Öz
Aim: Copy number variations (CNVs), comprising approximately two percent of the human genome, represent specific DNA segment deletions or duplications. While these structural variations may be present in healthy populations, they can also manifest clinically significant implications. The clinical interpretation of identified CNVs constitutes a complex process necessitating comprehensive family studies.
The interpretation of clinical and genetic data often presents challenges in achieving definitive conclusions. Machine learning algorithms have emerged as increasingly valuable tools in medical applications, particularly in genetics where large-scale datasets predominate. This investigation aimed to evaluate the implementation of machine learning algorithms for the clinical assessment of copy number variations.
Materials and Methods: The study methodology comprised an initial pilot analysis utilizing ISCA consortium data (n=11,989 variants), followed by a comprehensive analysis of ClinVar database variants (n= 66803). The variants were stratified into five clinical classification categories (Benign, Likely Benign, VUS, Likely Pathogenic, and Pathogenic). Analyses were conducted using the Microsoft Azure Machine Learning Studio platform, implementing various machine learning algorithms (Multiclass Decision Trees, Logistic Regression, and Neural Network) with a 70:30 training-testing data partition.
Results: The ISCA dataset analysis demonstrated an average accuracy of 0.96 utilizing multiclass decision trees, while the ClinVar dataset achieved 0.86 accuracy with the same algorithmic approach. The model exhibited predictive accuracies of 74.8%, 77.6%, and 62.6% for pathogenic, benign, and variants of unknown significance, respectively. Frequently occurring variants demonstrated superior predictive accuracy, and binary classification (benign/pathogenic) yielded an enhanced average accuracy of 0.90.
Conclusion: This investigation demonstrates the feasibility of developing a preliminary machine learning model for the clinical evaluation and potential automated classification of copy number variants.
Anahtar Kelimeler
Copy Number Variations Machine Learning Genetics Bioinformatics
Kaynakça
- Sebat J, Lakshmi B, Troge J, Alexander J, Young J, Lundin P, et al. Large-scale copy number polymorphism in the human genome. Science. 2004 Jul 23;305(5683):525–8.
- Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, et al. Global variation in copy number in the human genome. Nature. 2006 Nov;444(7118):444–54.
- Albertson DG, Pinkel D. Genomic microarrays in human genetic disease and cancer. Hum Mol Genet. 2003 Oct 15;12(suppl 2):R145–52.
- Slavotinek AM. Novel microdeletion syndromes detected by chromosome microarrays. Hum Genet [Internet]. 2008 Aug 30 [cited 2019 Nov 3];124(1):1–17. Available from: http://link.springer.com/10.1007/s00439-008- 0513-9
- Freeman JL, Perry GH, Feuk L, Redon R, McCarroll SA, Altshuler DM, et al. Copy number variation: new insights in genome diversity. Genome Res [Internet]. 2006 Aug 1 [cited 2019 Jul 9];16(8):949–61. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16809666
- Database of Genomic Variants [Internet]. [cited 2019 Nov 3]. Available from: http://dgv.tcag.ca/dgv/app/home
- Firth H V., Richards SM, Bevan AP, Clayton S, Corpas M, Rajan D, et al. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. The American Journal of Human Genetics [Internet]. 2009 Apr [cited 2019 Nov 3];84(4):524–33. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0002929709001074
- Kearney HM, Thorland EC, Brown KK, Quintero-Rivera F, South ST. American College of Medical Genetics standards and guidelines for interpretation and reporting of postnatal constitutional copy number variants. Genetics in Medicine. 2011 Jul 15;13(7):680–5.
- Shalev-Shwartz Shai, Ben-David Shai. Understanding machine learning : from theory to algorithms. 397 p.
- Isakov O, Dotan I, Ben-Shachar S. Machine Learning–Based Gene Prioritization Identifies Novel Candidate Risk Genes for Inflammatory Bowel Disease. Inflamm Bowel Dis. 2017 Sep 1;23(9):1516–23.
- Ainscough BJ, Barnell EK, Ronning P, Campbell KM, Wagner AH, Fehniger TA, et al. A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nat Genet. 2018;50(12):1735–43.
