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  1. AU="Marianna Milano"
  2. AU="London, Kevin"
  3. AU=Pain Debkumar
  4. AU="Snider, Elizabeth"
  5. AU="Klein, J R"
  6. AU="Deslandes, K."
  7. AU="Xie, Dandan"
  8. AU=Ward Helen
  9. AU="Grace Deukmedjian"
  10. AU="Lindeman, Neal"
  11. AU="Okoli, Mary"
  12. AU="Mathew, Philip"
  13. AU=Staros Eric B

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  1. Article ; Online: SEDEG

    Giuseppe Agapito / Marianna Milano / Pietro Cinaglia / Mario Cannataro

    Informatics in Medicine Unlocked, Vol 44, Iss , Pp 101432- (2024)

    An automatic method for preprocessing and selection of seed genes from gene expression data

    1481  

    Abstract: Select Essential Differential Expressed Genes (SEDEG) is a software pipeline designed to simplify the time-consuming and error-prone task of preparing Differential Expressed Genes (DEGs) for Pathway Enrichment Analysis (PEA). It automatically ... ...

    Abstract Select Essential Differential Expressed Genes (SEDEG) is a software pipeline designed to simplify the time-consuming and error-prone task of preparing Differential Expressed Genes (DEGs) for Pathway Enrichment Analysis (PEA). It automatically preprocesses, filters, and selects DEGs, making interpreting results of gene expression microarrays and Genome-Wide Association Studies easier.SEDEG is a Python tool that automates multiple different actions simultaneously, saving researchers significant time and effort. It identifies crucial DEGs and enriched pathways related to the condition being investigated.The SEDEG pipeline is a tool that enhances the consolidation process of DEGs, which is essential for computing PEA. It achieves this by automating several manual steps, resulting in more accurate lists of DEGs for PEA analysis. This automation also improves the relevance and significance of enriched pathways. To download SEDEG, please visit https://gitlab.com/giuseppeagapito/sedeg.
    Keywords Differential Expressed Gene ; Pathway enrichment ; Micorarray ; Gene expression ; Parallel computing ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 004
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Multilayer network alignment based on topological assessment via embeddings

    Pietro Cinaglia / Marianna Milano / Mario Cannataro

    BMC Bioinformatics, Vol 24, Iss 1, Pp 1-

    2023  Volume 19

    Abstract: Abstract Background Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology ... ...

    Abstract Abstract Background Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a user-friendly command line interface, also having a built-in guided mode (step-by-step) for defining input parameters. Results We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively. Conclusions DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer ...
    Keywords Multilayer networks ; Network Alignment ; Network analysis ; Embeddings ; Topological similarity ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Subject code 000
    Language English
    Publishing date 2023-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: An Extensive Assessment of Network Embedding in PPI Network Alignment

    Marianna Milano / Chiara Zucco / Marzia Settino / Mario Cannataro

    Entropy, Vol 24, Iss 730, p

    2022  Volume 730

    Abstract: Network alignment is a fundamental task in network analysis. In the biological field, where the protein–protein interaction (PPI) is represented as a graph, network alignment allowed the discovery of underlying biological knowledge such as conserved ... ...

    Abstract Network alignment is a fundamental task in network analysis. In the biological field, where the protein–protein interaction (PPI) is represented as a graph, network alignment allowed the discovery of underlying biological knowledge such as conserved evolutionary pathways and functionally conserved proteins throughout different species. A recent trend in network science concerns network embedding, i.e., the modelling of nodes in a network as a low-dimensional feature vector. In this survey, we present an overview of current PPI network embedding alignment methods, a comparison among them, and a comparison to classical PPI network alignment algorithms. The results of this comparison highlight that: (i) only five network embeddings for network alignment algorithms have been applied in the biological context, whereas the literature presents several classical network alignment algorithms; (ii) there is a need for developing an evaluation framework that may enable a unified comparison between different algorithms; (iii) the majority of the proposed algorithms perform network embedding through matrix factorization-based techniques; (iv) three out of five algorithms leverage external biological resources, while the remaining two are designed for domain agnostic network alignment and tested on PPI networks; (v) two algorithms out of three are stated to perform multi-network alignment, while the remaining perform pairwise network alignment.
    Keywords network embedding ; network alignment ; PPI ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Language English
    Publishing date 2022-05-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Statistical and Network-Based Analysis of Italian COVID-19 Data

    Marianna Milano / Mario Cannataro

    International Journal of Environmental Research and Public Health ; Volume 17 ; Issue 12

    Communities Detection and Temporal Evolution

    2020  

    Abstract: The coronavirus disease (COVID-19) outbreak started in Wuhan, China, and it has rapidly spread across the world. Italy is one of the European countries most affected by COVID-19, and it has registered high COVID-19 death rates and the death toll. In this ...

