Vidyasagar, Mathukumalli

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/5613

Professor Mathukumalli Vidyasagar holds the Cecil & Ida Green Chair in Systems Biology Science and is also a member of The Royal Society. His research interests are in the broad area of system and control theory, and its applications. Presently, he is working on:

  • Compressed sensing, including sparse solutions to large under-determined problems, and the intersection between compressed sensing and control theory.
  • Applying ideas from machine learning to problems in computational biology with emphasis on cancer.

Browse

Browsing Vidyasagar, Mathukumalli by Author "Lea, Jayanthi S."

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Sparse Feature Selection for Classification and Prediction of Metastasis in Endometrial Cancer
    (2017-03-27) Ahsen, Mehmet Eren; Boren, Todd P.; Singh, Nitin K.; Misganaw, Burook; Mutch, David G.; Moore, Kathleen N.; Backes, Floor J.; McCourt, Carolyn K.; Lea, Jayanthi S.; Miller, David S.; White, Michael A.; Vidyasagar, Mathukumalli; 27150194 (Vidyasagar, M); Vidyasagar, Mathukumalli
    Background: Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4-22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort. Results: A feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%). Conclusion: Results indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients.

Works in Treasures @ UT Dallas are made available exclusively for educational purposes such as research or instruction. Literary rights, including copyright for published works held by the creator(s) or their heirs, or other third parties may apply. All rights are reserved unless otherwise indicated by the copyright owner(s).