{"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":1772138091540,"version":"3.50.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T00:00:00Z","timestamp":1547769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Experience for Undergraduates"},{"name":"Modeling and Simulation in Systems Biology","award":["DMS-1460967"],"award-info":[{"award-number":["DMS-1460967"]}]},{"name":"University of Connecticut School of Medicine"},{"name":"UConn National Science Foundation","award":["#1460967"],"award-info":[{"award-number":["#1460967"]}]},{"name":"Modeling and Simulation in Systems Biology REU"},{"name":"Center for Quantitative Medicine"},{"name":"UConn Health Center"}],"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>Summary<\/jats:title>\n                    <jats:p>The rapid development in quantitatively measuring DNA, RNA and protein has generated a great interest in the development of reverse-engineering methods, that is, data-driven approaches to infer the network structure or dynamical model of the system. Many reverse-engineering methods require discrete quantitative data as input, while many experimental data are continuous. Some studies have started to reveal the impact that the choice of data discretization has on the performance of reverse-engineering methods. However, more comprehensive studies are still greatly needed to systematically and quantitatively understand the impact that discretization methods have on inference methods. Furthermore, there is an urgent need for systematic comparative methods that can help select between discretization methods. In this work, we consider four published intracellular networks inferred with their respective time-series datasets. We discretized the data using different discretization methods. Across all datasets, changing the data discretization to a more appropriate one improved the reverse-engineering methods\u2019 performance. We observed no universal best discretization method across different time-series datasets. Thus, we propose DiscreeTest, a two-step evaluation metric for ranking discretization methods for time-series data. The underlying assumption of DiscreeTest is that an optimal discretization method should preserve the dynamic patterns observed in the original data across all variables. We used the same datasets and networks to show that DiscreeTest is able to identify an appropriate discretization among several candidate methods. To our knowledge, this is the first time that a method for benchmarking and selecting an appropriate discretization method for time-series data has been proposed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>All the datasets, reverse-engineering methods and source code used in this paper are available in Vera-Licona\u2019s lab Github repository: https:\/\/github.com\/VeraLiconaResearchGroup\/Benchmarking_TSDiscretizations.<\/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\/btz036","type":"journal-article","created":{"date-parts":[[2019,1,14]],"date-time":"2019-01-14T15:35:26Z","timestamp":1547480126000},"page":"3102-3109","source":"Crossref","is-referenced-by-count":8,"title":["Benchmarking time-series data discretization on inference methods"],"prefix":"10.1093","volume":"35","author":[{"given":"Yuezhe","family":"Li","sequence":"first","affiliation":[{"name":"University of Connecticut School of Medicine R.D. Berlin Center for Cell Analysis and Modeling, , Farmington, CT, USA"}]},{"given":"Tiffany","family":"Jann","sequence":"additional","affiliation":[{"name":"University of California Department of Electrical Engineering and Computer Sciences, , Berkeley, CA, USA"}]},{"given":"Paola","family":"Vera-Licona","sequence":"additional","affiliation":[{"name":"University of Connecticut School of Medicine Center for Quantitative Medicine, , Farmington, CT, USA"},{"name":"University of Connecticut School of Medicine Department of Cell Biology, , Farmington, CT, USA"},{"name":"University of Connecticut School of Medicine Department of Pediatrics, , Farmington, CT, USA"},{"name":"Institute for Systems Genomics, University of Connecticut School of Medicine , Farmington, CT, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,1,18]]},"reference":[{"key":"2023062803480389000_btz036-B1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1196\/annals.1407.006","article-title":"Comparison of reverse-engineering methods using an in silico network","volume":"1115","author":"Camacho","year":"2007","journal-title":"Ann. 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