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Towards a Logic Programming Tool for Cancer Data Analysis

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Języki publikacji
EN
Abstrakty
EN
The main goal of this work is to propose a tool-chain capable of analyzing a data collection of temporally qualified (genetic) mutation profiles, i.e., a collection of DNA-sequences (genes) that present variations with respect to their “healthy” versions. We implemented a system consisting of a front-end, a reasoning core, and a post-processor: the first transforms the input data retrieved from medical databases into a set of logical facts, while the last displays the computation results as graphs. Concerning the reasoning core, we employed the Answer Set Programming paradigm, which is capable of deducing complex information from data. However, since the system is modular, this component can be replaced by any logic programming tool for different kinds of data analysis. Indeed, we tested the use of a probabilistic inductive logic programming core.
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Rocznik
Strony
299--319
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
  • Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Università degli Studi di Udine, Italy
  • Department of Applied Informatics, Alpen-Adria-Universität Klagenfurt, Austria
  • Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Università degli Studi di Udine, Italy
  • Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Università degli Studi di Udine, Italy
  • Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Università degli Studi di Udine, Italy
Bibliografia
  • [1] Gelfond M, Kahl Y. Knowledge representation, reasoning, and the design of intelligent agents: The answer-set programming approach. Cambridge University Press; 2013. ISBN:9781107776968.
  • [2] Muggleton S. Inductive Logic Programming. New Generation Comput. 1991;8(4):295-318. URL https://doi.org/10.1007/BF03037089.
  • [3] De Raedt L. Logical and Relational Learning. Cognitive Technologies. Springer; 2008. doi:10.1007/978-3-540-68856-3.
  • [4] Riguzzi F, Bellodi E, Zese R. A History of Probabilistic Inductive Logic Programming. Front Robotics and AI. 2014;2014. URL https://doi.org/10.3389/frobt.2014.00006.
  • [5] Riguzzi F. Foundations of Probabilistic Logic Programming. River Publishers; 2018. ISBN-10:8770220182, 13:978-8770220187.
  • [6] Srinivasan A, King RD, Muggleton S, Sternberg MJE. Carcinogenesis Predictions Using ILP. In: Lavrac N, Dzeroski S, eds. Proc of ILP-97, LNCS vol. 129, Springer; 1997. p. 273-287.
  • [7] Muggleton S. Inverse Entailment and Progol. New Generation Comput. 1995;13(3&4):245-286.
  • [8] Qiu Y, Shimada K, Hiraoka N, Maeshiro K, Ching WK, Aoki-Kinoshita KF, et al. Knowledge discovery for pancreatic cancer using inductive logic programming. IET Systems Biology. 2014;8:162-168. doi:10.1049/iet-syb.2013.0044.
  • [9] Bevilacqua V, Chiarappa P, Mastronardi G, Menolascina F, Paradiso A, Tommasi S. Identification of Tumor Evolution Patterns by Means of Inductive Logic Programming. Genomics, Proteomics & Bioinformatics. 2008;6(2):91-97. doi: 10.1016/S1672-0229(08)60024-8.
  • [10] Dal Palù A, Dovier A, Formisano A, Pontelli E. ASP Applications in Bio-informatics: A Short Tour. KI. 2018;32(2-3):157-164. URL https://doi.org/10.1007/s13218-018-0551-y.
  • [11] Dal Palù A, Dovier A, Formisano A, Pontelli E. Exploring Life: Answer Set Programming in Bioinformatics. In: Kifer M, Liu Y, editors. Declarative Logic Programming, Theory, Systems, and Applications. ACM Press; 2018. p. 359-426. URL https://doi.org/10.1145/3191315.3191323.
  • [12] Erdem E. Applications of Answer Set Programming in Phylogenetic Systematics. In: Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning. LNCS vol. 6565; Springer 2011. p. 415-431. doi:10.1007/978-3-642-20832-4_26.
  • [13] Dal Palù A, Dovier A, Formisano A, Policriti A, Pontelli E. Logic Programming Applied to Genome Evolution in Cancer. In: Proc of the 31st CILC, CEUR vol. 1645, 2016. p. 148-157. ID:5511211.
  • [14] Le T, Nguyen H, Pontelli E, Son TC. ASP at Work: An ASP Implementation of PhyloWS. In: Dovier A, Costa VS, eds, TC of ICLP 2012. vol. 17 of LIPIcs, p. 359-369. doi:10.4230/LIPIcs.ICLP.2012.359.
