Gökmen Altay
Developing and applying methods to analyse the data produced particularly by the Neal Lab. In particular, inferring gene networks from gene expression data. Integrating various complex biological data to identify disease causing target genes and elucidate their working mechanism for potential drug and biomarker development primarily in the context of prostate cancer.
Vita
since 10/2010
Postdoctoral Research Associate at Neal Lab and Markowetz Lab at Department of Oncology, University of Cambridge and Cambridge Research Institute, UK. Advisors: David Neal and Florian Markowetz
12/2008 - 10/2010
Postdoctoral Research Associate at Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, Queen's University Belfast, UK. (Advisor: Frank Emmert-Streib)
9/2007 - 9/2008
Postdoctoral Scholar, Stanford University, Department of Electrical Engineering, CA, USA. (Advisor: Arogyaswami Paulraj)
4/2004 - 9/2007
9/2009 - 12/2008
Lecturer at Department of Electrical & Electronics Engineering, Bahcesehir University, Turkey.
Degrees
PhD
Department of Electrical & Electronics Engineering, Istanbul University, Turkey, 2006
MSc
Telecommunications and Computer Network Engineering, London South Bank University, UK, 2001
BSc
Department of Electrical & Electronics Engineering, Karadeniz Technical University, Turkey, 1999.
Publications in Bioinformatics and Computational Biology
G. ALTAY, M. Asim, F. Markowetz, D.E. Neal, 'Differential C3NET reveals disease networks of direct physical interactions', BMC Bioinformatics, 12(296), 2011.
G. ALTAY, F. Emmert-Streib, ''Structural influence of gene networks on their inference: analysis of C3NET'', Biology Direct, 6(31), 2011.
G. ALTAY, F. Emmert-Streib, 'Inferring the conservative causal core of gene regulatory networks', BMC Systems Biology, 4(132), 2010.
G. ALTAY, F. Emmert-Streib, 'Revealing differences in gene network inference algorithms on the network-level by ensemble methods', Bioinformatics, 26(14), 1738-1744, 2010.
F. Emmert-Streib, G. ALTAY, 'Local network-based measures to assess the inferability of different regulatory networks', IET Systems Biology, 4(4), 277-288, 2010.
Publications in Machine Learning and Telecommunications
G. ALTAY, "Broadcast Multicast Capacity of Network Coding for Random Wireless Networks", IET Communications, 1495-1503 pp., 2010.
C.O. Sakar, G. Demir, O. Kursun, H. Ozdemir, G. ALTAY, S. Yalcin, "Feature selection for the prediction of tropospheric ozone concentration using a wrapper method", Intelligent Automation and Soft Computing, 403-413 pp., 2011
H. Cam, V. Ozduran, G. ALTAY, O. N. Ucan, "Cooperative communications with multilevel/AES-SD4-CPFSK in wireless sensor networks", Annals of Telecommunications, 2010
A. Sezgin, G. ALTAY and A. Paulraj, "Generalized partial feedback based Orthogonal Space-Time Block Coding", IEEE Trans. on Wireless Communication, 2771-2775 pp., 2009
G. ALTAY, "Performance of Systematic Distance-4 Binary Linear Block Codes with Continuous Phase Frequency Shift Keying over MIMO Systems", Wireless Personal Communications, 403-413 pp., 2008
Ozdemir, H., Demir, G., ALTAY, G., Albayrak, S. and Bayat, C., "Prediction of tropospheric ozone concentration in Istanbul by employing artificial neural network", Environmental Engineering Science, 1249-1254 pp., 2008
Demir, G., ALTAY, G., Sakar, O., Albayrak, S., Yalcin, S., "Prediction and evaluation of tropospheric ozone concentration in Istanbul using artificial neural network modeling according to time parameter", Journal of Scientific & Industrial Research, 674-679 pp., 2008
G. ALTAY, O. N. Ucan, H. F. Ugurdag, "Geometric Augmented Product Codes", IEE Proc. Communications, 591-596 pp., 2006
G. ALTAY, O.N. Ucan, "Heuristic Construction of High-Rate Linear Block Codes", International Journal of Electronics and Communications (AEU) , 663-666 pp., 2006
Additionally, presented many conference papers of which most of the contents are available in the above journal publications.
Software packages
c3net
This is an R software package that allows inferring large-scale gene networks from microarray gene expression datasets, which employes the recently introduced algorithm, C3NET, in BMC Sys. Bio. 2010, 4:132. It is freely available for academic and non-commercial usage and can be downloaded from either CRAN or R-Forge
Affiliated with 