1. bookVolume 29 (2019): Issue 1 (March 2019)
    Exploring Complex and Big Data (special section, pp. 7-91), Johann Gamper, Robert Wrembel (Eds.)
Journal Details
License
Format
Journal
eISSN
2083-8492
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English
Open Access

Machine learning techniques combined with dose profiles indicate radiation response biomarkers

Published Online: 29 Mar 2019
Volume & Issue: Volume 29 (2019) - Issue 1 (March 2019) - Exploring Complex and Big Data (special section, pp. 7-91), Johann Gamper, Robert Wrembel (Eds.)
Page range: 169 - 178
Received: 28 Feb 2018
Accepted: 18 Oct 2018
Journal Details
License
Format
Journal
eISSN
2083-8492
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English

Abbott, A. (2015). Researchers pin down risks of low-dose radiation, Nature523(7558): 17–8.10.1038/523017a26135428Search in Google Scholar

Alexa, A. and Rahnenfuhrer, J. (2010). topGO: Enrichment analysis for gene ontology, R Package Version 2.30.Search in Google Scholar

Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese J.C., Richardson, J.E., Ringwald, M., Rubin, G.M. and Sherlock, G. (2000). Gene Ontology: Tool for the unification of biology, Nature Genetics25(1): 25.10.1038/75556303741910802651Search in Google Scholar

Berger, J.O. and Pericchi, L.R. (1996). The intrinsic Bayes factor for model selection and prediction, Journal of the American Statistical Association91(433): 109–122.10.1080/01621459.1996.10476668Search in Google Scholar

Bersani, C., Xu, L., Vilborg, A., Lui, W. and Wiman, K. (2014). Wig-1 regulates cell cycle arrest and cell death through the p53 targets FAS and 14-3-3σ, Oncogene33(35): 4407.10.1038/onc.2013.594415098724469038Search in Google Scholar

Bolstad, B.M., Irizarry, R.A., Åstrand, M. and Speed, T.P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias, Bioinformatics19(2): 185–193.10.1093/bioinformatics/19.2.18512538238Search in Google Scholar

Brenner, D.J., Doll, R., Goodhead, D.T., Hall, E.J., Land, C.E., Little, J.B., Lubin, J.H., Preston, D.L., Preston, R.J., Puskin, J.S., Ron, E., Sachs, R.K., Samet, J.M., Setlow, R.B. and Zaider, M. (2003). Cancer risks attributable to low doses of ionizing radiation: Assessing what we really know, Proceedings of the National Academy of Sciences100(24): 13761–13766.10.1073/pnas.223559210028349514610281Search in Google Scholar

Brodsky, R.A., Vala, M.S., Barber, J.P., Medof, M.E. and Jones, R.J. (1997). Resistance to apoptosis caused by PIG-A gene mutations in paroxysmal nocturnal hemoglobinuria, Proceedings of the National Academy of Sciences94(16): 8756–8760.10.1073/pnas.94.16.8756231149238050Search in Google Scholar

Cruz-Garcia, L., O’Brien, G., Donovan, E., Gothard, L., Boyle, S., Laval, A., Testard, I., Ponge, L., Woźniak, G., Miszczyk, L., Candéias, S.M., Ainsbury E., Widlak, P., Somaiah, N. and Badie, C. (2018). Influence of confounding factors on radiation dose estimation in in vivo validated transcriptional biomarkers, Health Physics115(1): 90–101.10.1097/HP.0000000000000844596763529787434Search in Google Scholar

Dai, M., Wang, P., Boyd, A.D., Kostov, G., Athey, B., Jones, E.G., Bunney, W.E., Myers, R.M., Speed, T.P., Akil, H., Watson, S.J. and Meng, F. (2005). Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data, Nucleic Acids Research33(20): e175–e175.10.1093/nar/gni179128354216284200Search in Google Scholar

