35 research outputs found

    Comparison of TSNBA and Degree based approach for top 300 ranking genes.

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    <p>Comparison of TSNBA and Degree based approach for top 300 ranking genes.</p

    Framework of TSNBA. PPI network and gene expression data are integrated in the interaction activity matrix to rank genes for their relevancy to the perturbation.

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    <p>The top K ranking genes are further filtered with co-expression network for better pathology enrichment. Context likelihood of relatedness (CLR) algorithm is used in the third step to infer gene regulatory networks and identify key transcription regulators. Node in gray represents for known pathology related genes, white represents for predicted ones, and black represents for predicted key regulators.</p

    A Three Step Network Based Approach (TSNBA) to Finding Disease Molecular Signature and Key Regulators: A Case Study of IL-1 and TNF-Alpha Stimulated Inflammation

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    <div><p>A disease molecular signature is a set of biomolecular features that are prognostic of clinical phenotypes and indicative of underlying pathology. It is of great importance to develop computational approaches for finding more relevant molecular signatures. Based upon the hypothesis that various components in a molecular signature are more likely to share similar patterns, we introduced a novel three step network based approach (TSNBA) to identify the molecular signature and key pathological regulators. Protein-protein interaction (PPI) network and ranking algorithm were integrated in the first step to find pathology related proteins with high accuracy. It was followed by the second step to further screen with co-expression patterns for better pathology enrichment. Context likelihood of relatedness (CLR) algorithm was used in the third step to infer gene regulatory networks and identify key transcription regulators. We applied this approach to study IL-1 (interleukin-1) and TNF-alpha (tumor necrosis factor-alpha) stimulated inflammation. TSNBA identified inflammatory signature with high accuracy and outperformed 5 competing methods namely fold change, degree, interconnectivity, neighborhood score and network propagation based approaches. The best molecular signature, with 80% (40/50) confirmed inflammatory genes, was used to predict inflammation related genes. As a result, 8 out of 10 predicted inflammation genes that were not included in the benchmark Entrez Gene database were validated by literature evidence. Furthermore, 23 of the 32 predicted inflammation regulators were validated by literature evidence. The rest 9 were also validated with TF (transcription factor) binding site analysis. In conclusion, we developed an efficient strategy for disease molecular signature finding and key pathological regulator identification.</p></div

    Enrichment analysis of inflammation signature by TSNBA.

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    <p>(A): Enrichment ratio of top K genes for different data sets. IL1_0.5h, IL1_1h, IL1_2.5h, and IL1_6h represent for the perturbation with 0.5, 1.0, 2.5, and 6.0 hours IL-1 stimulation, respectively. TNF1-4 represent for the perturbation with 5 hours TNF stimulation. (B) Comparison of ranking algorithm with TSNBA, rank_50 represents for top 50 ranking genes.</p

    Statistical significance test for methods comparison.

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    <p>Fold change based approach (red), network propagation (green), interconnectivity (purple), and neighborhood scoring (cyan) are compared with first step of TSNBA (A) and full TSNBA (B).</p

    Performance of network degree based approach.

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    <p>Enrichment ratios are calculated for top K genes. The size of genes, K, is set to be 50, 100, 150, 200, 250 and 300.</p

    Performance of fold change based approach.

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    <p>Enrichment ratios are calculated under 5 absolute fold change cutoffs, namely 1.2, 1.5, 2.0, 3.0 and 4.0. Different colors represent for different data sets.</p

    Impact of training sample size.

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    <p>Prediction MCC based on different number of training samples for 10 endpoints using <i>NCentroid</i>.</p
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