{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:58:56Z","timestamp":1791237536348,"version":"4.1.0"},"reference-count":52,"publisher":"Radiation Research Society","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Radiation Research"],"published-print":{"date-parts":[[2026,1,14]]},"abstract":"<jats:p>Exposure to space radiation poses various health risks, so accurately estimating radiation dose in space is crucial. Herein, we integrated 301 transcriptomic profiles from 30 spaceflight datasets and developed radiation-dose estimation models for the space environment using a genetic algorithm. Two models were constructed in this work: one using gene expression fold changes as input (fold change model) and the other using gene degrees as input (degree model). Of note, we initially constructed a single sample network (SSN) for each spaceflight sample, respectively, and the degrees that represented the node (gene) features were extracted from the SSNs. Moreover, we not only constructed estimation models applicable to all tissues (overall models) but also developed specific models for each tissue (tissue models), enabling our models to be used across various task scenarios. According to the experimental results, all models demonstrate excellent performance in radiation dose estimation during spaceflight, and our genetic algorithm models achieve good predictive performance with a limited number of genes. We identified radiation-responsive genes, mainly involved in DNA repair, cell cycle, protein\/amino acid metabolic pathways, energy metabolic pathways, nervous system development and differentiation, and cancer pathways. Through the expression and interaction patterns of these genes, we found that the space radiation environment could induce health risks such as cancers, psychiatric\/neurological disorders, liver injury\/ toxicity disorders. In summary, the presented approach yields promising results for estimating radiation doses and supports the assessment of radiation risks in space environments.<\/jats:p>","DOI":"10.1667\/rade-24-00188.1","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T20:03:10Z","timestamp":1780948990000},"page":"582-596","source":"Crossref","is-referenced-by-count":1,"title":["Integrating Single-Sample Networks and Genetic Algorithm to Estimate Radiation Dose in Multiple Tissues of Mice under Space Environment"],"prefix":"10.1667","volume":"205","author":[{"given":"Yan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeqing","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1243","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"E Afshinnekoo, RT Scott, MJ MacKay,  \"Cell.\"  183, \"Fundamental Biological Features of Spaceflight: Advancing the Field to Enable Deep-Space Exploration.\" 1162 (2020)","DOI":"10.1016\/j.cell.2020.10.050"},{"key":"2","unstructured":"P. Martin,  \"Office of Inspector General, Report IG-16-003.\"  \"NASA's Efforts to Manage Health and Human Performance Risks for Space Exploration.\" 15 (2015)"},{"key":"3","doi-asserted-by":"crossref","unstructured":"JC Chancellor, GBI Scott, JP Sutton,  \"Life (Basel, Switzerland).\"  4, \"Space Radiation: The Number One Risk to Astronaut Health beyond Low Earth Orbit.\" 491 (2014)","DOI":"10.3390\/life4030491"},{"key":"4","doi-asserted-by":"crossref","unstructured":"L Zhao, Z Li, B Huang, et al.  \"Chemosphere.