Caminando a hombros de gigantes: intersección entre la genómica y la IA
DOI:
https://doi.org/10.56050/01205498.1653Palabras clave:
Inteligencia artificial (IA), terapia, diagnóstico, medicina de precisión, GenómicaResumen
El presente manuscrito revisa las aplicaciones actuales de la inteligencia artificial (IA) en genómica funcional. La reciente explosión de la IA sigue a los notables logros que ha hecho posible el “aprendizaje profundo”, junto con una explosión de “grandes conjuntos de datos” que pueden satisfacer su necesidad. Esto ha sido posible gracias a los enormes avances en el campo de las tecnologías de alto rendimiento, aplicadas para determinar cómo los componentes individuales de un sistema biológico trabajan juntos para lograr diferentes procesos. Las disciplinas que contribuyen a este volumen de datos se conocen colectivamente como genómica funcional. Consisten en estudios de: i) la información contenida en el ADN (genómica); ii) las modificaciones que el ADN puede sufrir de forma reversible (epigenómica); iii) las transcripciones de ARN originadas por un genoma (transcriptómica); iv) el conjunto de modificaciones químicas que decoran diferentes tipos de transcripciones del ARN (epitranscriptómica); v) los productos de las transcripciones que codifican proteínas (proteómica); y vi) las pequeñas moléculas producidas a partir del metabolismo celular (metabolómica) presentes en un organismo o sistema en un momento dado, en condiciones fisiológicas o patológicas.
Biografía del autor/a
Andrés F. Cardona, Centro de Tratamiento e Investigación sobre cáncer Luis Carlos Sarmiento Angulo (CTIC)
Dirección de Investigación y Educación, Centro de Tratamiento e Investigación sobre cáncer Luis Carlos Sarmiento Angulo (CTIC), Bogotá, Colombia.
² Fundación para la Investigación Clínica y Molecular Aplicada del Cáncer – FICMAC, Bogotá, Colombia.
³ Grupo de Investigación en Oncología Molecular y Sistemas Biológicos (Fox-G), Universidad El Bosque, Bogotá, Colombia.
Alejandro Ruíz Patiño, Fundación para la Investigación Clínica y Molecular Aplicada del Cáncer – FICMAC
Fundación para la Investigación Clínica y Molecular Aplicada del Cáncer – FICMAC, Bogotá, Colombia.
Grupo de Investigación en Oncología Molecular y Sistemas Biológicos (Fox-G), Universidad El Bosque, Bogotá, Colombia.
Elvira Jaller, Universidad El Bosque
Grupo de Investigación en Oncología Molecular y Sistemas Biológicos (Fox-G), Universidad El Bosque, Bogotá, Colombia.
Grupo Medicina Interna, Instituto Nacional de Cancerología – INC, Bogotá, Colombia.
July Rodríguez, Fundación para la Investigación Clínica y Molecular Aplicada del Cáncer – FICMAC
Fundación para la Investigación Clínica y Molecular Aplicada del Cáncer – FICMAC, Bogotá, Colombia.
Grupo de Investigación en Oncología Molecular y Sistemas Biológicos (Fox-G), Universidad El Bosque, Bogotá, Colombia.
Luis Eduardo Pino, Fundación Santa Fe de Bogotá, Bogotá
Grupo Oncología Clínica, Instituto de Cáncer Carlos Ardila Lülle, Fundación Santa Fe de Bogotá, Bogotá, Colombia.
Referencias bibliográficas
2. Leung MKK, Xiong HY, Lee LJ, Frey BJ. Deep learning of the tissue-regulated splicing code. Bioinformatics. 2014;30:i121–9.
3. Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, et al. Predicting splicing from primary sequence with deep learning. Cell. 2019;176:535–48.
4. Quang D, Xie X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 2016;44:e107.
5. Poplin R, Chang PC, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36:983–7.
6. Wick RR, Judd LM, Holt KE. Performance of neural network basecalling tools for Oxford nanopore sequencing. Genome Biol. 2019;20:129.
7. Luo R, Sedlazeck FJ, Lam TW, Schatz MC. A multi-task convolutional deep neural network for variant calling in single molecule sequencing. Nat Commun. 2019 Mar 1;10(1):998.
