Decision Tree in Biology

Authors

  • Komal Shazadi

DOI:

https://doi.org/10.47672/ejb.642

Keywords:

Artificial intelligence, tree-based, machine learning, biology analysis

Abstract

Purpose: Human biology is an essential field in scientific research as it helps in understanding the human body for adequate care. Technology has improved the way scientists do their biological research. One of the critical technologies is artificial intelligence (AI), which is revolutionizing the world. Scientists have applied AI in biological studies, using several methods to gain different types of data. Machine learning is a branch of artificial intelligence that helps computers learn from data and create predictions without being explicitly programmed.

Methodology: One critical methodology in the machine is using the tree-based decision. It is extensively used in biological research, as it helps in classifying complex data into simple and easy to interpret graphs. This paper aims to give a beginner-friendly view of the tree-based model, analyzing its use and advantages over other methods.

Finding: Artificial intelligence has greatly improved the collection, analysis, and prediction of biological and medical information. Machine learning, a subgroup of artificial intelligence, is useful in creating prediction models, which help a wide range of fields, including computational and systems biology. Contribution and future recommendation also discussed in this study.

 

Downloads

Download data is not yet available.

Author Biography

Komal Shazadi

MS Biomedical Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan

References

Ahmad, M. A., Eckert, C., & Teredesai, A. (2018). Interpretable machine learning in healthcare. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. https://doi.org/10.1145/3233547.3233667

Bhardwaj, R., Nambiar, A. R., & Dutta, D. (2017). A study of machine learning in healthcare. 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC). https://doi.org/10.1109/compsac.2017.164

Darcy, A.M., Louie, A.K., & Roberts, L.W. (2016). Machine Learning and the Profession of Medicine. J Am Med Assoc, 315(6), 551. https://doi.org/10.1001/jama.2015.18421.

Donepudi, P. K. (2017). AI and Machine Learning in Banking: A Systematic Literature Review. Asian Journal of Applied Science and Engineering 6(3): 157-162.

Donepudi, P. K. (2018). AI and Machine Learning in Retail Pharmacy: Systematic Review of Related Literature. ABC Journal of Advanced Research 7(2): 109-112.

Fishman, N. (2012). Policy statement on antimicrobial stewardship by the society for healthcare epidemiology of America (SHEA), the Infectious Diseases Society of America (IDSA), and the pediatric infectious diseases society (PIDS). Infection Control & Hospital Epidemiology, 33(4), 322-327. https://doi.org/10.1086/665010

Haider AH, Chang DC, Efron DT, Haut ER, Crandall M, Cornwell EE. Race and Insurance Status as Risk Factors for Trauma Mortality. Arch Surgery, 143(10), 945. https://doi.org/10.1001/archsurg.143.10.945.

Huskins, W. C., Fowler, V. G., & Evans, S. (2017). undefined. Clinical Infectious Diseases, 66(7), 1140-1146. https://doi.org/10.1093/cid/cix907

Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010). Inferring regulatory networks from expression data using tree-based methods. PLoS ONE, 5(9), e12776. https://doi.org/10.1371/journal.pone.0012776

Khan, N., & Tappen, M. F. (2013). Discriminative dictionary learning with spatial priors. 2013 IEEE International Conference on Image Processing. https://doi.org/10.1109/icip.2013.6738035

Mathur, P. (2018). Overview of machine learning in healthcare. Machine Learning Applications Using Python, 1-11. https://doi.org/10.1007/978-1-4842-3787-8_1

Mou, L., Peng, H., Li, G., Xu, Y., Zhang, L., & Jin, Z. (2015). Discriminative neural sentence modeling by tree-based convolution. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d15-1279

Nosratabadi, S., Mosavi, A., Keivani, R., Faizollahzadeh ardabili, S., & Aram, F. (2019). State of the art survey of deep learning and machine learning models for smart cities and urban sustainability. https://doi.org/10.20944/preprints201908.0154.v1

Olson, R. S., & Moore, J. H. (2019). TPOT: A tree-based pipeline optimization tool for automating machine learning. Automated Machine Learning, 151-160. https://doi.org/10.1007/978-3-030-05318-5_8

Olson, R. S., Urbanowicz, R. J., Andrews, P. C., Lavender, N. A., Kidd, L. C., & Moore, J. H. (2016). Automating biomedical data science through tree-based pipeline optimization. Applications of Evolutionary Computation, 123-137. https://doi.org/10.1007/978-3-319-31204-0_9

Othman, M., Ratna, S., Tewari, A., Kang, A., & Vaisman, I. (2018). Machine learning classification of antimicrobial peptides using reduced alphabets. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. https://doi.org/10.1145/3233547.3233657

Pakdel, R., & Herbert, J. (2017). Adaptive cost efficient framework for cloud-based machine learning. 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC). https://doi.org/10.1109/compsac.2017.42

Permanasari, A. E., & Nurlayli, A. (2017). Decision tree to analyze the cardiotocogram data for fetal distress determination. 2017 International Conference on Sustainable Information Engineering and Technology (SIET). https://doi.org/10.1109/siet.2017.8304182

Proceedings of the 2018 ACM International Conference on bioinformatics, computational biology, and health informatics. (2018). https://doi.org/10.1145/3233547

Rojas, J. C., Carey, K. A., Edelson, D. P., Venable, L. R., Howell, M. D., & Churpek, M. M. (2018). Predicting intensive care unit readmission with machine learning using electronic health record data. Annals of the American Thoracic Society, 15(7), 846-853. https://doi.org/10.1513/annalsats.201710-787oc

Rudin, C., & Ustun, B. (2018). Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice. Interfaces, 48(5), 449-466. https://doi.org/10.1287/inte.2018.0957

Shen, D., Zhang, D., Young, A., & Parvin, B. (2015). Editorial: Machine learning and data mining in medical imaging. IEEE Journal of Biomedical and Health Informatics, 19(5), 1587-1588. https://doi.org/10.1109/jbhi.2015.2444011

Suresh, A., Udendhran, R., & Balamurgan, M. (2019). Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers. Soft Computing, 24(11), 7947-7953. https://doi.org/10.1007/s00500-019-04066-4

Tree-based learners. (2020). Machine Learning Refined, 443-470. https://doi.org/10.1017/9781108690935.019

Downloads

Published

2021-01-07

How to Cite

Shazadi, K. . (2021). Decision Tree in Biology . European Journal of Biology, 6(1), 1 – 15. https://doi.org/10.47672/ejb.642

Issue

Vol. 6 No. 1 (2021): 2021

Section

Articles

License

Copyright (c) 2021 Komal Shazadi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.