Papers by ZURAINI BINTI ALI SHAH FC
A study of lightweight ontology for herb domain
2014 8th. Malaysian Software Engineering Conference (MySEC), 2014
The usage of herb has evolved from an alternative healing option to mainstream medical field, cul... more The usage of herb has evolved from an alternative healing option to mainstream medical field, culinary, cosmetic as well as gardening purpose. The dramatic increment of interest in the usage of herb these days are due to the critical scientific analysis and quality control of its therapeutic potential and safety. Consequently, the process of searching for herbal information becomes difficult due to the heterogeneity of herb data, as well as the overlapping content. The massive herb knowledge needs to be organized and managed so that it can be accessed easily by scientists, herbalists and general users. The herb species, usages and their relationship need to be well-captured and stored. Thus, ontology is seen as a solution to solve this problem. Nowadays, ontology is in widespread use as a means in representing domain knowledge. The reason for such extensive use is that it is not only consistent but also has upward compatibility and is modifiable. However, the ontology design released by researchers especially in herb domain is relatively small in number. A characteristic of lightweight ontology is further investigated in order to modelling the herb domain.
Information Sciences, 2010
Protein-protein interaction (PPI) networks play an outstanding role in the organization of life. ... more Protein-protein interaction (PPI) networks play an outstanding role in the organization of life. Parallel to the growth of experimental techniques on determining PPIs, the emergence of computational methods has greatly accelerated the time needed for the identification of PPIs on a wide genomic scale. Although experimental approaches have limitations that can be complemented by the computational methods, the results from computational methods still suffer from high false positive rates which contribute to the lack of solid PPI information. Our study introduces the PPI-Filter; a computational framework aimed at improving PPI prediction results. It is a post-prediction process which involves filtration, using information based on three different genomic features; (i) gene ontology annotation (GOA), (ii) homologous interactions and (iii) protein families (PFAM) domain interactions. In the study, we incorporated a protein function prediction method, based on interacting domain patterns, the protein function predictor or PFP (), for the purpose of aiding the GOA. The goal is to improve the robustness of predicted PPI pairs by removing the false positive pairs and sustaining as much true positive pairs as possible, thus achieving a high confidence level of PPI datasets. The PPI-Filter has been proven to be applicable based on the satisfactory results obtained using signal-to-noise ratio (SNR) and strength measurements that were applied on different computational PPI prediction methods
gmm.fsksm.utm.my
Component-Based Software Engineering (CBSE) has been touted as the latest revolution in software ... more Component-Based Software Engineering (CBSE) has been touted as the latest revolution in software engineering and bioinformatics field. Computer scientists and biologists are working together to apply this new design approach to the closely-intertwined problems of integrating distributed heterogeneous bioinformatics software into one house. However, interaction and communication of distributed heterogeneous bioinformatics software remains limited in practice because most of software components failed to interact with another component. Therefore, in order to solve interaction and communication problems, CBSE technologies allow computer scientists and biologists to build novel and integrated bioinformatics software via Interface Definition Language (IDL) and Object Request Broker (ORB). Using these technologies, the computer scientists and biologists are able to connect a number of different types of components (e.g. database components, net access components, sequence analysis components, and graphical display components) together to form a new application. Moreover, CBSE technologies are not only can reduce development times and cost but also offers computer scientists and biologists a way to quickly react on dynamic biotechnology requirements. In this paper, we discuss the idea of implementing CBSE technologies in the development of integrated bioinformatics software. Furthermore, this paper also present the current implementation of CBSE technologies in solving interaction and communication problems in various areas of bioinformatics applications (e.g. visualization and simulation, sequence analysis, and data mining) using IDL and ORB.
Computers in Biology and Medicine, 2009
Protein domains contain information about the prediction of protein structure, function, evolutio... more Protein domains contain information about the prediction of protein structure, function, evolution and design since the protein sequence may contain several domains with different or the same copies of the protein domain. In this study, we proposed an algorithm named SplitSSI-SVM that works with the following steps. First, the training and testing datasets are generated to test the SplitSSI-SVM. Second, the protein sequence is split into subsequence based on order and disorder regions. The protein sequence that is more than 600 residues is split into subsequences to investigate the effectiveness of the protein domain prediction based on subsequence. Third, multiple sequence alignment is performed to predict the secondary structure using bidirectional recurrent neural networks (BRNN) where BRNN considers the interaction between amino acids. The information of about protein secondary structure is used to increase the protein domain boundaries signal. Lastly, support vector machines (SVM) are used to classify the protein domain into single-domain, two-domain and multiple-domain. The SplitSSI-SVM is developed to reduce misleading signal, lower protein domain signal caused by primary structure of protein sequence and to provide accurate classification of the protein domain. The performance of SplitSSI-SVM is evaluated using sensitivity and specificity on single-domain, two-domain and multiple-domain. The evaluation shows that the SplitSSI-SVM achieved better results compared with other protein domain predictors such as DOMpro, GlobPlot, Dompred-DPS, Mateo, Biozon, Armadillo, KemaDom, SBASE, HMMPfam and HMMSMART especially in two-domain and multiple-domain.
Computers in Biology and Medicine, 2010
Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations s... more Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
CoIMIS : sistem maklumat pengurusan kepimpinan kolej
Combination of dimension reduction with modified robust estimation for gene-based cancer classification
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Papers by ZURAINI BINTI ALI SHAH FC