The Bioinformatic Analysis of Cancerous Cells


  • Ayush Yavagal Gifted Gabber
  • Coach Jo



artificial intelligence, cancer, cancerous cells, bioinformatics, analysis of cancer, bioinformatic analysis, bioinformatic analysis of cancer, image recognition


Cancer is a ruthless disease that has no definite cure and it is very consequential to treat it. Chemotherapy and other cancer treatments have lasting negative effects on patients, like fatigue, diarrhea, nausea, and many other harmful side effects. To decrease the time period of vigorous cancer treatments like chemotherapy, cancer should be detected very early. Pathologists and clinicians have used many methods of cancer diagnosis over the years, but to do this, large amounts of data about a patient and their history are needed. This is known as the bioinformatic analysis of cancer cells. Bioinformatics is the act of using biological information to aid in the diagnosis of a disease, which in this case is cancer. When pathologists have diagnosed cancer in the past, the main component of diagnosis was the processing of this biological data, and it still is a major part of diagnosis today. With the rise of new technologies, Artificial Intelligence has been brought into the medical limelight and has been used extensively in the medical field to process the abundance of this data in recent years. With the use of Artificial Intelligence, the analysis speed of data used to diagnose cancer is higher than if pathologists alone analyzed the data. This paper focuses on the processing of bioinformatic data in pathologists’ cancer diagnosis workflow.


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Author Biography

Coach Jo

The 8-week session where the student will conduct research and write a scientific journal guided by Dr. Rajagopal Appavu, Assistant Professor, Vaccine Developer, Senior Data Scientist/Analyst, Toxicologist, and Chemist. After the draft has been approved by Professor, students will be guided to submit their scientific journal.

References or Bibliography

Cui, M., & Zhang, D. Y. (2021). Artificial intelligence and computational pathology. Laboratory Investigation, 101(4), 412–422.

Singh, S. (2022, February 12). Computer-Aided Diagnosis of Gliomas Using Machine LearningClassification Algorithms Shreya Singh Shreya Singh. Gifted Gabber.

Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., Mak, R. H., Tamimi, R. M., Tempany, C. M., Swanton, C., Hoffmann, U., Schwartz, L. H., Gillies, R. J., Huang, R. Y., & Aerts, H. J. W. L. (2019). Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: A Cancer Journal for Clinicians.

‌Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019). Digital pathology and artificial intelligence. The Lancet Oncology, 20(5), e253–e261.

‌Chen, Z., Lin, L., Wu, C., Li, C., Xu, R., & Sun, Y. (2021). Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Communications, 41(11), 1100–1115.

Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., Hipp, J. D., Peng, L., & Stumpe, M. C. (2017). Detecting Cancer Metastases on Gigapixel Pathology Images.

‌Schiffman, J. D., Fisher, P. G., & Gibbs, P. (2015). Early Detection of Cancer: Past, Present, and Future. American Society of Clinical Oncology Educational Book, 35(35), 57–65.

‌Raab, S. S., & Grzybicki, D. M. (2010). Quality in Cancer Diagnosis. CA: A Cancer Journal for Clinicians, 60(3), 139–165.

‌Iyengar, J. N. (2021, January 1). Whole slide imaging: The futurescape of histopathology Iyengar JN - Indian J Pathol Microbiol.;year=2021;volume=64;issue=1;spage=8;epage=13;aulast=Iyengar



How to Cite

Yavagal, A., & Kethar, J. (2023). The Bioinformatic Analysis of Cancerous Cells. Journal of Student Research, 12(2).



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