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Knowledge-based hybrid molecular network (KHMN)

ABSTRACT: Here, we proposed a general method of a knowledge-based hybrid molecular network (KHMN) that was coconstructed by untargeted metabolomics MS/MS spectra with heterogeneous MS/MS spectra of interesting specialized metabolites as initial seeds. Using antiherbivore metabolites in maize as an example, a KHMN was generated for the rapid screening and efficient annotation of as many specialized metabolites as possible. First, a priori specialized metabolites were collected as seed metabolites. The liquid chromatography (LC)-MS/MS database of the seed metabolites was established. Knowledge (characteristic fragmentation patterns and biotransformation reaction) was fully extracted from the MS/MS spectra of seed metabolites or priori knowledge. Then, a KHMN was coconstructed using heterogeneous and homologous MS/MS spectra of the seed metabolites and untargeted ultrahigh-performance LC-HRMS (UPLC-HRMS) metabolomics data. At the same time, captured knowledge was integrated into the molecular network. Finally, both known and unknown specialized metabolites could be effectively extracted and automatically propagated to annotate by KHMN. Since we used known targeted metabolites as initial cues to trigger molecular networks, highly efficient capture of the molecular families of targeted metabolites could be achieved. Simultaneously, the integration of captured knowledge into the network increased the efficiency and accuracy of annotation propagation and benefitted the annotation of new metabolites.

ORGANISM(S): Zea mays (NCBITaxon:4577)

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PROVIDER: S-BSST1188 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident.We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship.Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second-order neighbors in the PPI network could be used to identify likely disease associations.We analyzed the human protein interaction network and its relationship to disease and found that both the number of interactions with other proteins and the disease relationship of neighboring proteins helped to determine whether a protein had a relationship to disease. Our classifier predicted many proteins with no annotated disease association to be disease-related, which indicated that these proteins have network characteristics that are similar to disease-related proteins and may therefore have disease associations not previously identified. By performing a post-processing step after the prediction, we were able to identify evidence in literature supporting this possibility. This method could provide a useful filter for experimentalists searching for new candidate protein targets for drug repositioning and could also be extended to include other network and data types in order to refine these predictions.

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Knowledge-based hybrid molecular network (KHMN)

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