- Barber D. Bayesian reasoning and machine learning. Cambridge University Press; 2012. 697 p.
- Alpaydin E. Machine Learning - Ethem Alpaydin. 2016 [cited 2025 Jan 10];112–8. Available from: https://mitpress.mit.edu/9780262529518/machine-learning/
- Beam AL, Drazen JM, Kohane IS, Leong TY, Manrai AK, Rubin EJ. Artificial Intelligence in Medicine. New England Journal of Medicine [Internet]. 2023 Mar 30 [cited 2024 Aug 9];388(13):1220–1. Available from: https://www.nejm.org/doi/full/10.1056/NEJMe2206291
- Shotton J, Sharp T, Kohli P, Nowozin S, Winn J, Criminisi A. Decision Jungles: Compact and Rich Models for Classification [Internet]. 2013 [cited 2019 Nov 27]. Available from: https://www.microsoft.com/en- us/research/publication/decision-jungles-compact-and-rich-models-for-classification/
- Mayoraz E, Alpaydin E. Support vector machines for multi-class classification. In Springer, Berlin, Heidelberg ; 1999 [cited 2019 Nov 25]. p. 833–42. Available from: http://link.springer.com/10.1007/BFb0100551
- Ainscough BJ, Barnell EK, Ronning P, Campbell KM, Wagner AH, Fehniger TA, et al. A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nat Genet [Internet]. 2018 [cited 2019 Nov 10];50(12):1735–43. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30397337
- Miller DT, Adam MP, Aradhya S, Biesecker LG, Brothman AR, Carter NP, et al. Consensus Statement: Chromosomal Microarray Is a First-Tier Clinical Diagnostic Test for Individuals with Developmental Disabilities or Congenital Anomalies. The American Journal of Human Genetics. 2010 May 14;86(5):749–64.
- Kaminsky EB, Kaul V, Paschall J, Church DM, Bunke B, Kunig D, et al. An evidence-based approach to establish the functional and clinical significance of copy number variants in intellectual and developmental disabilities. Genet Med. 2011 Sep;13(9):777–84.
- nstd101 - ClinGen - dbVar Study - NCBI [Internet]. [cited 2019 Nov 18]. Available from: https://www.ncbi.nlm.nih.gov/dbvar/studies/nstd101/
- nstd102 - Clinical Structural Variants - dbVar Study - NCBI [Internet]. [cited 2019 Nov 18]. Available from: https://www.ncbi.nlm.nih.gov/dbvar/studies/nstd102/
- Microsoft Azure Machine Learning Studio (classic) [Internet]. [cited 2019 Nov 18]. Available from: https://studio.azureml.net/
- Spielmann M, Klopocki E. CNVs of noncoding cis-regulatory elements in human disease. Curr Opin Genet Dev. 2013 Jun 1;23(3):249–56.
- Brandt T, Sack LM, Arjona D, Tan D, Mei H, Cui H, et al. Adapting ACMG/AMP sequence variant classification guidelines for single-gene copy number variants. Genetics in Medicine. 2019 Sep 19;1–9.
- Koolen DA, Pfundt R, de Leeuw N, Hehir-Kwa JY, Nillesen WM, Neefs I, et al. Genomic microarrays in mental retardation: A practical workflow for diagnostic applications. Hum Mutat. 2009 Mar 1;30(3):283–92.
- Barber D. Bayesian reasoning and machine learning. Cambridge University Press; 2012. 697 p.
- Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet. 2019 Jan 26;51(1):12–8.
- Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet [Internet]. 2019 Jan 26 [cited 2019 Nov 24];51(1):12–8. Available from: http://www.nature.com/articles/s41588-018-0295-5
- Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature [Internet]. 2021 Aug 26 [cited 2024 Jul 26];596(7873):583–9. Available from: https://pubmed.ncbi.nlm.nih.gov/34265844/
- de Sainte Agathe JM, Filser M, Isidor B, Besnard T, Gueguen P, Perrin A, et al. SpliceAI-visual: a free online tool to improve SpliceAI splicing variant interpretation. Hum Genomics [Internet]. 2023 Dec 1 [cited 2024 Jul 26];17(1). Available from: https://pubmed.ncbi.nlm.nih.gov/36765386/
- Hill T, Unckless RL. A Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data. G3 (Bethesda) [Internet]. 2019 Nov 5 [cited 2019 Nov 17];9(11):3575–82. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31455677
- Lappalainen I, Lopez J, Skipper L, Hefferon T, Spalding JD, Garner J, et al. DbVar and DGVa: public archives for genomic structural variation. Nucleic Acids Res. 2013 Jan;41(Database issue):D936-41.
