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      Enhancing the accuracy of Network Medicine through understanding the impact of sample size in gene co-expression networks

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            Abstract

            Network Medicine relies on RNA sequencing to infer gene co-expression networks, which are crucial to identify functional gene clusters and gene regulatory interactions, and offer a deeper understanding of disease phenotypes and drug mechanisms 1-4. Previous studies have used gene co-expression networks to prioritize drug candidates based on their interaction with relevant transcription factors 5, to identify potential treatments for schizophrenia 6, or to enrich the disease modules associated to rare diseases 7. It remains unknown, however, how many samples do we need to make reliable predictions. Here, we propose a power-law model to predict the relationship between the number of inferred significant interactions and sample size, allowing us to quantitatively link sample size to the accuracy of the inferred networks. We apply our model to investigate the effect of sample size on biomarker discovery and differentiation of protein-protein interactions from non-interacting pairs, ultimately unveiling the critical role of data quality in generating meaningful predictions in Network Medicine.

            1. van Dam, S., Võsa, U., van der Graaf, A., Franke, L. & de Magalhães, J. P. Gene co-expression analysis for functional classification and gene-disease predictions. Brief. Bioinform. 19, 575–592 (2018).

            2. Gysi, D. M. & Nowick, K. Construction, comparison and evolution of networks in life sciences and other disciplines. J. R. Soc. Interface 17, 20190610 (2020).

            3. Chowdhury, H. A., Bhattacharyya, D. K. & Kalita, J. K. (Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices. IEEE/ACM Trans. Comput. Biol. Bioinform. 17, 1154–1173 (2020).

            4. Paci, P. et al. Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery. Npj Syst. Biol. Appl. 7, 1–11 (2021).

            5. De Bastiani, M. et al. Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning. Front Pharmacol. 9:697 (2018).

            6. Truong, T. et al. Network-based drug repurposing for schizophrenia. Neuropsychopharmacology. 49(6):983-992 (2024).

            7. Buphamalai, P. et al. Network analysis reveals rare disease signatures across multiple levels of biological organization. Nat Commun. 12(1):6306 (2021).

            Content

            Author and article information

            Conference
            RExPO24 Conference
            REPO4EU
            30 April 2024
            Affiliations
            [1 ] Northeastern University ( https://ror.org/04t5xt781)
            [2 ] STALICLA;
            [3 ] Scipher Medicine Corporation;
            Author notes
            Author information
            https://orcid.org/0000-0002-1971-1350
            https://orcid.org/0000-0003-3595-3285
            https://orcid.org/0000-0003-4745-6331
            https://orcid.org/0000-0002-5771-8182
            https://orcid.org/0000-0002-4028-3522
            Article
            10.58647/REXPO.24000042.v1
            01f3c07d-6371-4326-8a76-083eb01ac967

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            RExPO24
            3
            Munich, Germany
            3-5 July 2024
            History
            : 30 April 2024
            Product

            REPO4EU

            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
            Award ID: 1P01HL132825
            Funded by: funder-id http://dx.doi.org/10.13039/100000738, U.S. Department of Veterans Affairs;
            Award ID: 36C24120D0027
            Funded by: funder-id , Scipher Inc.;
            Award ID: 21-C-01472
            Categories

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Bioinformatics & Computational biology
            sample size,gene co-expression networks,network medicine,rna-seq

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