Pattern Recognition and Signal Processing Methods in Genomics

Progress in biological science will require adopting systems methods, as engineering has already found useful and necessary in dealing with complexity. We will develop computational methods to (a) learn about the characteristics of genomic signals using theories in signal processing, (b) reduce the dimensionality of the genomic data using machine learning techniques, and (c) produce an integrated, multi-layer circuit of the transcriptional response using methods such as Bayesian networks. Image from Vahedi, et al, IEEE Transactions on Biomed. Eng. 2008.

Relevant Publications

• Vahedi, G., Ivanov, I. V., Dougherty, E. R., “Inference of Boolean networks under constraint on bidirectional gene relationships”. IET Syst Biol, 3(3): 191-202, 2009.

• Vahedi, G., Faryabi, B., Chamberland, J. F., Datta, A., Dougherty, E. R., “Sampling-rate-dependent probabilistic Boolean networks”. J Theor Biol, 261(4): 540-7, 2009.

• Vahedi, G., Faryabi, B., Chamberland, J. F., Datta, A., Dougherty, E. R., “Optimal intervention strategies for cyclic therapeutic methods”, IEEE Trans Biomed Eng, 56(2): 281-91, 2009.

• Vahedi, G., Faryabi, B., Chamberland, J. F., Datta, A., Dougherty, E. R., “Intervention in gene regulatory networks via a stationary mean-first-passage-time control policy”, IEEE Trans Biomed Eng, 55(10): 2319-31, 2008.