  • [15] Schwartz R, Schäffer A. The evolution of tumour phylogenetics: principles and practice. Nature Review Genetics. 2017;18(4):213-229. doi:10.1038/nrg.2016.170.
  • [16] Lecca P, Casiraghi N, Demichelis F. Defining order and timing of mutations during cancer progression: the TO-DAG probabilistic graphical model. Frontiers in Genetics. 2015; 6:309. doi:10.3389/fgene.2015.00309.
  • [17] Yuan K, Sakoparnig T, Markowetz F, Beerenwinkel N. BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies. Genome biology. 2015;16(1):36. doi:10.1186/s13059-015-0592-6.
  • [18] Ross EM, Markowetz F. OncoNEM: inferring tumor evolution from single-cell sequencing data. Genome biology. 2016;17(1):69. doi:10.1186/s13059-016-0929-9.
  • [19] Jahn K, Kuipers J, Beerenwinkel N. Tree inference for single-cell data. Genome biology. 2016;17(1):86. URL https://doi.org/10.1186/s13059-016-0936-x,
  • [20] Davis A, Navin NE. Computing tumor trees from single cells. Genome biology. 2016;17(1):113.
  • [21] Zafar H, Tzen A, Navin N, Chen K, Nakhleh L. SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models. Genome biology. 2017;18(1):178. URL https://doi.org/10.1186/s13059-017-1311-2.
  • [22] Caravagna G, Graudenzi A, Ramazzotti D, Sanz-Pamplona R, Sano LD, Mauri G, et al. Algorithmic methods to infer the evolutionary trajectories in cancer progression. PNAS 2016;113(28):E4025-E4034. URL https://doi.org/10.1073/pnas.1520213113.
  • [23] Ramazzotti D, Caravagna G, Olde Loohuis L, Graudenzi A, Korsunsky I, Mauri G, et al. CAPRI: efficient inference of cancer progression models from cross-sectional data. Bioinformatics. 2015;31(18):3016-3026. URL https://doi.org/10.1093/bioinformatics/btv296.
  • [24] Suppes P. A probabilistic theory of causality. North-Holland Publishing Company Amsterdam; 1970.
  • [25] Misra N, Szczurek E, Vingron M. Inferring the paths of somatic evolution in cancer. Bioinformatics. 2014;30(17):2456-2463. URL https://doi.org/10.1093/bioinformatics/btu319.
  • [26] Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Discovery. 2012;2:1-6. doi:10.1158/2159-8290.CD-12-0095.
  • [27] Gao J, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science Signaling. 2013;2(6):269. Available from: https://doi.org/10.1126/scisignal.2004088.
  • [28] NCBI. https://www.ncbi.nlm.nih.gov/;.
  • [29] MAF. https://wiki.nci.nih.gov/display/TCGA/Mutation+Annotation+Format;.
  • [30] HUGO. http://www.genenames.org/;.
  • [31] Eirew P, et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature. 2014;518(7539):422-426. Available from: https://doi.org/10.1038/nature13952.
  • [32] Network TCGA. Comprehensive molecular portraits of human breast tumours. Nature. 2012; Available from: https://doi.org/10.1038/nature11412.
  • [33] Gebser M, Kaminski R, Kaufmann B, Schaub T. Answer Set Solving in Practice. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers; 2012. ISBN:978-1-60845-971-1.
  • [34] GraphViz. http://www.graphviz.org/;.
  • [35] Bellodi E, Riguzzi F. Structure learning of probabilistic logic programs by searching the clause space. Theory and Practice of Logic Programming. 2013;15(4):213,229. doi:10.1017/S1471068413000689.
  • [36] Riguzzi F. Probabilistic Inductive Logic Programming. ALP Newsletter. 2014 March(1). arXiv:1405.0720 [cs.AI].
  • [37] Govek K, Sikes C, Oesper L. A consensus approach to infer tumor evolutionary histories. In: Proc of the 2018 ACM Int’l Conf on Bioinformatics, Computational Biology, and Health Informatics, p. 63-72. URL https://doi.org/10.1145/3233547.3233584.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-278bed30-04a4-4daa-afb7-3a1285d1dd16
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