\"  \"Integrating evolutionarily conserved mechanism of response to radiation for exploring novel Caenorhabditis elegans radiation-responsive genes for estimation of radiation dose associated with spaceflight.\" (2024)","DOI":"10.1016\/j.chemosphere.2024.141148"},{"key":"5","doi-asserted-by":"crossref","unstructured":"S Furukawa, A Nagamatsu, M Nenoi,  \"BioMed research international.\"  2020, \"Space Radiation Biology for \u201cLiving in Space\" 2020","DOI":"10.1155\/2020\/4703286"},{"key":"6","doi-asserted-by":"crossref","unstructured":"AS Hirao, Y Watanabe, Y Hasegawa, et al.  \"Science of the Total Environment.\"  \"Mutational effects of chronic gamma radiation throughout the life cycle of Arabidopsis thaliana: Insight into radiosensitivity in the reproductive stage.\" 838 (2022)","DOI":"10.1016\/j.scitotenv.2022.156224"},{"key":"7","doi-asserted-by":"crossref","unstructured":"FA Cucinotta, MH Kim, V Willingham, et al.  \"Radiation research.\"  170, \"Physical and biological organ dosimetry analysis for international space station astronauts.\" 127 (2008)","DOI":"10.1667\/RR1330.1"},{"key":"8","doi-asserted-by":"crossref","unstructured":"M L\u00f6brich  and PA Jeggo  \"Science.\"  364, \"Hazards of human spaceflight.\" 127 (2019)","DOI":"10.1126\/science.aaw7086"},{"key":"9","doi-asserted-by":"crossref","unstructured":"E Macaeva, M Mysara, WH De Vos, et al.  \"International journal of radiation biology.\"  95, \"Gene expression-based biodosimetry for radiological incidents: assessment of dose and time after radiation exposure.\" 64 (2019)","DOI":"10.1080\/09553002.2018.1511926"},{"key":"10","doi-asserted-by":"crossref","unstructured":"B Andersson, B Langen, P Liu, et al.  \"Frontiers in oncology.\"  13, \"Development of a machine learning framework for radiation biomarker discovery and absorbed dose prediction.\" 1156009 (2023)","DOI":"10.3389\/fonc.2023.1156009"},{"key":"11","doi-asserted-by":"crossref","unstructured":"SA. Amundson,  \"International journal of radiation biology.\"  99, \"Transcriptomics for radiation biodosimetry: progress and challenges.\" 925 (2023)","DOI":"10.1080\/09553002.2021.1928784"},{"key":"12","doi-asserted-by":"crossref","unstructured":"LM Sanders, H Chok, F Samson,  \"Frontiers in Astronomy and Space Sciences.\"  10, \"Batch effect correction methods for NASA GeneLab transcriptomic datasets.\" (2023)","DOI":"10.3389\/fspas.2023.1200132"},{"key":"13","doi-asserted-by":"crossref","unstructured":"Y Zhang, XH Du, L Zhao, et al.  \"International journal of radiation biology.\"  \"Construction of dose prediction model and identification of sensitive genes for space radiation based on single-sample networks under spaceflight conditions.\" (2024)","DOI":"10.1080\/09553002.2024.2327393"},{"key":"14","doi-asserted-by":"crossref","unstructured":"L Hood  and M Flores  \"New Biotechnology.\"  29, \"A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory.\" 613 (2012)","DOI":"10.1016\/j.nbt.2012.03.004"},{"key":"15","doi-asserted-by":"crossref","unstructured":"A-L Barab\u00e1si, N Gulbahce, J Loscalzo,  \"Nature reviews genetics.\"  12, \"Network medicine: a network-based approach to human disease.\" 56 (2011)","DOI":"10.1038\/nrg2918"},{"key":"16","doi-asserted-by":"crossref","unstructured":"Y Huang, X Chang, Y Zhang, et al.  \"Briefings in bioinformatics.\"  22, \"Disease characterization using a partial correlation-based sample-specific network.\" (2021)","DOI":"10.1093\/bib\/bbaa062"},{"key":"17","doi-asserted-by":"crossref","unstructured":"Y Zhang, L Zhao, YQ Sun,  \"NPJ microgravity.