- Sneddon TP, Church DM. Online resources for genomic structural variation. Methods Mol Biol [Internet]. 2012 [cited 2019 Nov 24];838:273–89. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22228017
- NCBI Variation Summary [Internet]. [cited 2019 Nov 24]. Available from: https://www.ncbi.nlm.nih.gov/dbvar/content/org_summary/
- Mallick S, Li H, Lipson M, Mathieson I, Gymrek M, Racimo F, et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature [Internet]. 2016 Oct 13 [cited 2019 Nov 24];538(7624):201–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27654912
- Rauch A, Schellmoser S, Kraus C, D�rr HG, Trautmann U, Altherr MR, et al. First known microdeletion within the Wolf-Hirschhorn syndrome critical region refines genotype-phenotype correlation. Am J Med Genet. 2001 Apr 1;99(4):338–42.
- Slavotinek AM. Novel microdeletion syndromes detected by chromosome microarrays. Hum Genet. 2008 Aug 30;124(1):1–17.
- Peterson TA, Doughty E, Kann MG. Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants. J Mol Biol. 2013 Nov 1;425(21):4047–63.
- Kearney HM, Thorland EC, Brown KK, Quintero-Rivera F, South ST. American College of Medical Genetics standards and guidelines for interpretation and reporting of postnatal constitutional copy number variants. Genetics in Medicine [Internet]. 2011 Jul 15 [cited 2019 Nov 3];13(7):680–5. Available from: http://www.nature.com/doifinder/10.1097/GIM.0b013e3182217a3a
- Brandt T, Sack LM, Arjona D, Tan D, Mei H, Cui H, et al. Adapting ACMG/AMP sequence variant classification guidelines for single-gene copy number variants. Genetics in Medicine [Internet]. 2019 Sep 19 [cited 2019 Nov 3];1–9. Available from: http://www.nature.com/articles/s41436-019-0655-2
- Hanke RE, Gibbons AT, Casar Berazaluce AM, Ponsky TA. Digital Transformation of Academic Medicine: Breaking Barriers, Borders, and Boredom. J Pediatr Surg [Internet]. 2019 Nov 9 [cited 2019 Nov 27]; Available from: https://www.sciencedirect.com/science/article/pii/S0022346819307729?via%3Dihub
- Al-Mufti F, Kim M, Dodson V, Sursal T, Bowers C, Cole C, et al. Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success. Curr Neurol Neurosci Rep [Internet]. 2019 Nov 13 [cited 2019 Nov 27];19(11):89. Available from: http://link.springer.com/10.1007/s11910-019-0998-8
- Kilic A. Artificial Intelligence and Machine Learning in Cardiovascular Healthcare. Ann Thorac Surg [Internet]. 2019 Nov 7 [cited 2019 Nov 27]; Available from: https://www.sciencedirect.com/science/article/pii/S0003497519316121?via%3Dihub
Öz
Amaç: Kopya sayısı varyasyonları, insan genomunun yaklaşık yüzde ikisinde bulunan belirli DNA bölgelerinin kayıp veya kazançlarıdır. Yapısal varyasyonlar arasında yer alan bu grup, sağlıklı popülasyonda bulunabileceği gibi ilgili bölgenin kayıp veya kazançları klinik tablolarla da ilişkilendirilebilir. Tespit edilen kopya sayısı varyasyonunun klinik olarak yorumlanması, aile çalışmasını da gerektiren karmaşık bir süreçtir. Klinik ve genetik verilerin yorumlanması sürecinde her zaman doğru bilgiye ulaşılamamaktadır. Kullanımı artan makine öğrenme algoritmaları giderek tıp alanında da kullanılmakta ve özellikle büyük veri setlerinin bulunduğu genetik gibi alanlarda giderek önem kazanmaktadır. Bu çalışma ile kopya sayısı varyasyonlarının klinik değerlendirilmesinde makine öğrenme algoritmalarının kullanımı amaçlanmıştır.
