Social Investment. As a new charity finance tool: using both head and heart

Presenting associations between individual amino acid residues and EULAR response. (DOCX 23 kb

Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients

<div><p>Background</p><p>The dysregulation of microRNAs (miRNAs) alters expression level of pro-oncogenic or tumor suppressive mRNAs in breast cancer, and in the long run, causes multiple biological abnormalities. Identification of such interactions of miRNA-mRNA requires integrative analysis of miRNA-mRNA expression profile data. However, current approaches have limitations to consider the regulatory relationship between miRNAs and mRNAs and to implicate the relationship with phenotypic abnormality and cancer pathogenesis.</p><p>Methodology/Findings</p><p>We modeled causal relationships between genomic expression and clinical data using a Bayesian Network (BN), with the goal of discovering miRNA-mRNA interactions that are associated with cancer pathogenesis. The Multiple Beam Search (MBS) algorithm learned interactions from data and discovered that hsa-miR-21, hsa-miR-10b, hsa-miR-448, and hsa-miR-96 interact with oncogenes, such as, CCND2, ESR1, MET, NOTCH1, TGFBR2 and TGFB1 that promote tumor metastasis, invasion, and cell proliferation. We also calculated Bayesian network posterior probability (BNPP) for the models discovered by the MBS algorithm to validate true models with high likelihood.</p><p>Conclusion/Significance</p><p>The MBS algorithm successfully learned miRNA and mRNA expression profile data using a BN, and identified miRNA-mRNA interactions that probabilistically affect breast cancer pathogenesis. The MBS algorithm is a potentially useful tool for identifying interacting gene pairs implicated by the deregulation of expression.</p></div

Additional file 1 of Efficacy and safety comparison of neoadjuvant chemotherapy followed by surgery and upfront surgery for treating intrahepatic cholangiocarcinoma: a systematic review and meta-analysis

Additional file 1: Supplementary Table 1. The detailed search strategies of PubMed

List of miRNA-mRNA interactions that are associated with breast cancer phenotype, metastatic tumor indicator, and molecular subtype.

<p>List of miRNA-mRNA interactions that are associated with breast cancer phenotype, metastatic tumor indicator, and molecular subtype.</p

The result of categorization on KEGG pathway gene signature for the mRNAs interacting with miRNAs associated with breast cancer pathogenesis.

<p>The result of categorization on KEGG pathway gene signature for the mRNAs interacting with miRNAs associated with breast cancer pathogenesis.</p

Additional file 2: of Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences

Web-links to databases and software packages used in this paper. (DOCX 14 kb

The expression level of hsa-miR-96 and its target mRNAs that are associated with breast cancer.

<p>The expression levels of miRNAs and mRNAs were represented in 76 normal tissue samples and 747 tumor patients. t-test p-values are indicated above the box plots.</p

Summary of microRNA and mRNA expression datasets in breast cancer.

<p>(Patient that have both miRNA and mRNA expression data.).</p

The model for the effect of miRNA-i on the target through the hidden variable.

<p>(a). The model including the hidden variable <i>H</i>, (b) The interaction model learned from data.</p

For the breast cancer datasets, the average fraction of the total number of patterns checked before the bound finds the true pattern.

<p>For the breast cancer datasets, the average fraction of the total number of patterns checked before the bound finds the true pattern.</p