8. Tang H, Thomas PD. Tools for predicting the functional impact of nonsynonymous genetic variation. Genetics. 2016;203:635–47.
9. Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015;31:761–3.
10. Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46:D1062–7.
11. FDA approves stroke-detecting AI software. Nat Biotechnol. 2018;36:290.
12. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015;12:931–4.
13. Zhou J, Park CY, Theesfeld CL, Wong AK, Yuan Y, Scheckel C, et al. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nat Genet. 2019;51:973–80.
14. Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet. 2018;50:1171–9.
15. Telenti A, Pierce LCT, Biggs WH, Di Iulio J, Wong EHM, Fabani MM, et al. Deep sequencing of 10,000 human genomes. Proc Natl Acad Sci U S A. 2016;113:11901–6.
16. Erikson GA, Bodian DL, Rueda M, Molparia B, Scott ER, Scott-Van Zeeland AA, et al. Whole-genome sequencing of a healthy aging cohort. Cell. 2016;165:1002–11.
17. Gurovich Y, Hanani Y, Bar O, Nadav G, Fleischer N, Gelbman D, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med. 2019;25:60–4.
18. Lumaka A, Cosemans N, Lulebo Mampasi A, Mubungu G, Mvuama N, Lubala T, et al. Facial dysmorphism is influenced by ethnic background of the patient and of the evaluator. Clin Genet. 2017;92:166–71.
19. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–91.
20. Hsieh T-C, Mensah MA, Pantel JT, Aguilar D, Bar O, Bayat A, et al. PEDIA: prioritization of exome data by image analysis. Genet Med. 2019.
21. Dolgin E. AI face-scanning app spots signs of rare genetic disorders. Nature. 2019.
22. Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A. 2018;115:E2970–9.
23. Clark MM, Hildreth A, Batalov S, Ding Y, Chowdhury S, Watkins K, et al. Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Sci Transl Med. 2019;11:eaat6177.
24. Lello L, Avery SG, Tellier L, Vazquez AI. de los Campos G, Hsu SDH. Accurate genomic prediction of human height. Genetics. 2018;210:477–97.
25. Wang B, Mezlini AM, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014 Mar; 11(3):333-7.
26. Meng C, Zeleznik OA, Thallinger GG, et al. Dimension reduction techniques for the integrative analysis of multiomics data. Brief Bioinform. 2016 Jul; 17(4):628-41.
27. Bersanelli M, Mosca E, Remondini D, et al. Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics. 2016 Jan 20; 17 Suppl 2:15.
28. Ibrahim R, Pasic M, Yousef GM. Omics for personalized medicine: defining the current we swim in. Expert Rev Mol Diagn. 2016 Jul;16(7):719-22.
29. MacArthur DG, Manolio TA, Dimmock DP, et al. Guidelines for investigating causality of sequence variants in human disease. Nature. 2014 Apr 24; 508(7497):469-76.
30. Tang H, Thomas PD. Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation. Genetics. 2016 Jun; 203(2):635-47.
31. Richards S, Aziz N, Bale S, et al; ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May;17(5):405-24.
32. Li MM, Datto M, Duncavage EJ, et al. Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn. 2017 Jan;19(1):4-23.
33. Lindeman NI, Cagle PT, Aisner DL, et al. Updated Molecular Testing Guideline for the Selection of Lung Cancer Patients for Treatment With Targeted Tyrosine Kinase Inhibitors: Guideline From the College of American Pathologists, the International Association for the Study of Lung Cancer, and the Association for Molecular Pathology. J Mol Diagn. 2018 Mar;20(2):129-159. doi: 10.1016/j.jmoldx.2017.11.004. Epub 2018 Jan 23.
34. Kundra R, Zhang H, Sheridan R, et al. OncoTree: A Cancer Classification System for Precision Oncology. JCO Clin Cancer Inform. 2021 Feb;5:221-230. doi: 10.1200/CCI.20.00108.
35. Weinstein JN, Collisson EA, Mills GB, et al; Cancer Genome Atlas Research Network. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013 Oct; 45(10):1113-20.