\"  10, \"Using single-sample networks to identify the contrasting patterns of gene interactions and reveal the radiation dose-dependent effects in multiple tissues of spaceflight mice.\" (2024)","DOI":"10.1038\/s41526-024-00383-7"},{"key":"18","doi-asserted-by":"crossref","unstructured":"HH Chen, CW Hsueh, CH Lee,  \"Briefings in bioinformatics.\"  24, \"SWEET: a single-sample network inference method for deciphering individual features in disease.\" (2023)","DOI":"10.1093\/bib\/bbad032"},{"key":"19","doi-asserted-by":"crossref","unstructured":"YY Chen, Y Gu, ZX Hu, et al.  \"Briefings in bioinformatics.\"  22, \"Sample-specific perturbation of gene interactions identifies breast cancer subtypes.\" (2021)","DOI":"10.1093\/bib\/bbaa268"},{"key":"20","doi-asserted-by":"crossref","unstructured":"Y Zhang, WY Shi, YQ Sun,  \"BMC genomics.\"  24, \"A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology.\" (2023)","DOI":"10.1186\/s12864-023-09157-z"},{"key":"21","doi-asserted-by":"crossref","unstructured":"H Zhang, F Liu, Y Zhou, et al.  \"Information Sciences.\"  539, \"A hybrid method integrating an elite genetic algorithm with tabu search for the quadratic assignment problem.\" 347 (2020)","DOI":"10.1016\/j.ins.2020.06.036"},{"key":"22","doi-asserted-by":"crossref","unstructured":"LP Li, CR Weinberg, TA Darden, et al.  \"Bioinformatics (Oxford, England).\"  17, \"Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA\/KNN method.\" 1131 (2001)","DOI":"10.1093\/bioinformatics\/17.12.1131"},{"key":"23","doi-asserted-by":"crossref","unstructured":"S Marjit, T Bhattacharyya, B Chatterjee, et al.  \"Computers in Biology and Medicine.\"  \"Simulated annealing aided genetic algorithm for gene selection from microarray data.\" 158 (2023)","DOI":"10.1016\/j.compbiomed.2023.106854"},{"key":"24","doi-asserted-by":"crossref","unstructured":"ML Kuijjer, MG Tung, G Yuan, et al.  \"iScience.\"  14, \"Estimating Sample-Specific Regulatory Networks.\" 226 (2019)","DOI":"10.1016\/j.isci.2019.03.021"},{"key":"26","doi-asserted-by":"crossref","unstructured":"WF Guo, X Yu, QQ Shi, et al.  \"PLoS computational biology.\"  17, \"Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis.\" e1008962 (2021)","DOI":"10.1371\/journal.pcbi.1008962"},{"key":"27","doi-asserted-by":"crossref","unstructured":"M Akhavan  and SMH Hasheminejad  \"Knowledge-Based Systems.\"  262, \"A two-phase gene selection method using anomaly detection and genetic algorithm for microarray data.\" (2023)","DOI":"10.1016\/j.knosys.2022.110249"},{"key":"28","doi-asserted-by":"crossref","unstructured":"YY Zhou, B Zhou, L Pache,  \"Nature communications.\"  10, \"Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.\" (2019)","DOI":"10.1038\/s41467-019-09234-6"},{"key":"29","doi-asserted-by":"crossref","unstructured":"J Pinero, J Sauch, F Sanz, et al.  \"Computational and Structural Biotechnology Journal.\"  19, \"The DisGeNET cytoscape app: Exploring and visualizing disease genomics data.\" 2960 (2021)","DOI":"10.1016\/j.csbj.2021.05.015"},{"key":"30","doi-asserted-by":"crossref","unstructured":"L Zhao, D Mi, Y Sun,  \"Chinese Science Bulletin.\"  63, \"Issues and challenges of space radiation risk assessment in manned deep space exploration missions.\" 1523 (2018)","DOI":"10.1360\/N972018-00071"},{"key":"31","doi-asserted-by":"crossref","unstructured":"Z Nikitaki, M Hol\u00e1, M Don\u00e0,  \"Mutation Research-Reviews in Mutation Research.