Sentence examples for multiple biological data from inspiring English sources

The predictive strength of this strategy is based on the combined constraints arising from multiple biological data sources, including time course gene expression data, combined molecular interaction network data, and GO category information.

It was developed to enable scientists to perform advanced querying of multiple biological data sources through a single web interface.

We note that integrating multiple biological data sources in addition to PPI network [ 29] can improve the identification of protein complexes.

These results were the primary motivation for the work presented in this paper, as they clearly show that the RWR method is not suitable for dealing with multiple biological data.

The prior research shows that it is essential for the study to be based on a comprehensive consideration of the multiple biological data to grasp an in-depth understanding of the complex mechanisms of the human diseases and the identification of disease markers.

We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules.

Recently, several studies have observed a significant improvement in protein function prediction when multiple heterogenous biological data sources are integrated.

To facilitate further investigation of these hES lincRNAs, we integrated gene models and annotations with multiple related biological data into an integrative web portal.

Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle.

We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction.

The second major difficulty is the lack of efficient methods to integrate multiple levels of biological data to enhance model accuracy.