Zuraini Ali Shah

Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction

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.

A Review of Feature Extraction Software for Microarray Gene Expression Data

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method.

Incorporating multiple genomic features with the utilization of interacting domain patterns to improve the prediction of protein-protein interactions

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.

SPlitSSI-SVM: An algorithm to reduce the misleading and increase the strength of domain signal

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.

A Review of Computational Approaches to Predict Gene Functions

Recently, novel high-throughput biotechnologies have provided rich data about different genomes. However, manual annotation of gene function is time consuming. It is also very expensive and infeasible for the growing amounts of data. At present there are numerous functions in certain species that remain unknown or only partially known. Hence, the use of computational approaches to predicting gene function is becoming widespread. Computational approaches are time saving and less costly. Prediction analysis provided can be used in hypotheses to drive the biological validation of gene function. Objective: This paper reviews computational approaches such as the support vector machine, clustering, hierarchical ensemble and network-based approaches. Methods: Comparisons between these approaches are also made in the discussion portion. Results: In addition, the advantages and disadvantages of these computational approaches are discussed. Conclusion: With the emergence of omics data, the focus should be continued on integrating newly added data for gene functions prediction field.

Granular support vector machine to identify unknown structural classes of protein

To date, classification of structural class using local protein structure rather than the whole structure has been gaining widespread attention. It is noted that the structural class lies in local composition or arrangement of secondary structure, while the threshold-based classification method has restricted rules in determining these structural classes. As a consequence, some of the structures are unknown. In order to determine these unknown structural classes, we propose a fusion algorithm, abbreviated as GSVM-SigLpsSCPred (Granular Support Vector Machine--with Significant Local protein structure for Structural Class Prediction), which consists of two major components, which are: optimal local protein structure to represent the feature vector and granular support vector machine to predict the unknown structural classes. The results highlight the performance of GSVM-SigLpsSCPred as an alternative computational method for low-identity sequences.