36. Davis RJ, Gönen M, Margineantu DH, et al. Pan-cancer transcriptional signatures predictive of oncogenic mutations reveal that Fbw7 regulates cancer cell oxidative metabolism. Proc Natl Acad Sci U S A. 2018 May 22;115(21):5462-5467.
37. Castro M, Pampana A, Alam A, et al. Combination chemotherapy versus temozolomide for patients with methylated MGMT (m-MGMT) glioblastoma: results of computational biological modeling to predict the
magnitude of treatment benefit. J Neurooncol. 2021 Jul;153(3):393-402. doi: 10.1007/s11060-021-03780-0.
38. Zhang Z, He T, Huang L, et al. Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system. Comput Struct Biotechnol J. 2021 Apr 12;19:2329-2346. doi: 10.1016/j.csbj.2021.04.025.
39. Nosi V, Luca A, Milan M, et al. MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning. Int J Mol Sci. 2021 Apr 19;22(8):4217. doi: 10.3390/ijms22084217.
40. Chakraborty D, Ivan C, Amero P, et al. Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer. Cancers (Basel). 2021 Jul 9;13(14):3450. doi: 10.3390/cancers13143450.
41. Ding J, Bashashati A, Roth A, et al. Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data. Bioinformatics. 2012 Jan 15; 28(2):167-75.
42. Hao Y, Xuei X, Li L, et al. RareVar: A Framework for Detecting Low-Frequency Single-Nucleotide Variants. J Comput Biol. 2017 Jul;24(7):637-646.
43. Spinella JF, Mehanna P, Vidal R, et al. SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing. MC Genomics. 2016 Nov 14;17(1):912.
44. ST, et al (2018) Deep learning mutation prediction enables early-stage lung cancer detection in liquid biopsy. IN: ICLR 2018 conference, Vancouver.
45. Wood DE, White JR, Georgiadis A, et al. A machine learning approach for somatic mutation discovery. Sci Transl Med. 2018 Sep 5;10(457):141.
46. Antaki D, Brandler WM, Sebat J. SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes. Bioinformatics. 2018 May 15; 34(10):1774-1777.
47. Onsongo G, Baughn LB, Bower M, et al. CNV-RF Is a Random Forest-Based Copy Number Variation Detection Method Using Next-Generation Sequencing. J Mol Diagn. 2016 Nov; 18(6):872-881.
48. Going Deeper with Convolutions (2014) arXiv:1409.4842v1.
49. Caravagna G, Giarratano Y, Ramazzotti D, et al. Detecting repeated cancer evolution from multi-region tumor sequencing data. Nat Methods. 2018 Sep;15(9):707- 714.
50. Qi H, Zhang H, Zhao Y, et al. MVP predicts the pathogenicity of missense variants by deep learning. Nat Commun. 2021 Jan 21;12(1):510. doi: 10.1038/s41467-020- 20847-0.
51. Zomnir MG, Lipkin L, Pacula M, et al. Artificial Intelligence Approach for Variant Reporting. JCO Clin Cancer Inform. 2018;2:CCI.16.00079. doi: 10.1200/CCI.16.00079. Epub 2018 Mar 22.
52. Krallinger M, Vazquez M, Leitner F, et al. The ProteinProtein Interaction tasks of BioCreative III: classification/ ranking of articles and linking bio-ontology concepts to full text. BMC Bioinformatics. 2011 Oct 3;12 Suppl 8(Suppl 8):S3. doi: 10.1186/1471-2105-12-S8-S3.
53. Habibi M, Weber L, Neves M, et al. Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics. 2017 Jul 15;33(14):i37-i48..
Cómo citar
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2022 Medicina
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Copyright
ANM de Colombia
Los autores deben declarar revisión, validación y aprobación para publicación del manuscrito, además de la cesión de los derechos patrimoniales de publicación, mediante un documento que debe ser enviado antes de la aparición del escrito. Puede solicitar el formato a través del correo revistamedicina@anmdecolombia.org.co o descargarlo directamente Documento Garantías y cesión de derechos.docx
Copyright
ANM de Colombia
Authors must state that they reviewed, validated and approved the manuscript's publication. Moreover, they must sign a model release that should be sent.