Gereç ve Yöntem: Araştırmada öncelikle 11989 varyant bulunan ISCA konsorsiyumu verileri ile pilot analiz gerçekleştirilmiş, sonrasında ClinVar veri tabanından elde edilen 63156 varyantlı veri seti kullanılmıştır. Beş ana sınıfta (Benign, Muhtemel Benign, VUS, Muhtemel Patojenik ve Patojenik) bulunan varyantlar, Microsoft Azure Machine Learning Studio platformunda, %70 eğitim ve %30 test verisi olarak ayrılmış ve çeşitli makine öğrenmesi algoritmaları (Çok Sınıflı Karar Ağaçları, Lojistik Regresyon ve Sinir Ağı) kullanılarak analiz gerçekleştirilmiştir.
Bulgular: ISCA veri seti ile yapılan modelde çok sınıflı karar ağacı ile ortalamada 0,96 doğruluğa ulaşılırken, ClinVar veri setinde yine çok sınıflı karar ağacı ile 0,86 doğruluğa ulaşılmıştır. Bu modelde patojenikler %74.8, benignler %77.6 ve önemi bilinmeyen varyantlar %62.6 oranında doğru tahmin edilmiştir. Çalışmada sık karşılaşılan varyantlar daha yüksek başarı ile tanımlanmış ve örneklemin benign ve patojenik olarak iki sınıflı haline getirilmesi durumunda ise ortalama ve toplamda 0.90 doğruluğa ulaşılmıştır.
Sonuç: Bu çalışma, kopya sayısı varyantlarının klinik değerlendirilmesinde kullanılabilecek ve tanıyı otomatikleştirebilecek öncül bir makine öğrenme modeli oluşturulabileceğini göstermiştir.
Anahtar Kelimeler
Kopya Sayısı Varyasyonları Makine Öğrenme Biyoinformatik Genetik;
Kaynakça
- Sebat J, Lakshmi B, Troge J, Alexander J, Young J, Lundin P, et al. Large-scale copy number polymorphism in the human genome. Science. 2004 Jul 23;305(5683):525–8.
- Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, et al. Global variation in copy number in the human genome. Nature. 2006 Nov;444(7118):444–54.
- Albertson DG, Pinkel D. Genomic microarrays in human genetic disease and cancer. Hum Mol Genet. 2003 Oct 15;12(suppl 2):R145–52.
- Slavotinek AM. Novel microdeletion syndromes detected by chromosome microarrays. Hum Genet [Internet]. 2008 Aug 30 [cited 2019 Nov 3];124(1):1–17. Available from: http://link.springer.com/10.1007/s00439-008- 0513-9
- Freeman JL, Perry GH, Feuk L, Redon R, McCarroll SA, Altshuler DM, et al. Copy number variation: new insights in genome diversity. Genome Res [Internet]. 2006 Aug 1 [cited 2019 Jul 9];16(8):949–61. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16809666
- Database of Genomic Variants [Internet]. [cited 2019 Nov 3]. Available from: http://dgv.tcag.ca/dgv/app/home
- Firth H V., Richards SM, Bevan AP, Clayton S, Corpas M, Rajan D, et al. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. The American Journal of Human Genetics [Internet]. 2009 Apr [cited 2019 Nov 3];84(4):524–33. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0002929709001074
- Kearney HM, Thorland EC, Brown KK, Quintero-Rivera F, South ST. American College of Medical Genetics standards and guidelines for interpretation and reporting of postnatal constitutional copy number variants. Genetics in Medicine. 2011 Jul 15;13(7):680–5.
- Shalev-Shwartz Shai, Ben-David Shai. Understanding machine learning : from theory to algorithms. 397 p.
