Interval based fuzzy systems for identification of important genes from microarray gene expression data: Application to carcinogenic development

MM-CCNB: Essential protein prediction using MAX-MIN strategies and compartment of common neighboring approach

BACKGROUND AND OBJECTIVE

Proteins are indispensable for the flow of the life of living organisms. Protein pairs in interaction exhibit more functional activities than individuals. These activities have been considered an essential measure in predicting their essentiality. Neighborhood approaches have been used frequently in the prediction of essentiality scores. All paired neighbors of the essential proteins are nominated for the suitable candidate seeds for prediction. Still now Jaccard's coefficient is limited to predicting functions, homologous groups, sequence analysis, etc. It really motivate us to predict essential proteins efficiently using different computational approaches.

METHODS

In our work, we proposed modified Jaccard's coefficient to predict essential proteins. We have proposed a novel methodology for predicting essential proteins using MAX-MIN strategies and modified Jaccard's coefficient approach.

RESULTS

The performance of our proposed methodology has been analyzed for Saccharomyces cerevisiae datasets with an accuracy of more than 80%. It has been observed that the proposed algorithm is outperforms with an accuracy of 0.78, 0.74, 0.79, and 0.862 for YDIP, YMIPS, YHQ, and YMBD datasets respectivly.

CONCLUSIONS

There are several computational approaches in the existing state-of-art model of essential protein prediction. It has been noted that our predicted methodology outperforms other existing models viz. different centralities, local interaction density combined with protein complexes, modified monkey algorithm and ortho_sim_loc methods.

A novel approach for the analysis of time-course gene expression data based on computing with words

In this paper, a novel approach is proposed for the analysis of time-course gene expression data based on the path-breaking work of Zadeh, Computing with Words. This method can automatically discover the patterns of temporal gene expression profile in terms of two distinguishing descriptions: linguistic description that is understandable and interpretable for human inference; and type-2 fuzzy description that is suitable for robust machine inference in the presence of uncertainty. In contrast to conventional static data mining methods which focus on the steady-state gene expression levels, the proposed scheme is a new time-series pattern mining technique for dynamical modeling of gene expression. To evaluate the performance of this paradigm, it is applied to a case study dataset from Gene Expression Omnibus (GEO) which includes the temporal transcriptional profile of human colon cancer cells. The goal is to investigate the pharmacodynamics of two anticancer drugs. The transient and steady-state analysis of the transcriptional response clearly demonstrates the ability of the proposed approach to reveal the pharmacodynamical effects of drug type and dosage on the expression of genes.

Relating Complexity and Error Rates of Ontology Concepts. More Complex NCIt Concepts Have More Errors

OBJECTIVES

Ontologies are knowledge structures that lend support to many health-information systems. A study is carried out to assess the quality of ontological concepts based on a measure of their complexity. The results show a relation between complexity of concepts and error rates of concepts.

METHODS

A measure of lateral complexity defined as the number of exhibited role types is used to distinguish between more complex and simpler concepts. Using a framework called an area taxonomy, a kind of abstraction network that summarizes the structural organization of an ontology, concepts are divided into two groups along these lines. Various concepts from each group are then subjected to a two-phase QA analysis to uncover and verify errors and inconsistencies in their modeling. A hierarchy of the National Cancer Institute thesaurus (NCIt) is used as our test-bed. A hypothesis pertaining to the expected error rates of the complex and simple concepts is tested.

RESULTS

Our study was done on the NCIt's Biological Process hierarchy. Various errors, including missing roles, incorrect role targets, and incorrectly assigned roles, were discovered and verified in the two phases of our QA analysis. The overall findings confirmed our hypothesis by showing a statistically significant difference between the amounts of errors exhibited by more laterally complex concepts vis-à-vis simpler concepts.

CONCLUSIONS

QA is an essential part of any ontology's maintenance regimen. In this paper, we reported on the results of a QA study targeting two groups of ontology concepts distinguished by their level of complexity, defined in terms of the number of exhibited role types. The study was carried out on a major component of an important ontology, the NCIt. The findings suggest that more complex concepts tend to have a higher error rate than simpler concepts. These findings can be utilized to guide ongoing efforts in ontology QA.

Alpha-plane based automatic general type-2 fuzzy clustering based on simulated annealing meta-heuristic algorithm for analyzing gene expression data

This paper considers microarray gene expression data clustering using a novel two stage meta-heuristic algorithm based on the concept of α-planes in general type-2 fuzzy sets. The main aim of this research is to present a powerful data clustering approach capable of dealing with highly uncertain environments. In this regard, first, a new objective function using α-planes for general type-2 fuzzy c-means clustering algorithm is represented. Then, based on the philosophy of the meta-heuristic optimization framework 'Simulated Annealing', a two stage optimization algorithm is proposed. The first stage of the proposed approach is devoted to the annealing process accompanied by its proposed perturbation mechanisms. After termination of the first stage, its output is inserted to the second stage where it is checked with other possible local optima through a heuristic algorithm. The output of this stage is then re-entered to the first stage until no better solution is obtained. The proposed approach has been evaluated using several synthesized datasets and three microarray gene expression datasets. Extensive experiments demonstrate the capabilities of the proposed approach compared with some of the state-of-the-art techniques in the literature.

Signal Peptidase Complex Subunit 1 and Hydroxyacyl-CoA Dehydrogenase Beta Subunit Are Suitable Reference Genes in Human Lungs

Lung cancer is a common cancer, and expression profiling can provide an accurate indication to advance the medical intervention. However, this requires the availability of stably expressed genes as reference. Recent studies had shown that genes that are stably expressed in a tissue may not be stably expressed in other tissues suggesting the need to identify stably expressed genes in each tissue for use as reference genes. DNA microarray analysis has been used to identify those reference genes with low fluctuation. Fourteen datasets with different lung conditions were employed in our study. Coefficient of variance, followed by NormFinder, was used to identify stably expressed genes. Our results showed that classical reference genes such as GAPDH and HPRT1 were highly variable; thus, they are unsuitable as reference genes. Signal peptidase complex subunit 1 (SPCS1) and hydroxyacyl-CoA dehydrogenase beta subunit (HADHB), which are involved in fundamental biochemical processes, demonstrated high expression stability suggesting their suitability in human lung cell profiling.