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  1. Article ; Online: Machine learning and network medicine approaches for drug repositioning for COVID-19.

    Santos, Suzana de Siqueira / Torres, Mateo / Galeano, Diego / Sánchez, María Del Mar / Cernuzzi, Luca / Paccanaro, Alberto

    Patterns (New York, N.Y.)

    2021  Volume 3, Issue 1, Page(s) 100396

    Abstract: We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach ... ...

    Abstract We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorization algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 repositioning explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.
    Language English
    Publishing date 2021-11-09
    Publishing country United States
    Document type Journal Article
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2021.100396
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: BioNetStat: A Tool for Biological Networks Differential Analysis.

    Jardim, Vinícius Carvalho / Santos, Suzana de Siqueira / Fujita, Andre / Buckeridge, Marcos Silveira

    Frontiers in genetics

    2019  Volume 10, Page(s) 594

    Abstract: The study of interactions among biological components can be carried out by using methods grounded on network theory. Most of these methods focus on the comparison of two biological networks (e.g., control vs. disease). However, biological systems often ... ...

    Abstract The study of interactions among biological components can be carried out by using methods grounded on network theory. Most of these methods focus on the comparison of two biological networks (e.g., control vs. disease). However, biological systems often present more than two biological states (e.g., tumor grades). To compare two or more networks simultaneously, we developed BioNetStat, a Bioconductor package with a user-friendly graphical interface. BioNetStat compares correlation networks based on the probability distribution of a feature of the graph (e.g., centrality measures). The analysis of the structural alterations on the network reveals significant modifications in the system. For example, the analysis of centrality measures provides information about how the relevance of the nodes changes among the biological states. We evaluated the performance of BioNetStat in both, toy models and two case studies. The latter related to gene expression of tumor cells and plant metabolism. Results based on simulated scenarios suggest that the statistical power of BioNetStat is less sensitive to the increase of the number of networks than Gene Set Coexpression Analysis (GSCA). Also, besides being able to identify nodes with modified centralities, BioNetStat identified altered networks associated with signaling pathways that were not identified by other methods.
    Language English
    Publishing date 2019-06-21
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2019.00594
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: CoGA: An R Package to Identify Differentially Co-Expressed Gene Sets by Analyzing the Graph Spectra.

    Santos, Suzana de Siqueira / Galatro, Thais Fernanda de Almeida / Watanabe, Rodrigo Akira / Oba-Shinjo, Sueli Mieko / Nagahashi Marie, Suely Kazue / Fujita, André

    PloS one

    2015  Volume 10, Issue 8, Page(s) e0135831

    Abstract: Gene set analysis aims to identify predefined sets of functionally related genes that are differentially expressed between two conditions. Although gene set analysis has been very successful, by incorporating biological knowledge about the gene sets and ... ...

    Abstract Gene set analysis aims to identify predefined sets of functionally related genes that are differentially expressed between two conditions. Although gene set analysis has been very successful, by incorporating biological knowledge about the gene sets and enhancing statistical power over gene-by-gene analyses, it does not take into account the correlation (association) structure among the genes. In this work, we present CoGA (Co-expression Graph Analyzer), an R package for the identification of groups of differentially associated genes between two phenotypes. The analysis is based on concepts of Information Theory applied to the spectral distributions of the gene co-expression graphs, such as the spectral entropy to measure the randomness of a graph structure and the Jensen-Shannon divergence to discriminate classes of graphs. The package also includes common measures to compare gene co-expression networks in terms of their structural properties, such as centrality, degree distribution, shortest path length, and clustering coefficient. Besides the structural analyses, CoGA also includes graphical interfaces for visual inspection of the networks, ranking of genes according to their "importance" in the network, and the standard differential expression analysis. We show by both simulation experiments and analyses of real data that the statistical tests performed by CoGA indeed control the rate of false positives and is able to identify differentially co-expressed genes that other methods failed.
    MeSH term(s) Algorithms ; Biomarkers, Tumor/genetics ; Brain Neoplasms/genetics ; Computational Biology/methods ; Computer Graphics ; Gene Expression Profiling ; Gene Expression Regulation ; Gene Regulatory Networks ; Humans ; Models, Biological ; Oligonucleotide Array Sequence Analysis/methods
    Chemical Substances Biomarkers, Tumor
    Language English
    Publishing date 2015-08-27
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0135831
    Database MEDical Literature Analysis and Retrieval System OnLINE

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