    Abstract The coronavirus disease (COVID-19) outbreak started in Wuhan, China, and it has rapidly spread across the world. Italy is one of the European countries most affected by COVID-19, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period 24 February to 29 March 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices. Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behavior. In particular, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behavior. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e., how they join and leave them, along time and how community consistency changes along time and with respect to the different available data.
    Keywords COVID-19 ; network analysis ; community detection ; covid19
    Subject code 006
    Language English
    Publishing date 2020-06-12
    Publisher Multidisciplinary Digital Publishing Institute
    Publishing country ch
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Statistical and Network-Based Analysis of Italian COVID-19 Data

    Marianna Milano / Mario Cannataro

    International Journal of Environmental Research and Public Health, Vol 17, Iss 4182, p

    Communities Detection and Temporal Evolution

    2020  Volume 4182

    Abstract: The coronavirus disease (COVID-19) outbreak started in Wuhan, China, and it has rapidly spread across the world. Italy is one of the European countries most affected by COVID-19, and it has registered high COVID-19 death rates and the death toll. In this ...

    Abstract The coronavirus disease (COVID-19) outbreak started in Wuhan, China, and it has rapidly spread across the world. Italy is one of the European countries most affected by COVID-19, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period 24 February to 29 March 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices. Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behavior. In particular, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behavior. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e., how they join and leave them, along time and how community consistency changes along time and with respect to the different available data.
    Keywords COVID-19 ; network analysis ; community detection ; Medicine ; R ; covid19
    Subject code 006
    Language English
    Publishing date 2020-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: L-HetNetAligner

    Marianna Milano / Tijana Milenković / Mario Cannataro / Pietro Hiram Guzzi

    Scientific Reports, Vol 10, Iss 1, Pp 1-

    A novel algorithm for Local Alignment of Heterogeneous Biological Networks

    2020  Volume 20

    Abstract: Abstract Networks are largely used for modelling and analysing a wide range of biological data. As a consequence, many different research efforts have resulted in the introduction of a large number of algorithms for analysis and comparison of networks. ... ...

    Abstract Abstract Networks are largely used for modelling and analysing a wide range of biological data. As a consequence, many different research efforts have resulted in the introduction of a large number of algorithms for analysis and comparison of networks. Many of these algorithms can deal with networks with a single class of nodes and edges, also referred to as homogeneous networks. Recently, many different approaches tried to integrate into a single model the interplay of different molecules. A possible formalism to model such a scenario comes from node/edge coloured networks (also known as heterogeneous networks) implemented as node/ edge-coloured graphs. Therefore, the need for the introduction of algorithms able to compare heterogeneous networks arises. We here focus on the local comparison of heterogeneous networks, and we formulate it as a network alignment problem. To the best of our knowledge, the local alignment of heterogeneous networks has not been explored in the past. We here propose L-HetNetAligner a novel algorithm that receives as input two heterogeneous networks (node-coloured graphs) and builds a local alignment of them. We also implemented and tested our algorithm. Our results confirm that our method builds high-quality alignments. The following website *contains Supplementary File 1 material and the code.
    Keywords Medicine ; R ; Science ; Q
    Subject code 004 ; 006
    Language English
    Publishing date 2020-03-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: An extensive assessment of network alignment algorithms for comparison of brain connectomes

    Marianna Milano / Pietro Hiram Guzzi / Olga Tymofieva / Duan Xu / Christofer Hess / Pierangelo Veltri / Mario Cannataro

    BMC Bioinformatics, Vol 18, Iss S6, Pp 31-

    2017  Volume 45

    Abstract: Abstract Background Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the ... ...

    Abstract Abstract Background Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. Results In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. Conclusion The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.
    Keywords Human connectome ; Graph theory ; Alignment network algorithms ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2017-06-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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