\"  775, \"Integrating plant and animal biology for the search of novel DNA damage biomarkers.\" 21 (2018)","DOI":"10.1016\/j.mrrev.2018.01.001"},{"key":"32","doi-asserted-by":"crossref","unstructured":"M Moreno-Villanueva, M Wong, T Lu, et al.  \"NPJ microgravity.\"  3, \"Interplay of space radiation and microgravity in DNA damage and DNA damage response.\" 14 (2017)","DOI":"10.1038\/s41526-017-0019-7"},{"key":"33","doi-asserted-by":"crossref","unstructured":"JH Houtgraaf, J Versmissen, WJ van der Giessen,  \"Cardiovascular revascularization medicine: including molecular interventions.\"  7, \"A concise review of DNA damage checkpoints and repair in mammalian cells.\" 165 (2006)","DOI":"10.1016\/j.carrev.2006.02.002"},{"key":"34","doi-asserted-by":"crossref","unstructured":"AV Beatty  and JW Beatty  \"Proceedings of the National Academy of Sciences of the United States of America.\"  49, \"Radiation recovery enhanced through inhibitors of protein synthesis and amino acids.\" 434 (1963)","DOI":"10.1073\/pnas.49.4.434"},{"key":"35","doi-asserted-by":"crossref","unstructured":"E Pariset, S Malkani, E Cekanaviciute, et al.  \"International journal of radiation biology.\"  97, \"Ionizing radiation-induced risks to the central nervous system and countermeasures in cellular and rodent models.\" S132 (2021)","DOI":"10.1080\/09553002.2020.1820598"},{"key":"36","doi-asserted-by":"crossref","unstructured":"PM Klein, VK Parihar, GG Szabo,  \"Neurobiology of disease.\"  151, \"Detrimental impacts of mixed-ion radiation on nervous system function.\" 105252 (2021)","DOI":"10.1016\/j.nbd.2021.105252"},{"key":"37","doi-asserted-by":"crossref","unstructured":"M. Lalkovicova,  \"Neural regeneration research.\"  17, \"Neuroprotective agents effective against radiation damage of central nervous system.\" 1885 (2022)","DOI":"10.4103\/1673-5374.335137"},{"key":"38","doi-asserted-by":"crossref","unstructured":"D Belpomme, P Irigaray, L Hardell,  \"Environmental research.\"  105, \"The multitude and diversity of environmental carcinogens.\" 414 (2007)","DOI":"10.1016\/j.envres.2007.07.002"},{"key":"39","doi-asserted-by":"crossref","unstructured":"WY Park, CI Hwang, CN Im,  \"Oncogene.\"  21, \"Identification of radiation-specific responses from gene expression profile.\" 8521 (2002)","DOI":"10.1038\/sj.onc.1205977"},{"key":"40","doi-asserted-by":"crossref","unstructured":"M Yi, Y Yuan, L Ma,  \"Clin Transl Med.\"  14, \"Inhibition of TGFb1 activation prevents radiation-induced lung fibrosis.\" e1546 (2024)","DOI":"10.1002\/ctm2.1546"},{"key":"41","doi-asserted-by":"crossref","unstructured":"CP Hsiao, B Daly, MK Chen,  \"Nursing research.\"  70, \"Possible Bioenergetic Bio-marker for Chronic Cancer-Related Fatigue.\" 475 (2021)","DOI":"10.1097\/NNR.0000000000000547"},{"key":"42","doi-asserted-by":"crossref","unstructured":"K Kita, YP Wu, S Sugaya,  \"Biochemical and biophysical research communications.\"  274, \"Search for UV-responsive genes in human cells by differential mRNA display: Involvement of human Ras-related GTP-binding protein, Rheb, in UV susceptibility.\" 859 (2000)","DOI":"10.1006\/bbrc.2000.3220"},{"key":"43","doi-asserted-by":"crossref","unstructured":"K Roy, O Chakrabarti, D Mukhopadhyay,  \"Biochemistry and cell biology = Biochimie et biologie cellulaire.\"  92, \"Interaction of Grb2 SH3 domain with UVRAG in an Alzheimer's disease-like scenario.\" 219 (2014)","DOI":"10.1139\/bcb-2014-0001"},{"key":"44","doi-asserted-by":"crossref","unstructured":"X Yue, Z Xiang, Y Yi,  \"Cell death & disease.