1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
10 years of news and resources for members of the IEEE Signal Processing Society
In the last decade, bioinformatics data has been accumulated at an unprecedented rate, thanks to the advancement in sequencing technologies. Such rapid development poses both challenges and promising research topics. In this dissertation, the authors propose a series of associative pattern recognition algorithms in biological regulation studies. In particular, the authors emphasize efficiently recognizing associative patterns between genes, transcription factors, histone modifications and functional labels using heterogeneous data sources (numeric, sequences, time series data and textual labels).
In protein-DNA associative pattern recognition, the authors introduce an efficient algorithm for affinity test by searching for over-represented DNA sequences using a hash function and modulo addition calculation. This substantially improves the efficiency of next generation sequencing data analysis. In gene regulatory network inference, the authors propose a framework for refining weak networks based on transcription factor binding sites, thus improved the precision of predicted edges by up to 52%. In histone modification code analysis, the authors propose an approach to genome-wide combinatorial pattern recognition for "histone code to function" associative pattern recognition, and achieved improvement by up to 38:1%. The authors also propose a novel shape based modification pattern analysis approach, using this to successfully predict sub-classes of genes in flowering-time category. The authors also propose a "combination to combination" associative pattern recognition, and achieved better performance compared against multi-label classification and bidirectional associative memory methods. Their proposed approaches recognize associative patterns from different types of data efficiently, and provides a useful toolbox for biological regulation analysis. This dissertation presents a road-map to associative patterns recognition at genome wide level.
|Series to Highlight Women in Signal Processing: Sheila S. Hemami||1 November 2019|
|Inactive Chapters||1 November 2019|
|Enhancements added to OU Analytics - Geographic Map||1 November 2019|
|Redesigned OU Monthly Statistics Now Available||1 November 2019|
|OU Analytics - Latest Enhancement||1 November 2019|
|OU Analytics - A Valuable Resource for Volunteers||1 November 2019|
|Call for Nominations: Fellow Evaluation Committee - Extended to November 22||22 November 2019|
© Copyright 2019 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.