Predictive Diabetes Mellitus From DNA Sequences Using Deep Learning

Authors

Lena abed ALraheim Hamza, College for Women, University of Babylon, Babylon, Iraq.Follow
Hussein Attya Lafta, Hussein Attya Lafta works at College of Information Technology, University of Babylon, Babylon, Iraq
Sura Zaki Al-Rashid, College of Information Technology, University of Babylon, Babylon, Iraq.

Abstract

Diabetes is a chronic metabolic disorder characterized by elevated blood sugar levels. It manifests in different forms, with type 1 and type 2 being the most prevalent. Type 1 diabetes results from the autoimmune destruction of insulin-producing cells, whereas type 2 diabetes primarily stems from insulin resistance. Despite advancements in treatment, accurate detection and prediction of diabetes remain challenging. Early diagnosis is crucial for effective management and prevention of complications. Another obstacle lies in interpreting vast amounts of health data, including DNA sequencing, which poses difficulties for healthcare professionals in identifying relevant patterns and associations. Artificial intelligence (AI) holds promise in healthcare by developing and training deep learning algorithms to analyze health data and DNA sequences. The research paper focuses on applying both Convolutional Neural Networks (CNNs) algorithm, in addition to Long Short-Term Memory (LSTM) algorithm for predicting types of diabetes based on DNA sequencing. The study aims to leverage the power of CNN and LSTM, known for their proficiency in analyzing image and sequence data, to accurately classify diabetes types. The experimental results of the proposed CNN-LSTM model showcased remarkable performance, achieving a recorded accuracy of 100% on a labeled dataset that included DNA sequencing and corresponding diabetes types. The model's evaluation encompassed several metrics, including accuracy, recall, precision, and the F1 score.

Recommended Citation

Hamza, Lena abed ALraheim; Lafta, Hussein Attya; and Al-Rashid, Sura Zaki (2023) "Predictive Diabetes Mellitus From DNA Sequences Using Deep Learning," Al-Bahir Journal for Engineering and Pure Sciences: Vol. 3: Iss. 2, Article 3.
Available at: https://doi.org/10.55810/2313-0083.1042

References

[1] Forouhi NG, Wareham NJ. Epidemiology of diabetes. Medicine Jan 2019;47(1):22-7. https://doi.org/10.1016/j.mpmed.2018.10.004.

[2] Morris AP. Progress in defining the genetic contribution to type 2 diabetes susceptibility. Curr Opin Genet Dev Jun. 2018;50:41-51. https://doi.org/10.1016/j.gde.2018.02.003.

[3] Seo J-W, Lee K-J. Post-translational Modifications and Their Biological Functions: Proteomic Analysis and Systematic Approaches. BMB Rep Jan 2004;37(1):35-44. https://doi.org/10.5483/bmbrep.2004.37.1.035.

[4] Basl_e E, Joubert N, Pucheault M. Protein Chemical Modification on Endogenous Amino Acids. Chem Biol Mar. 2010; 17(3):213-27. https://doi.org/10.1016/j.chembiol.2010.02.008.

[5] Kinsner W. Towards cognitive analysis of DNA. In: Proc. 9^th IEEE int. Conf. On cognitive informatics, Beijing, China; 2010. https://doi.org/10.1109/coginf.2010.5599728.

[6] Nguyen NG, et al. DNA Sequence Classification by Convolutional Neural Network. J Biomed Sci Eng 2016;9(5): 280-6. https://doi.org/10.4236/jbise.2016.95021.

[7] El-Attar NE, Moustafa BM, Awad WA. Deep learning model to detect diabetes mellitus based on DNA sequence. Intell Autom Soft Comput 2022;31(1):325-38. https://doi.org/10.32604/iasc.2022.019970.

[8] Shrestha A, Mahmood A. Review of Deep Learning Algorithms and Architectures. IEEE Access 2019;7:53040-65. https://doi.org/10.1109/access.2019.2912200.

[9] Al Rashid SZ. Collaborative Computing-Based K-Nearest Neighbour Algorithm and Mutual Information to Classify Gene Expressions for Type 2 Diabetes. Int J e-Collab 2022; 18(2):12. https://doi.org/10.4018/IJeC.304044.

[10] Rajeswari SVKR, VijayakumarPonnusamy. Prediction of Diabetes Mellitus Using Machine Learning Algorithm. In: Annals of the Romanian society for cell biology; 2021. p. 5655-62. Retrieved from, https://www.annalsofrscb.ro/index.php/journal/article/view/6653.

[11] Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H. Predicting Diabetes Mellitus With Machine Learning Techniques. Front Genet Nov 2018;9. https://doi.org/10.3389/fgene.2018.00515.

[12] Das B. A deep learning model for identification of diabetes type 2 based on nucleotide signals. Neural Comput Appl Mar 2022;34(15):12587-99. https://doi.org/10.1007/s00521-022-07121-8.

[13] Shanan NAA, Lafta HA, Alrashid SZ. Using alignment-free methods as preprocessing stage to classification whole genomes. Research Paper. Babylon, Iraq: Computer Department, Science College for Women, University of Babylon; 2021. https://doi.org/10.22075/IJNAA.2021.5281.

[14] Aziz FA, Al-Rashid SZ. Prediction of DNA Binding Sites Bound to Specific Transcription Factors by the SVM Algorithm. Int J Sci 2022;63(11):37. https://doi.org/10.24996/ijs.2022.63.11.37.

[15] Shalev-Shwartz Shai, Ben-David Shai. Understanding machine learning : from theory to algorithms. Delhi: Cambridge University Press; 2015.

[16] Mathew A, Amudha P, Sivakumari S. Deep Learning Techniques: An Overview. In: Advances in intelligent systems and computing; May 2020. p. 599-608. https://doi.org/10.1007/978-981-15-3383-9_54.

[17] Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 2021;8(1):53. https://doi.org/10.1186/s40537-021-00421-4.

[18] Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge, Massachusetts: The Mit Press; 2016.

[19] Khan S, Rahmani H, Shah A, Bennamoun M. Convolutional neural network. Jan 2018. p. 43-68. https://doi.org/10.1007/978-3-031-01821-3_4.

[20] Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, et al. A primer on deep learning in genomics. Nat Genet 2019; 51(1):12-8. https://doi.org/10.1038/s41588-018-0295-5.

[21] Nonis F, Barbiero P, Cirrincione G, Olivetti EC, Marcolin F, Vezzetti E. Understanding Abstraction in Deep CNN: An Application on Facial Emotion Recognition. 2021. p. 281-90. https://doi.org/10.1007/978-981-15-5093-5_26.

[22] El Attar NE, Hassan MK, Alghamdi OA, Awad WA. Deep learning model for classification and bioactivity prediction of essential oil producing plants from Egypt. Sci Rep 2020;10(1): 1-10. https://doi.org/10.1038/s41598-020-78449-1.

[23] Mohammadpoor M, Sheikhi M. A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images. Peer J Comp Sci 2021;3:1-12. https://doi.org/10.1007/s00330-021-07715-1.

[24] Van Houdt G, Mosquera C, N_apoles G. A review on the long short-term memory model. Artif Intell Rev 2020;53:5929-55. https://doi.org/10.1007/s10462-020-09819-9.

[25] The INS gene and its putative association with human ageing. https://genomics.senescence.info/genes/entry.php?hgnc=INS (accessed Jul. 1, 2023).