Innovative Algorithms and Evaluation Methods for Biological Motif Finding
نویسندگان
چکیده
Biological motifs are defined as overly recurring sub-patterns in biological systems. Sequence motifs and network motifs are the examples of biological motifs. Due to the wide range of applications, many algorithms and computational tools have been developed for efficient search for biological motifs. Therefore, there are more computationally derived motifs than experimentally validated motifs, and how to validate the biological significance of the ‘candidate motifs’ becomes an important question. Some of sequence motifs are verified by their structural similarities or their functional roles in DNA or protein sequences, and stored in databases. However, biological role of network motifs is still invalidated and currently no databases exist for this purpose. In this thesis, we focus not only on the computational efficiency but also on the biological meanings of the motifs. We provide an efficient way to incorporate biological information with clustering analysis methods: For example, a sparse nonnegative matrix factorization (SNMF) method is used with Chou-Fasman parameters for the protein motif finding. Biological network motifs are searched by various clustering algorithms with Gene ontology (GO) information. Experimental results show that the algorithms perform better than existing algorithms by producing a larger number of high-quality of biological motifs. In addition, we apply biological network motifs for the discovery of essential proteins. Essential proteins are defined as a minimum set of proteins which are vital for development to a fertile adult and in a cellular life in an organism. We design a new centrality algorithm with biological network motifs, named MCGO, and score proteins in a protein-protein interaction (PPI) network to find essential proteins. MCGO is also combined with other centrality measures to predict essential proteins using machine learning techniques. We have three contributions to the study of biological motifs through this thesis; 1) Clustering analysis is efficiently used in this work and biological information is easily integrated with the analysis; 2) We focus more on the biological meanings of motifs by adding biological knowledge in the algorithms and by suggesting biologically related evaluation methods. 3) Biological network motifs are successfully applied to a practical application of prediction of essential proteins. INDEX WORDS: Biological network motif, Clustering analysis, Gene ontology, Essential protein, Machine learning INNOVATIVE ALGORITHMS AND EVALUATION METHODS FOR BIOLOGICAL MOTIF FINDING
منابع مشابه
A Novel Intrusion Detection Systems based on Genetic Algorithms-suggested Features by the Means of Different Permutations of Labels’ Orders
Intrusion detection systems (IDS) by exploiting Machine learning techniques are able to diagnose attack traffics behaviors. Because of relatively large numbers of features in IDS standard benchmark dataset, like KDD CUP 99 and NSL_KDD, features selection methods play an important role. Optimization algorithms like Genetic algorithms (GA) are capable of finding near-optimum combination of the fe...
متن کاملIdentification of Transcription Factor Binding Sites in Promoter Regions by Modularity Analysis of the Motif Co-occurrence Graph
Many algorithms have been proposed to date for the problem of finding biologically significant motifs in promoter regions. They can be classified into two large families: combinatorial methods and probabilistic methods. Probabilistic methods have been used more extensively, since their output is easier to interpret. Combinatorial methods have the potential to identify hard to detect motifs, but...
متن کاملHIGEDA: a hierarchical gene-set genetics based algorithm for finding subtle motifs in biological sequences
MOTIVATION Identification of motifs in biological sequences is a challenging problem because such motifs are often short, degenerate, and may contain gaps. Most algorithms that have been developed for motif-finding use the expectation-maximization (EM) algorithm iteratively. Although EM algorithms can converge quickly, they depend strongly on initialization parameters and can converge to local ...
متن کاملAssessment of the Performance of Clustering Algorithms in the Extraction of Similar Trajectories
In recent years, the tremendous and increasing growth of spatial trajectory data and the necessity of processing and extraction of useful information and meaningful patterns have led to the fact that many researchers have been attracted to the field of spatio-temporal trajectory clustering. The process and analysis of these trajectories have resulted in the extraction of useful information whic...
متن کاملDiscovering larger network motifs: Network clustering for Network Motif discovery
We want to discover larger network motifs, with more than 15 number of nodes. In order to propose an algorithm for finding larger network motifs in any biological network, we review some of models and algorithms to find network motifs. There are two types of methods, one is exact counting and the other is approximate sampling. Generalization of random graphs is another important issue to evalua...
متن کامل