A clustering method for the problem of protein subcellular localization

Download

index.pdf

Date

2006

Author

Bezek, Perit

Metadata

Show full item record

Item Usage Stats

245
views

50
downloads

In this study, the focus is on predicting the subcellular localization of a protein, since subcellular localization is helpful in understanding a protein’s functions. Function of a protein may be estimated from its sequence. Motifs or conserved subsequences are strong indicators of function. In a given sample set of protein sequences known to perform the same function, a certain subsequence or group of subsequences should be common; that is, occurrence (frequency) of common subsequences should be high. Our idea is to find the common subsequences through clustering and use these common groups (implicit motifs) to classify proteins. To calculate the distance between two subsequences, traditional string edit distance is modified so that only replacement is allowed and the cost of replacement is related to an amino acid substitution matrix. Based on the modified string edit distance, spectral clustering embeds the subsequences into some transformed space for which the clustering problem is expected to become easier to solve. For a given protein sequence, distribution of its subsequences over the clusters is the feature vector which is subsequently fed to a classifier. The most important aspect if this approach is the use of spectral clustering based on modified string edit distance.

Subject Keywords

Computer Software.

URI

http://etd.lib.metu.edu.tr/upload/12607981/index.pdf
https://hdl.handle.net/11511/16112

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A classification system for the problem of protein subcellular localization Alay, Gökçen; Atalay, Mehmet Volkan; Department of Computer Engineering (2007) The focus of this study is on predicting the subcellular localization of a protein. Subcellular localization information is important for protein function annotation which is a fundamental problem in computational biology. For this problem, a classification system is built that has two main parts: a predictor that is based on a feature mapping technique to extract biologically meaningful information from protein sequences and a client/server architecture for searching and predicting subcellular localization...
Subsequence feature maps for protein function annotation Saraç, Ömer Sinan; Atalay, Mehmet Volkan; Department of Computer Engineering (2008) With the advances in sequencing technologies, the number of protein sequences with unknown function increases rapidly. Hence, computational methods for functional annotation of these protein sequences become of the upmost importance. In this thesis, we first defined a feature space mapping of protein primary sequences to fixed dimensional numerical vectors. This mapping, which is called the Subsequence Profile Map (SPMap), takes into account the models of the subsequences of protein sequences. The resulting...
A systematic study of probabilistic aggregation strategies in swarm robotic systems Soysal, Onur; Şahin, Erol; Department of Computer Engineering (2005) In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performa...
Prediction of enzyme classes in a hierarchical approach by using spmap Yaman, Ayşe Gül; Atalay, Mehmet Volkan; Department of Computer Engineering (2009) Enzymes are proteins that play an important role in biochemical reactions as catalysts. They are classified based on the reaction they catalyzed, in a hierarchical scheme by International Enzyme Commission (EC). This hierarchical scheme is expressed as a four-level tree structure and a unique number is assigned to each enzyme class. There are six major classes at the top level according to the reaction they carried out and sub-classes at the lower levels are further specific reactions of these classes. The ...
Improving search result clustering by integrating semantic information from Wikipedia Çallı, Çağatay; Üçoluk, Göktürk; Şehitoğlu, Onur Tolga; Department of Computer Engineering (2010) Suffix Tree Clustering (STC) is a search result clustering (SRC) algorithm focused on generating overlapping clusters with meaningful labels in linear time. It showed the feasibility of SRC but in time, subsequent studies introduced description-first algorithms that generate better labels and achieve higher precision. Still, STC remained as the fastest SRC algorithm and there appeared studies concerned with different problems of STC. In this thesis, semantic relations between cluster labels and documents ar...

Citation Formats

P. Bezek, “A clustering method for the problem of protein subcellular localization,” M.S. - Master of Science, Middle East Technical University, 2006.