Finding differentially expressed genes for pattern generation

Download

index.pdf

Date

2005-02-15

Author

Abul, O
Alhajj, R
Polat, Faruk
Barker, K

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

198
views

0
downloads

Motivation: It is important to consider finding differentially expressed genes in a dataset of microarray experiments for pattern generation. Results: We developed two methods which are mainly based on the q-values approach; the first is a direct extension of the q-values approach, while the second uses two approaches: q-values and maximum-likelihood. We present two algorithms for the second method, one for error minimization and the other for confidence bounding. Also, we show how the method called Patterns from Gene Expression (PaGE) (Grant et al., 2000) can benefit from q-values. Finally, we conducted some experiments to demonstrate the effectiveness of the proposed methods; experimental results on a selected dataset (BRCA1 vs BRCA2 tumor types) are provided

Subject Keywords

Statistics and Probability, Computational Theory and Mathematics, Biochemistry, Molecular Biology, Computational Mathematics, Computer Science Applications

URI

https://hdl.handle.net/11511/48041

Journal

BIOINFORMATICS

DOI

https://doi.org/10.1093/bioinformatics/bti189

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Integrated search and alignment of protein structures Sacan, Ahmet; Toroslu, İsmail Hakkı; Ferhatosmanoglu, Hakan (Oxford University Press (OUP), 2008-12-15) Motivation: Identification and comparison of similar three-dimensional (3D) protein structures has become an even greater challenge in the face of the rapidly growing structure databases. Here, we introduce Vorometric, a new method that provides efficient search and alignment of a query protein against a database of protein structures. Voronoi contacts of the protein residues are enriched with the secondary structure information and a metric substitution matrix is developed to allow efficient indexing. The ...
LFM-Pro: a tool for detecting significant local structural sites in proteins Sacan, Ahmet; Ozturk, Ozgur; Ferhatosmanoglu, Hakan; Wang, Yusu (Oxford University Press (OUP), 2007-03-15) Motivation: The rapidly growing protein structure repositories have opened up new opportunities for discovery and analysis of functional and evolutionary relationships among proteins. Detecting conserved structural sites that are unique to a protein family is of great value in identification of functionally important atoms and residues. Currently available methods are computationally expensive and fail to detect biologically significant local features.
Integrating multi-attribute similarity networks for robust representation of the protein space Camoglu, Orhan; Can, Tolga; Singh, Ambuj K. (Oxford University Press (OUP), 2006-07-01) Motivation: A global view of the protein space is essential for functional and evolutionary analysis of proteins. In order to achieve this, a similarity network can be built using pairwise relationships among proteins. However, existing similarity networks employ a single similarity measure and therefore their utility depends highly on the quality of the selected measure. A more robust representation of the protein space can be realized if multiple sources of information are used.
GLANET: genomic loci annotation and enrichment tool Otlu, Burcak; Firtina, Can; Keles, Sunduz; Tastan, Oznur (Oxford University Press (OUP), 2017-09-15) Motivation: Genomic studies identify genomic loci representing genetic variations, transcription factor (TF) occupancy, or histone modification through next generation sequencing (NGS) technologies. Interpreting these loci requires evaluating them with known genomic and epigenomic annotations.
Implicit motif distribution based hybrid computational kernel for sequence classification Atalay, Mehmet Volkan (Oxford University Press (OUP), 2005-04-15) Motivation: We designed a general computational kernel for classification problems that require specific motif extraction and search from sequences. Instead of searching for explicit motifs, our approach finds the distribution of implicit motifs and uses as a feature for classification. Implicit motif distribution approach may be used as modus operandi for bioinformatics problems that require specific motif extraction and search, which is otherwise computationally prohibitive.

Citation Formats

O. Abul, R. Alhajj, F. Polat, and K. Barker, “Finding differentially expressed genes for pattern generation,” BIOINFORMATICS, pp. 445–450, 2005, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48041.