International Journal of Computational Bioinformatics and In Silico Modeling
ABSTRACT: Discovery of genes that are responsible for various diseases, becomes an important task. Since the genes are related with many diseases, the gene-disease association should be discovered. To obtain this gene-disease association from available biomedical literature, the relation type between the gene and disease is extracted from the biomedical literature. So, this becomes more and more important to deal with the extraction problem from the biomedical texts in an automatic way. Then the gene-disease association is visualized by network construction and association score matrix is constructed to calculate the gene-disease association score. The gene-disease relation type is identified and then the association score is calculated by integrating disease similarity network and protein-protein interaction network. The candidate genes for the particular disease and the novel genes for various diseases can also be found by calculating the association score and visualizing the dataset network.
KeyWords: Association Extraction, Biomedical Literature, Protein-protein Interaction.
How to cite: Kanimozhi U et al. Association Extraction from Biomedical Text using Network Analysis. Int J Comput Bioinfo In Silico Model. 6(1) 2017: 894-905
ABSTRACT: Biotic and Abiotic stresses such as drought, temperature extremes and salinity are the major constraints towards the living world more specifically the plant kingdom whose developments as well as crop yield is negatively hampered. Biotechnological approaches, including all the ‘omics’ have immense potential to help in circumventing biotic and abiotic stress-related issues. Successful application of omics on biotic and abiotic constraints requires advanced knowledge of stress response at molecular level like gene expression to protein or metabolite and its phenotypic effects. Such advances will ensure the availability of the sequenced genomes of model crop plants, along with the possibility of next generation sequencing, thereby increasing the potential to greatly facilitate the ‘omics’ approaches. Advances in these omics techniques will help in illuminating the genetic structure of plants and thereby allow the full deployment of genetic resources for crop improvement. For instance, a number of bioinformatics and systems biology tools for sequence assembly and annotation, transformation systems, and genomic DNA libraries have been developed following the release of the genome sequences of important model crop plants. In this report, our major emphasis was to acquaint the participants on how to increase stress tolerance in crop plants by using advanced computational technologies and skills in bioinformatics as we progress towards the systemic use of high-throughput sequencing in agricultural research. This will enable to develop the capacity for resolving the complexity of stress biology for enhancing the crop productivity.
KeyWords: Biotic, Abiotic, Omics data, Gene expression, Assembly, Annotation, Stress Biology.
How to cite: Anil Kumar et al. Addressing the complexity of Stress Biology through Hi-throughput Omics Data Analysis using Bioinformatics Tools: A National Bioinformatics Workshop Report. Int J Comput Bioinfo In Silico Model. 6(1) 2017: 906-910