- Isakov O, Dotan I, Ben-Shachar S. Machine Learning–Based Gene Prioritization Identifies Novel Candidate Risk Genes for Inflammatory Bowel Disease. Inflamm Bowel Dis. 2017 Sep 1;23(9):1516–23.
- Ainscough BJ, Barnell EK, Ronning P, Campbell KM, Wagner AH, Fehniger TA, et al. A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nat Genet. 2018;50(12):1735–43.
- Barber D. Bayesian reasoning and machine learning. Cambridge University Press; 2012. 697 p.
- Alpaydin E. Machine Learning - Ethem Alpaydin. 2016 [cited 2025 Jan 10];112–8. Available from: https://mitpress.mit.edu/9780262529518/machine-learning/
- Beam AL, Drazen JM, Kohane IS, Leong TY, Manrai AK, Rubin EJ. Artificial Intelligence in Medicine. New England Journal of Medicine [Internet]. 2023 Mar 30 [cited 2024 Aug 9];388(13):1220–1. Available from: https://www.nejm.org/doi/full/10.1056/NEJMe2206291
- Shotton J, Sharp T, Kohli P, Nowozin S, Winn J, Criminisi A. Decision Jungles: Compact and Rich Models for Classification [Internet]. 2013 [cited 2019 Nov 27]. Available from: https://www.microsoft.com/en- us/research/publication/decision-jungles-compact-and-rich-models-for-classification/
- Mayoraz E, Alpaydin E. Support vector machines for multi-class classification. In Springer, Berlin, Heidelberg ; 1999 [cited 2019 Nov 25]. p. 833–42. Available from: http://link.springer.com/10.1007/BFb0100551
- Ainscough BJ, Barnell EK, Ronning P, Campbell KM, Wagner AH, Fehniger TA, et al. A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nat Genet [Internet]. 2018 [cited 2019 Nov 10];50(12):1735–43. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30397337
- Miller DT, Adam MP, Aradhya S, Biesecker LG, Brothman AR, Carter NP, et al. Consensus Statement: Chromosomal Microarray Is a First-Tier Clinical Diagnostic Test for Individuals with Developmental Disabilities or Congenital Anomalies. The American Journal of Human Genetics. 2010 May 14;86(5):749–64.
- Kaminsky EB, Kaul V, Paschall J, Church DM, Bunke B, Kunig D, et al. An evidence-based approach to establish the functional and clinical significance of copy number variants in intellectual and developmental disabilities. Genet Med. 2011 Sep;13(9):777–84.
- nstd101 - ClinGen - dbVar Study - NCBI [Internet]. [cited 2019 Nov 18]. Available from: https://www.ncbi.nlm.nih.gov/dbvar/studies/nstd101/
- nstd102 - Clinical Structural Variants - dbVar Study - NCBI [Internet]. [cited 2019 Nov 18]. Available from: https://www.ncbi.nlm.nih.gov/dbvar/studies/nstd102/
- Microsoft Azure Machine Learning Studio (classic) [Internet]. [cited 2019 Nov 18]. Available from: https://studio.azureml.net/
- Spielmann M, Klopocki E. CNVs of noncoding cis-regulatory elements in human disease. Curr Opin Genet Dev. 2013 Jun 1;23(3):249–56.
- Brandt T, Sack LM, Arjona D, Tan D, Mei H, Cui H, et al. Adapting ACMG/AMP sequence variant classification guidelines for single-gene copy number variants. Genetics in Medicine. 2019 Sep 19;1–9.
- Koolen DA, Pfundt R, de Leeuw N, Hehir-Kwa JY, Nillesen WM, Neefs I, et al. Genomic microarrays in mental retardation: A practical workflow for diagnostic applications. Hum Mutat. 2009 Mar 1;30(3):283–92.
- Barber D. Bayesian reasoning and machine learning. Cambridge University Press; 2012. 697 p.
- Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet. 2019 Jan 26;51(1):12–8.
- Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet [Internet]. 2019 Jan 26 [cited 2019 Nov 24];51(1):12–8. Available from: http://www.nature.com/articles/s41588-018-0295-5
- Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature [Internet]. 2021 Aug 26 [cited 2024 Jul 26];596(7873):583–9. Available from: https://pubmed.ncbi.nlm.nih.gov/34265844/
- de Sainte Agathe JM, Filser M, Isidor B, Besnard T, Gueguen P, Perrin A, et al. SpliceAI-visual: a free online tool to improve SpliceAI splicing variant interpretation. Hum Genomics [Internet]. 2023 Dec 1 [cited 2024 Jul 26];17(1). Available from: https://pubmed.ncbi.nlm.nih.gov/36765386/
- Hill T, Unckless RL. A Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data. G3 (Bethesda) [Internet]. 2019 Nov 5 [cited 2019 Nov 17];9(11):3575–82. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31455677
- Lappalainen I, Lopez J, Skipper L, Hefferon T, Spalding JD, Garner J, et al. DbVar and DGVa: public archives for genomic structural variation. Nucleic Acids Res. 2013 Jan;41(Database issue):D936-41.
- Sneddon TP, Church DM. Online resources for genomic structural variation. Methods Mol Biol [Internet]. 2012 [cited 2019 Nov 24];838:273–89. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22228017
- NCBI Variation Summary [Internet]. [cited 2019 Nov 24]. Available from: https://www.ncbi.nlm.nih.gov/dbvar/content/org_summary/
- Mallick S, Li H, Lipson M, Mathieson I, Gymrek M, Racimo F, et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature [Internet]. 2016 Oct 13 [cited 2019 Nov 24];538(7624):201–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27654912
- Rauch A, Schellmoser S, Kraus C, D�rr HG, Trautmann U, Altherr MR, et al. First known microdeletion within the Wolf-Hirschhorn syndrome critical region refines genotype-phenotype correlation. Am J Med Genet. 2001 Apr 1;99(4):338–42.
- Slavotinek AM. Novel microdeletion syndromes detected by chromosome microarrays. Hum Genet. 2008 Aug 30;124(1):1–17.
- Peterson TA, Doughty E, Kann MG. Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants. J Mol Biol. 2013 Nov 1;425(21):4047–63.
- Kearney HM, Thorland EC, Brown KK, Quintero-Rivera F, South ST. American College of Medical Genetics standards and guidelines for interpretation and reporting of postnatal constitutional copy number variants. Genetics in Medicine [Internet]. 2011 Jul 15 [cited 2019 Nov 3];13(7):680–5. Available from: http://www.nature.com/doifinder/10.1097/GIM.0b013e3182217a3a
- Brandt T, Sack LM, Arjona D, Tan D, Mei H, Cui H, et al. Adapting ACMG/AMP sequence variant classification guidelines for single-gene copy number variants. Genetics in Medicine [Internet]. 2019 Sep 19 [cited 2019 Nov 3];1–9. Available from: http://www.nature.com/articles/s41436-019-0655-2
- Hanke RE, Gibbons AT, Casar Berazaluce AM, Ponsky TA. Digital Transformation of Academic Medicine: Breaking Barriers, Borders, and Boredom. J Pediatr Surg [Internet]. 2019 Nov 9 [cited 2019 Nov 27]; Available from: https://www.sciencedirect.com/science/article/pii/S0022346819307729?via%3Dihub
- Al-Mufti F, Kim M, Dodson V, Sursal T, Bowers C, Cole C, et al. Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success. Curr Neurol Neurosci Rep [Internet]. 2019 Nov 13 [cited 2019 Nov 27];19(11):89. Available from: http://link.springer.com/10.1007/s11910-019-0998-8
- Kilic A. Artificial Intelligence and Machine Learning in Cardiovascular Healthcare. Ann Thorac Surg [Internet]. 2019 Nov 7 [cited 2019 Nov 27]; Available from: https://www.sciencedirect.com/science/article/pii/S0003497519316121?via%3Dihub
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Tıbbi Genetik (Kanser Genetiği hariç) |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Yayımlanma Tarihi | 12 Mart 2025 |
| Gönderilme Tarihi | 17 Aralık 2024 |
| Kabul Tarihi | 4 Şubat 2025 |
| Yayımlandığı Sayı | Yıl 2025Cilt: 64 Sayı: 1 |
