Dr Wisnu Ananta Kusuma: Network Analysis Able to Find Comorbid Diseases with Lung Cancer

Cancer is a disease that is sometimes difficult to detect, especially if it is comorbid with several other degenerative diseases. Several technologies are able to identify diseases that may be comorbid with cancer, especially lung cancer. This technology is a development in the field of bioinformatics, namely through network analysis.

Dr. Wisnu Ananta Kusuma, IPB University Lecturer from the Department of Computer Science, Faculty of Mathematics and Natural Sciences (FMIPA) explained the research with the research team of the Tropical Biopharmaca Research Center, Institute for Research and Community Service (LPPM) regarding this matter in the Weekly Seminar of the Indonesian Association for Patterns. Recognition (INAPR) “A network Analysis to Identify Lung Cancer Comorbid Diseases”, (05/08). This research has also been published as an International scientific journal Applied Network Science in 2022.

According to him, this study was conducted to take advantage of high-level scientific and computational breakthroughs that can spur data exploration, especially data related to cancer. Disease is a problem that can be solved as an interconnected network. This disease network can explore the nature of its comorbidity with another disease.

"This can be modeled and represented by the relationship between the diseases through this analysis," he explained. He explained that this network analysis can find proteins that have an important role in disease. Further research can also find precision medicine with herbs. "To achieve this, not only consider the function of protein compounds but other genomic information," he continued.

According to him, cancer is characterized by uncontrolled cell growth that can even cause death. Judging from the comorbidities, the most affecting the severity of lung cancer is hypertension. “Because of this comorbid disease, its clinical management and treatment will be more complex. So of course it will have an impact on increasing the cost (of treatment),” said Dr. Wisnu.

This network analysis, he said, is used to detect communities that can be used to identify comorbidities of a disease. The convenience of this analysis is that it does not require primary data to analyze the network, only secondary data published in scientific journals. That is done by acquiring disease list data through text mining scripts in PubMed.

The disease network, which was developed based on the similarity of disease ontologies, can be used for comorbidity detection with community detection algorithms. The relevant algorithms in classifying lung cancer comorbidities are Label propagation, Spinglass, Chinese Whispering, Louvain, and RB Plots.

The results of this analysis found that there are four types of comorbid diseases that are significant with lung cancer, namely vascular disease, immune system disease, bone disease and pancreatic disease. Proximity to lung cancer was analyzed by disease ontology.

“Actually, we have an opportunity where making an approach does not need to use real data or primary patient data, but secondary data that has been written in scientific journals. Even though the algorithm must be elaborated again," he concluded. (MW/Zul) IAAS/SYA

Published Date : 10-Aug-2022

Resource Person : Dr Wisnu Ananta Kusuma

Keyword : Lecturer of IPB University, bioinformatics, health, cancer, precision medicine