Electrical engineering Ph.D. student wins student best paper award from GENSIPS 2013

NoorAmina Noor, a doctoral student in the Department of Electrical and Computer Engineering at Texas A&M University, won the student best paper award from the IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2013). 

Noor won the student best  paper award (Third Place Award) for her paper, "ROBNCA: Robust Network Component Analysis for Recovering Transcription Factor Activities."

In her paper, Noor and co-authors Dr. Aitzaz Ahmad, Dr. Erchin Serpedin, Dr. Mohamed Nounou and Dr. Hazem Nounou developed a novel algorithm, ROBust Network Component Analysis (ROBNCA), for reconstruction of transcription factor (TF) activity profile and TF-gene interaction matrix, key features in computational biology used to characterize the transcriptional regulation processes in cells.

It is widely believed that a thorough understanding of the complex transcriptional regulation and TF-gene interactions will help in predicting biological processes and in designing strategies to cure diseases and avoid diseased conditions. ROBNCA is a well-suited algorithm for the analysis of gene regulatory networks and presents important features such as the ability to counteract the presence of outliers in the microarray data, computational efficiency, superior consistency and greater accuracy than the existing state-of-the-art algorithms. 

Noor  received the B.E. and M.S. degrees in electrical engineering from National University of Sciences and Technology in Islamabad, Pakistan. She has completed her Ph.D. dissertation at Texas A&M under Serpedin's supervision and will graduate in December. Her research interests include statistical signal processing, machine learning and bioinformatics.

GENSIPS' 2013 is a forum for signal processing researchers, bioinformaticians, computational biologists and biomedical engineers to exchange ideas and discuss the challenges confronting computational bioinformatics and systems biology communities due to the high modality of disparate high-throughput data, high variability of data acquisition, high dimensionality of biomedical data, and high complexity of genomics and proteomics data analysis.