No abstract

No abstract

The immense availability of protein interaction data, provided with an abstract network approach is valuable for the improved interpretation of biological processes and protein functions globally. The connectivity of a protein and its structure are related to its functional properties. Highly connected proteins are often functionally cardinal and the knockout of such proteins leads to lethality. In this paper, we propose a new approach based on graph information centrality measures to identify the synthetic lethal pairs in biological systems. To illustrate the efficacy of our approach, we have applied it to a human cancer protein interaction network. It is found that the lethal pairs obtained were analogous to the experimental and computational inferences, implying that our approach can serve as a surrogate for predicting the synthetic lethality.

No abstract

With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

No abstract

No abstract

No abstract