencing: Overview of the processing and steps from raw data to downstream analysis, bioRxiv. 2019. https://doi.org/10.1101/836973.
    L. Siegwald, H. Touzet, Y. Lemoine, et al., Assessment of Common and Emerging Bioinformatics Pipelines for Targeted Metagenomics, PLoS One 12 (2017), e0169563.
    C.P. Moritz, T. Muhlhaus, S. Tenzer, et al., Poor transcript-protein correlation in the brain: Negatively correlating gene products reveal neuronal polarity as a potential cause, J. Neurochem. 149 (2019) 582-604.
    M. Jafari, Y. Guan, D.C. Wedge, et al., Re-evaluating experimental validation in the Big Data Era: a conceptual argument, Genome Biol. 22 (2021), 71.
    C. Manzoni, D.A. Kia, J. Vandrovcova, et al., Genome, transcriptome and proteome: The rise of omics data and their integration in biomedical sciences, Brief Bioinform. 19 (2018) 286-302.
    C.H. Lo, J. Zeng, Defective lysosomal acidification: A new prognostic marker and therapeutic target for neurodegenerative diseases, Transl. Neurodegener. 12 (2023), 29.
    D. Pitt, C.H. Lo, S.A. Gauthier, et al., Toward precision phenotyping of multiple sclerosis, Neurol. Neuroimmunol. Neuroinflamm. 9 (2022), e200025.
    K.S. Suh, S. Sarojini, M. Youssif, et al., Tissue banking, bioinformatics, and electronic medical records: The front-end requirements for personalized medicine, J. Oncol. 2013 (2013), 368751.
    A. Kolodkin, E. Simeonidis, R. Balling, et al., Understanding complexity in neurodegenerative diseases: in silico reconstruction of emergence, Front. Physiol. 3 (2012), 291.
    S. Golriz Khatami, S. Mubeen, M. Hofmann-Apitius, Data science in neurodegenerative disease: Its capabilities, limitations, and perspectives, Curr. Opin. Neurol. 33 (2020) 249-254.
    M.A. Myszczynska, P.N. Ojamies, A.M.B. Lacoste, et al., Applications of machine learning to diagnosis and treatment of neurodegenerative diseases, Nat. Rev. Neurol. 16 (2020) 440-456.
    A. Mammoliti, P. Smirnov, M. Nakano, et al., Orchestrating and sharing large multimodal data for transparent and reproducible research, Nat. Commun. 12 (2021), 5797.
    S. Lam, A. Bayraktar, C. Zhang, et al., A systems biology approach for studying neurodegenerative diseases, Drug Discov. Today 25 (2020) 1146-1159.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(1)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (422) PDF downloads(38) Cited by()
    Proportional views
    Related

    Copyright © Journal of Pharmaceutical Analysis

    E-mail: jpa2011@126.com;  jpasubmit@126.com;  jpaeditor@xjtu.edu.cn   Tel:+86-029-88960092

    Address: Western China Science and Technology Innovation Harbour, Xixian New Area, Xi’an, 712000, China

    Email alert RSS

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return