Ananda Mondal

Project Title: Deep learning to discover the disparities in lung cancer between African American and European American males

The highest number of deaths among American males occurs from lung cancer, which is more deaths than brain, colorectal, and prostate cancers combined. The African American Males (AAMs) have significantly higher lung cancer death rates than European American Males (EAMs). Cigarette smoking is considered the strongest risk factor for lung cancer, but smoking alone cannot explain the disparity of lung cancer development between AAMs and EAMs. The traditional cohort-based genome-wide association studies and comparative genomic analyses failed to identify the AAM-specific genetic locations that cause lung cancer. These studies are similar to the current standard of medical practice, which largely relies on cohort-based epidemiological studies in which the genetic variability of individuals is largely ignored, resulting in population-based conclusions. As a result, similar cancer types respond differently to the same treatment since each tumor has its own set of unique mutations. Thus, a personalized approach is required that can identify the genetic variations that happen during cancer development in an individual. Recent advancement in deep learning application shows that it can mirror the transcription state perturbations needed to cause a tumor in a healthy individual. The central hypothesis is that, using deep learning, we can estimate the transcription state alterations between tumor and normal tissues of each individual from the AAM and EAM cohorts. This information will help decipher the race-specific risk factors of lung cancer development in AAMs and EAMs. The impact is significant since it will identify the heterogeneity of lung cancer at a personalized level and provide precision medicine’s rational design. This improved precision medicine will help reduce health disparities in lung cancer between AAMs and EAMs and maximize the therapeutic benefit in both AAM and EAM communities.

Research Interests

Machine Learning, Deep Learning, Bioinformatics, Cancer Genomics, Epigenomics, and Transcriptomics

Assistant Professor, Knight Foundation School of Computing and Infirmation Sciences

Dr. Ananda Mohan Mondal is an Assistant Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU). His research area is at the intersection of Machine Learning, Deep Learning, and Bioinformatics. Dr. Mondal has a strong background in computational biology with specific training and expertise in analyzing genomic, epigenomic, and transcriptomic data for cancers employing computer science concepts and statistical machine learning techniques, including deep learning. The mission of his research group, Machine Learning and Data Analytics Group (MLDAG), at FIU is to train Ph.D., M.S., and Undergraduate students to develop AI-based computational tools for discovering disease biomarkers that can be used as screening tools.
Mondal received a Bachelor of Science in Chemical Engineering from Bangladesh University of Engineering and Technology (BUET) with First Class and First Position with Honors. He obtained his Master’s in Computer Engineering (2003) and Ph.D. in Computer Science and Engineering (2011) from the University of South Carolina.

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