Presentation_2_Matrix Integrative Analysis (MIA) of Multiple Genomic Data for Modular Patterns.PDF

<p>The increasing availability of high-throughput biological data, especially multi-dimensional genomic data across the same samples, has created an urgent need for modular and integrative analysis tools that can reveal the relationships among different layers of cellular activities. To this end, we present a MATLAB package, Matrix Integration Analysis (MIA), implementing and extending four published methods, designed based on two classical techniques, non-negative matrix factorization (NMF), and partial least squares (PLS). This package can integrate diverse types of genomic data (e.g., copy number variation, DNA methylation, gene expression, microRNA expression profiles, and/or gene network data) to identify the underlying modular patterns by each method. Particularly, we demonstrate the differences between these two classes of methods, which give users some suggestions about how to select a suitable method in the MIA package. MIA is a flexible tool which could handle a wide range of biological problems and data types. Besides, we also provide an executable version for users without a MATLAB license.</p

Presentation_1_Matrix Integrative Analysis (MIA) of Multiple Genomic Data for Modular Patterns.PDF

<p>The increasing availability of high-throughput biological data, especially multi-dimensional genomic data across the same samples, has created an urgent need for modular and integrative analysis tools that can reveal the relationships among different layers of cellular activities. To this end, we present a MATLAB package, Matrix Integration Analysis (MIA), implementing and extending four published methods, designed based on two classical techniques, non-negative matrix factorization (NMF), and partial least squares (PLS). This package can integrate diverse types of genomic data (e.g., copy number variation, DNA methylation, gene expression, microRNA expression profiles, and/or gene network data) to identify the underlying modular patterns by each method. Particularly, we demonstrate the differences between these two classes of methods, which give users some suggestions about how to select a suitable method in the MIA package. MIA is a flexible tool which could handle a wide range of biological problems and data types. Besides, we also provide an executable version for users without a MATLAB license.</p

Link communities of three networks of heterogeneous cliques.

<p>(A) The ring network of heterogeneous cliques. Each community is a clique, and two adjacent communities are connected by one node. (B) The ring network of overlapping heterogeneous cliques. Each community is a clique, and two adjacent communities are connected by one node or one link. (C) The tree network of heterogeneous cliques. Each community is a clique, and two adjacent communities are overlapped by one node <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083739#pone.0083739-Ahn1" target="_blank">[11]</a>.</p

Link communities of three artifical networks.

<p>(A) The network consists of five overlapping communities. Nodes 1, 7, 12, 16 are overlapping nodes; (B) The network consists of two overlapping communities. Nodes 1 and 2 are overlapping nodes that belong to the two communities, and link (1, 2) belongs to the two communities as well; (C) The network consists of two overlapping cliques and the overlapped subgraph is a 3-clique.</p

Link communities of some real-world networks.

<p>(A) The Karate club network; (B) The word association network; (C) The co-appearance network.</p

The parameters used in the GA algorithm for solving the link community detection problem on networks in Figure 2.

<p>The parameters used in the GA algorithm for solving the link community detection problem on networks in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083739#pone-0083739-g002" target="_blank">Figure 2</a>.</p

Three different partition results of a tree network.

<p>(A) Correct partition. (B,C) Two counter-intuitive partitions. The red links and their adjacent nodes constitute a community, the blue links and their adjacent nodes form another community. The black node is overlapped.</p

The network in Ref. [11] can be correctly partitioned into three communities by our model, and the objective function value is 1.

<p>The network in Ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083739#pone.0083739-Ahn1" target="_blank">[11]</a> can be correctly partitioned into three communities by our model, and the objective function value is 1.</p

Sílabo de Derecho Laboral

La asignatura es un curso formativo para el estudiante de contabilidad necesariamente vinculado a la empresa o a personas naturales con negocios donde se desarrollan relaciones laborales y por lo tanto se generan derechos y obligaciones para los empleadores y trabajadores. El curso es de naturaleza teórica – práctica y tiene por objeto dotar al estudiante de los conocimientos que le permitan liquidar los beneficios sociales y demás derechos laborales a fin de cumplir de manera eficiente su actividad profesional

Synergistic Transcriptional and Post-Transcriptional Regulation of ESC Characteristics by Core Pluripotency Transcription Factors in Protein-Protein Interaction Networks

<div><p>The molecular mechanism that maintains the pluripotency of embryonic stem cells (ESCs) is not well understood but may be reflected in complex biological networks. However, there have been few studies on the effects of transcriptional and post-transcriptional regulation during the development of ESCs from the perspective of computational systems biology. In this study, we analyzed the topological properties of the “core” pluripotency transcription factors (TFs) OCT4, SOX2 and NANOG in protein-protein interaction networks (PPINs). Further, we identified synergistic interactions between these TFs and microRNAs (miRNAs) in PPINs during ESC development. Results show that there were significant differences in centrality characters between TF-targets and non-TF-targets in PPINs. We also found that there was consistent regulation of multiple “core” pluripotency TFs. Based on the analysis of shortest path length, we found that the module properties were not only within the targets regulated by common or multiple “core” pluripotency TFs but also between the groups of targets regulated by different TFs. Finally, we identified synergistic regulation of these TFs and miRNAs. In summary, the synergistic effects of “core” pluripotency TFs and miRNAs were analyzed using computational methods in both human and mouse PPINs.</p></div