Dataset Information

BS-KNN: An Effective Algorithm for Predicting Protein Subchloroplast Localization.

ABSTRACT: Chloroplasts are organelles found in cells of green plants and eukaryotic algae that conduct photosynthesis. Knowing a protein's subchloroplast location provides in-depth insights about the protein's function and the microenvironment where it interacts with other molecules. In this paper, we present BS-KNN, a bit-score weighted K-nearest neighbor method for predicting proteins' subchloroplast locations. The method makes predictions based on the bit-score weighted Euclidean distance calculated from the composition of selected pseudo-amino acids. Our method achieved 76.4% overall accuracy in assigning proteins to 4 subchloroplast locations in cross-validation. When tested on an independent set that was not seen by the method during the training and feature selection, the method achieved a consistent overall accuracy of 76.0%. The method was also applied to predict subchloroplast locations of proteins in the chloroplast proteome and validated against proteins in Arabidopsis thaliana. The software and datasets of the proposed method are available at https://edisk.fandm.edu/jing.hu/bsknn/bsknn.html.

SUBMITTER: Hu J

PROVIDER: S-EPMC3256996 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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BS-KNN: An Effective Algorithm for Predicting Protein Subchloroplast Localization.

Hu Jing J Yan Xianghe X

Evolutionary bioinformatics online 20120105

Chloroplasts are organelles found in cells of green plants and eukaryotic algae that conduct photosynthesis. Knowing a protein's subchloroplast location provides in-depth insights about the protein's function and the microenvironment where it interacts with other molecules. In this paper, we present BS-KNN, a bit-score weighted K-nearest neighbor method for predicting proteins' subchloroplast locations. The method makes predictions based on the bit-score weighted Euclidean distance calculated fr ...[more]

PMID: 22267906

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Project description:BackgroundDiabetes mellitus characterized by hyperglycemia as a result of insufficient production of or reduced sensitivity to insulin poses a growing threat to the health of people. It is a heterogeneous disorder with multiple etiologies consisting of type 1 diabetes, type 2 diabetes, gestational diabetes and so on. Diabetes-associated protein/gene prediction is a key step to understand the cellular mechanisms related to diabetes mellitus. Compared with experimental methods, computational predictions of candidate proteins/genes are cheaper and more effortless. Protein-protein interaction (PPI) data produced by the high-throughput technology have been used to prioritize candidate disease genes/proteins. However, the false interactions in the PPI data seriously hurt computational methods performance. In order to address that particular question, new methods are developed to identify candidate disease genes/proteins via integrating biological data from other sources.ResultsIn this study, a new framework called PDMG is proposed to predict candidate disease genes/proteins. First, the weighted networks are building in terms of the combination of the subcellular localization information and PPI data. To form the weighted networks, the importance of each compartment is evaluated based on the number of interacted proteins in this compartment. This is because the very different roles played by different compartments in cell activities. Besides, some compartments are more important than others. Based on the evaluated compartments, the interactions between proteins are scored and the weighted PPI networks are constructed. Second, the known disease genes are extracted from OMIM database as the seed genes to expand disease-specific networks based on the weighted networks. Third, the weighted values between a protein and its neighbors in the disease-related networks are added together and the sum is as the score of the protein. Last but not least, the proteins are ranked based on descending order of their scores. The candidate proteins in the top are considered to be associated with the diseases and are potential disease-related proteins. Various types of data, such as type 2 diabetes-associated genes, subcellular localizations and protein interactions, are used to test PDMG method.ConclusionsThe results show that the proteins/genes functionally exerting a direct influence over diabetes are consistently placed at the head of the queue. PDMG expands and ranks 445 candidate proteins from the seed set including original 27 type 2 diabetes proteins. Out of the top 27 proteins, 14 proteins are the real type 2 diabetes proteins. The literature extracted from the PubMed database has proved that, out of 13 novel proteins, 8 proteins are associated with diabetes.

Project description:BACKGROUND: Protein function determination is a key challenge in the post-genomic era. Experimental determination of protein functions is accurate, but time-consuming and resource-intensive. A cost-effective alternative is to use the known information about sequence, structure, and functional properties of genes and proteins to predict functions using statistical methods. In this paper, we describe the Multi-Source k-Nearest Neighbor (MS-kNN) algorithm for function prediction, which finds k-nearest neighbors of a query protein based on different types of similarity measures and predicts its function by weighted averaging of its neighbors' functions. Specifically, we used 3 data sources to calculate the similarity scores: sequence similarity, protein-protein interactions, and gene expressions. RESULTS: We report the results in the context of 2011 Critical Assessment of Function Annotation (CAFA). Prior to CAFA submission deadline, we evaluated our algorithm on 1,302 human test proteins that were represented in all 3 data sources. Using only the sequence similarity information, MS-kNN had term-based Area Under the Curve (AUC) accuracy of Gene Ontology (GO) molecular function predictions of 0.728 when 7,412 human training proteins were used, and 0.819 when 35,622 training proteins from multiple eukaryotic and prokaryotic organisms were used. By aggregating predictions from all three sources, the AUC was further improved to 0.848. Similar result was observed on prediction of GO biological processes. Testing on 595 proteins that were annotated after the CAFA submission deadline showed that overall MS-kNN accuracy was higher than that of baseline algorithms Gotcha and BLAST, which were based solely on sequence similarity information. Since only 10 of the 595 proteins were represented by all 3 data sources, and 66 by two data sources, the difference between 3-source and one-source MS-kNN was rather small. CONCLUSIONS: Based on our results, we have several useful insights: (1) the k-nearest neighbor algorithm is an efficient and effective model for protein function prediction; (2) it is beneficial to transfer functions across a wide range of organisms; (3) it is helpful to integrate multiple sources of protein information.

Dataset Information

BS-KNN: An Effective Algorithm for Predicting Protein Subchloroplast Localization.

Publications

BS-KNN: An Effective Algorithm for Predicting Protein Subchloroplast Localization.

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