\"  16, \"Targeting the TRIM14\/USP14 axis enhances radiotherapy efficacy by inducing GPX4 degradation and disrupting ferroptotic defense in HCC.\" 481 (2025)","DOI":"10.1038\/s41419-025-07807-6"},{"key":"45","unstructured":"Y. Qi,  \"Alteration of Tumor-Secreted Immunoregulatory RNAs in Radiation-Induced Antitumor Immunity.\"  (2024)"},{"key":"46","doi-asserted-by":"crossref","unstructured":"MW Epperly, CA Sikora, SJ DeFilippi,  \"Radiation research.\"  157, \"Manganese super-oxide dismutase (SOD2) inhibits radiation-induced apoptosis by stabilization of the mitochondrial membrane.\" 568 (2002)","DOI":"10.1667\/0033-7587(2002)157[0568:MSDSIR]2.0.CO;2"},{"key":"47","doi-asserted-by":"crossref","unstructured":"S Camero, G Vitali, P Pontecorvi,  \"Cells.\"  10, \"DNMT3A and DNMT3B Targeting as an Effective Radiosensitizing Strategy in Embryonal Rhabdomyosarcoma.\" (2021)","DOI":"10.3390\/cells10112956"},{"key":"48","doi-asserted-by":"crossref","unstructured":"J He, Y Guo, J Chen, et al.  \"bioRxiv 2024.\"  \"Exploring the Correlation Between UVB Sensitivity and SLE Activity: Insights into UVB-Driven Pathogenesis in Lupus Erythematosus.\"","DOI":"10.1101\/2024.09.28.615513"},{"key":"49","doi-asserted-by":"crossref","unstructured":"WJ Chen, XY Xie, CY Liu,  \"Cellular signalling.\"  \"IRAK1 deficiency potentiates the efficacy of radiotherapy in repressing cervical cancer development.\" 119 (2024)","DOI":"10.1016\/j.cellsig.2024.111192"},{"key":"50","doi-asserted-by":"crossref","unstructured":"Y Tian, ZG Shi, S Yang, et al.  \"Journal of Neurosurgery.\"  109, \"Changes in myelin basic protein and demyelination in the rat brain within 3 months of single 2-, 10-, or 30-Gy whole-brain radiation treatments.\" 881 (2008)","DOI":"10.3171\/JNS\/2008\/109\/11\/0881"},{"key":"51","doi-asserted-by":"crossref","unstructured":"BV Becker, T Seeger, T Beiert,  \"Health Physics.\"  115, \"Impact of Ionizing Radiation on Electrophysiological Behavior of Human-induced Ipsc-derived Cardiomyocytes on Multielectrode Arrays.\" 21 (2018)","DOI":"10.1097\/HP.0000000000000817"},{"key":"52","doi-asserted-by":"crossref","unstructured":"A Gilbert, V Payet, B Bernay,  \"Frontiers in Bioscience-Landmark.\"  27, \"Label-Free Direct Mass Spectrometry Analysis of the Bystander Effects Induced in Chondrocytes by Chondrosarcoma Cells Irradiated with X-rays and Carbon Ions.\" (2022)","DOI":"10.31083\/j.fbl2709277"},{"key":"25","doi-asserted-by":"crossref","unstructured":"MI Love, W Huber, S Anders,  \"Genome Biology.\"  15, \"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.\" 550 (2014)","DOI":"10.1186\/s13059-014-0550-8"}],"container-title":["Radiation Research"],"original-title":[],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:27:33Z","timestamp":1791235653000},"score":1,"resource":{"primary":{"URL":"https:\/\/bioone.org\/journals\/radiation-research\/volume-205\/issue-6\/RADE-24-00188.1\/Integrating-Single-Sample-Networks-and-Genetic-Algorithm-to-Estimate-Radiation\/10.1667\/RADE-24-00188.1.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,14]]},"references-count":52,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6,1]]}},"URL":"https:\/\/doi.org\/10.1667\/rade-24-00188.1","relation":{},"ISSN":["0033-7587","1938-5404"],"issn-type":[{"value":"0033-7587","type":"print"},{"value":"1938-5404","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,14]]}}}