Elf, A.-K., Bernhardt, P., Hofving, T., Arvidsson, Y., Forssell-Aronsson, E., Wängberg, B., Nilsson, O. and Johanson, V. (2017). NAMPT inhibitor GMX1778 enhances the efficacy of 177Lu-DOTATATE treatment of neuroendocrine tumors, Journal of Nuclear Medicine58(2): 288–292.10.2967/jnumed.116.17758427688470Search in Google Scholar

Fargeas, A., Albera, L., Kachenoura, A., Dréan, G., Ospina, J.-D., Coloigner, J., Lafond, C., Delobel, J.-B., De Crevoisier, R. and Acosta, O. (2015). On feature extraction and classification in prostate cancer radiotherapy using tensor decompositions, Medical Engineering and Physics37(1): 126–131.10.1016/j.medengphy.2014.08.00925443534Search in Google Scholar

Finnon, P., Kabacik, S., MacKay, A., Raffy, C., AHern, R., Owen, R., Badie, C., Yarnold, J. and Bouffler, S. (2012). Correlation of in vitro lymphocyte radiosensitivity and gene expression with late normal tissue reactions following curative radiotherapy for breast cancer, Radiotherapy and Oncology105(3): 329–336.10.1016/j.radonc.2012.10.00723157981Search in Google Scholar

Francescatto, M., Chierici, M., Dezfooli, S.R., Zandonà, A., Jurman, G. and Furlanello, C. (2018). Multi-omics integration for neuroblastoma clinical endpoint prediction, Biology Direct13(1): 5.10.1186/s13062-018-0207-8590772229615097Search in Google Scholar

Guidi, G., Maffei, N., Vecchi, C., Gottardi, G., Ciarmatori, A., Mistretta, G. M., Mazzeo, E., Giacobazzi, P., Lohr, F. and Costi, T. (2017). Expert system classifier for adaptive radiation therapy in prostate cancer, Australasian Physical & Engineering Sciences in Medicine40(2): 337–348.10.1007/s13246-017-0535-528290067Search in Google Scholar

Jagga, Z. and Gupta, D. (2015). Machine learning for biomarker identification in cancer research—developments toward its clinical application, Personalized Medicine12(4): 371–387.10.2217/pme.15.529771660Search in Google Scholar

Johnson, W.E., Li, C. and Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods, Biostatistics8(1): 118–127.10.1093/biostatistics/kxj03716632515Search in Google Scholar

Joiner, M.C. (2004). A simple α/β-independent method to derive fully isoeffective schedules following changes in dose per fraction, International Journal of Radiation Oncology Biology Physics58(3): 871–875.10.1016/j.ijrobp.2003.10.03614967444Search in Google Scholar

Jonckheere, A.R. (1954). A distribution-free k-sample test against ordered alternatives, Biometrika41(1/2): 133–145.10.1093/biomet/41.1-2.133Search in Google Scholar

Kabacik, S., Mackay, A., Tamber, N., Manning, G., Finnon, P., Paillier, F., Ashworth, A., Bouffler, S. and Badie, C. (2011). Gene expression following ionising radiation: Identification of biomarkers for dose estimation and prediction of individual response, International Journal of Radiation Biology87(2): 115–129.10.3109/09553002.2010.51942421067298Search in Google Scholar

Kabacik, S., Manning, G., Raffy, C., Bouffler, S. and Badie, C. (2015). Time, dose and ataxia telangiectasia mutated (ATM) status dependency of coding and noncoding RNA expression after ionizing radiation exposure, Radiation Research183(3): 325–337.10.1667/RR13876.125738893Search in Google Scholar

Kong, X., Liu, N. and Xu, X. (2014). Bioinformatics analysis of biomarkers and transcriptional factor motifs in down syndrome, Brazilian Journal of Medical and Biological Research47(10): 834–841.10.1590/1414-431X20143792Search in Google Scholar

Krol, L. (2015). Distributed Monte Carlo feature selection: Extracting informative features out of multidimensional problems with linear speedup, in S. Kozielski et al. (Eds.), Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery, Springer, Cham, pp. 463–474.Search in Google Scholar

