Abstract: Microarray gene expression data play a vital in biological processes, gene regulation and disease mechanism. Biclustering in gene expression data is a subset of the genes indicating consistent patterns under the subset of the conditions. Finding a biclustering is an optimization problem. In recent years, swarm intelligence techniques are popular due to the fact that many real-world problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to find an optimization technique whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. In this paper, the algorithmic concepts of the Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFL) and Cuckoo Search (CS) algorithms have been analyzed for the four benchmark gene expression dataset. The experiment results show that CS outperforms PSO and SFL for 3 datasets and SFL give better performance in one dataset. Also this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.
Abstract: Ontologies play an important role in semantic web
applications and are often developed by different groups and
continues to evolve over time. The knowledge in ontologies changes
very rapidly that make the applications outdated if they continue to
use old versions or unstable if they jump to new versions. Temporal
frames using frame versioning and slot versioning are used to take
care of dynamic nature of the ontologies. The paper proposes new
tags and restructured OWL format enabling the applications to work
with the old or new version of ontologies. Gene Ontology, a very
dynamic ontology, has been used as a case study to explain the OWL
Ontology with Temporal Tags.
Abstract: The most important property of the Gene Ontology is
the terms. These control vocabularies are defined to provide
consistent descriptions of gene products that are shareable and
computationally accessible by humans, software agent, or other
machine-readable meta-data. Each term is associated with
information such as definition, synonyms, database references, amino
acid sequences, and relationships to other terms. This information has
made the Gene Ontology broadly applied in microarray and
proteomic analysis. However, the process of searching the terms is
still carried out using traditional approach which is based on keyword
matching. The weaknesses of this approach are: ignoring semantic
relationships between terms, and highly depending on a specialist to
find similar terms. Therefore, this study combines semantic similarity
measure and genetic algorithm to perform a better retrieval process
for searching semantically similar terms. The semantic similarity
measure is used to compute similitude strength between two terms.
Then, the genetic algorithm is employed to perform batch retrievals
and to handle the situation of the large search space of the Gene
Ontology graph. The computational results are presented to show the
effectiveness of the proposed algorithm.
Abstract: Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents experimenters using the algorithm for hierarchical classification called Multi-label Hierarchical Classification using a Competitive Neural Network (MHC-CNN). It was tested in ten datasets the Gene Ontology (GO) Cellular Component Domain. The results are compared with the Clus-HMC and Clus-HSC using the hF-Measure.
Abstract: Ontologies play an important role in semantic web applications and are often developed by different groups and continues to evolve over time. The knowledge in ontologies changes very rapidly that make the applications outdated if they continue to use old versions or unstable if they jump to new versions. Temporal frames using frame versioning and slot versioning are used to take care of dynamic nature of the ontologies. The paper proposes new tags and restructured OWL format enabling the applications to work with the old or new version of ontologies. Gene Ontology, a very dynamic ontology, has been used as a case study to explain the OWL Ontology with Temporal Tags.
Abstract: Annotation of a protein sequence is pivotal for the understanding of its function. Accuracy of manual annotation provided by curators is still questionable by having lesser evidence strength and yet a hard task and time consuming. A number of computational methods including tools have been developed to tackle this challenging task. However, they require high-cost hardware, are difficult to be setup by the bioscientists, or depend on time intensive and blind sequence similarity search like Basic Local Alignment Search Tool. This paper introduces a new method of assigning highly correlated Gene Ontology terms of annotated protein sequences to partially annotated or newly discovered protein sequences. This method is fully based on Gene Ontology data and annotations. Two problems had been identified to achieve this method. The first problem relates to splitting the single monolithic Gene Ontology RDF/XML file into a set of smaller files that can be easy to assess and process. Thus, these files can be enriched with protein sequences and Inferred from Electronic Annotation evidence associations. The second problem involves searching for a set of semantically similar Gene Ontology terms to a given query. The details of macro and micro problems involved and their solutions including objective of this study are described. This paper also describes the protein sequence annotation and the Gene Ontology. The methodology of this study and Gene Ontology based protein sequence annotation tool namely extended UTMGO is presented. Furthermore, its basic version which is a Gene Ontology browser that is based on semantic similarity search is also introduced.
Abstract: Nowadays, Gene Ontology has been used widely by many researchers for biological data mining and information retrieval, integration of biological databases, finding genes, and incorporating knowledge in the Gene Ontology for gene clustering. However, the increase in size of the Gene Ontology has caused problems in maintaining and processing them. One way to obtain their accessibility is by clustering them into fragmented groups. Clustering the Gene Ontology is a difficult combinatorial problem and can be modeled as a graph partitioning problem. Additionally, deciding the number k of clusters to use is not easily perceived and is a hard algorithmic problem. Therefore, an approach for solving the automatic clustering of the Gene Ontology is proposed by incorporating cohesion-and-coupling metric into a hybrid algorithm consisting of a genetic algorithm and a split-and-merge algorithm. Experimental results and an example of modularized Gene Ontology in RDF/XML format are given to illustrate the effectiveness of the algorithm.
Abstract: Prediction of bacterial virulent protein sequences can
give assistance to identification and characterization of novel
virulence-associated factors and discover drug/vaccine targets against
proteins indispensable to pathogenicity. Gene Ontology (GO)
annotation which describes functions of genes and gene products as a
controlled vocabulary of terms has been shown effectively for a
variety of tasks such as gene expression study, GO annotation
prediction, protein subcellular localization, etc. In this study, we
propose a sequence-based method Virulent-GO by mining informative
GO terms as features for predicting bacterial virulent proteins.
Each protein in the datasets used by the existing method
VirulentPred is annotated by using BLAST to obtain its homologies
with known accession numbers for retrieving GO terms. After
investigating various popular classifiers using the same five-fold
cross-validation scheme, Virulent-GO using the single kind of GO
term features with an accuracy of 82.5% is slightly better than
VirulentPred with 81.8% using five kinds of sequence-based features.
For the evaluation of independent test, Virulent-GO also yields better
results (82.0%) than VirulentPred (80.7%). When evaluating single
kind of feature with SVM, the GO term feature performs much well,
compared with each of the five kinds of features.