Manning, G., Kabacik, S., Finnon, P., Bouffler, S. and Badie, C. (2013). High and low dose responses of transcriptional biomarkers in ex vivo X-irradiated human blood, International Journal of Radiation Biology89(7): 512–522.10.3109/09553002.2013.769694Search in Google Scholar

Meehan, T.F., Vasilevsky, N.A., Mungall, C.J., Dougall, D.S., Haendel, M.A., Blake, J.A. and Diehl, A.D. (2013). Ontology based molecular signatures for immune cell types via gene expression analysis, BMC Bioinformatics14(1): 263.10.1186/1471-2105-14-263Search in Google Scholar

Mullenders, L., Atkinson, M., Paretzke, H., Sabatier, L. and Bouffler, S. (2009). Assessing cancer risks of low-dose radiation, Nature Reviews Cancer9(8): 596.10.1038/nrc2677Search in Google Scholar

Papiez, A., Finnon, P., Badie, C., Bouffler, S. and Polanska, J. (2014). Integrating expression data from different microarray platforms in search of biomarkers of radiosensitivit, International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain, Vol. 1, pp. 484–493.Search in Google Scholar

Park, B., Yee, C. and Lee, K.-M. (2014). The effect of radiation on the immune response to cancers, International Journal of Molecular Sciences15(1): 927–943.10.3390/ijms15010927Search in Google Scholar

Parmar, C., Grossmann, P., Bussink, J., Lambin, P. and Aerts, H.J. (2015). Machine learning methods for quantitative radiomic biomarkers, Scientific Reports5(13087): 13087.10.1038/srep13087Search in Google Scholar

Ray, M., Yunis, R., Chen, X. and Rocke, D.M. (2012). Comparison of low and high dose ionising radiation using topological analysis of gene coexpression networks, BMC Genomics13(1): 190.10.1186/1471-2164-13-190Search in Google Scholar

Reinhardt, M.J., Kubota, K., Yamada, S., Iwata, R. and Yaegashi, H. (1997). Assessment of cancer recurrence in residual tumors after fractionated radiotherapy: A comparison of fluorodeoxyglucose, L-methionine and thymidine, The Journal of Nuclear Medicine38(2): 280.Search in Google Scholar

Schmid, P.R., Palmer, N.P., Kohane, I.S. and Berger, B. (2012). Making sense out of massive data by going beyond differential expression, Proceedings of the National Academy of Sciences109(15): 5594–5599.10.1073/pnas.1118792109Search in Google Scholar

Shao, L., Luo, Y. and Zhou, D. (2014). Hematopoietic stem cell injury induced by ionizing radiation, Antioxidants & Redox Signaling20(9): 1447–1462.10.1089/ars.2013.5635Search in Google Scholar

Terpstra, T.J. (1952). The asymptotic normality and consistency of Kendall’s test against trend, when ties are present in one ranking, Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen55(1): 327–333.10.1016/S1385-7258(52)50043-XSearch in Google Scholar

UNSCEAR (2000). Sources and Effects of Ionizing Radiation, Vol. 1, United Nations Publications, New York, NY.Search in Google Scholar

Weichselbaum, R.R., Hallahan, D., Fuks, Z. and Kufe, D. (1994). Radiation induction of immediate early genes: Effectors of the radiation-stress response, International Journal of Radiation Oncology, Biology, Physics30(1): 229–234.10.1016/0360-3016(94)90539-8Search in Google Scholar

Yarnold, J., Ashton, A., Bliss, J., Homewood, J., Harper, C., Hanson, J., Haviland, J., Bentzen, S. and Owen, R. (2005). Fractionation sensitivity and dose response of late adverse effects in the breast after radiotherapy for early breast cancer: Long-term results of a randomised trial, Radiotherapy and Oncology75(1): 9–17.10.1016/j.radonc.2005.01.00515878095Search in Google Scholar

Zhan, Q. (2005). GADD45A, a p53-and BRCA1-regulated stress protein, in cellular response to DNA damage, Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis569(1): 133–143.10.1016/j.mrfmmm.2004.06.05515603758Search in Google Scholar

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