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  1. Article: PubSqueezer: A Text-Mining Web Tool to Transform Unstructured Documents into Structured Data

    Calderone, Alberto

    Abstract: The amount of scientific papers published every day is daunting and constantly increasing. Keeping up with literature represents a challenge. If one wants to start exploring new topics it is hard to have a big picture without reading lots of articles. ... ...

    Abstract The amount of scientific papers published every day is daunting and constantly increasing. Keeping up with literature represents a challenge. If one wants to start exploring new topics it is hard to have a big picture without reading lots of articles. Furthermore, as one reads through literature, making mental connections is crucial to ask new questions which might lead to discoveries. In this work, I present a web tool which uses a Text Mining strategy to transform large collections of unstructured biomedical articles into structured data. Generated results give a quick overview on complex topics which can possibly suggest not explicitly reported information. In particular, I show two Data Science analyses. First, I present a literature based rare diseases network build using this tool in the hope that it will help clarify some aspects of these less popular pathologies. Secondly, I show how a literature based analysis conducted with PubSqueezer results allows to describe known facts about SARS-CoV-2. In one sentence, data generated with PubSqueezer make it easy to use scientific literate in any computational analysis such as machine learning, natural language processing etc. Availability: http://www.pubsqueezer.com
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  2. Book ; Online: PubSqueezer

    Calderone, Alberto

    A Text-Mining Web Tool to Transform Unstructured Documents into Structured Data

    2020  

    Abstract: The amount of scientific papers published every day is daunting and constantly increasing. Keeping up with literature represents a challenge. If one wants to start exploring new topics it is hard to have a big picture without reading lots of articles. ... ...

    Abstract The amount of scientific papers published every day is daunting and constantly increasing. Keeping up with literature represents a challenge. If one wants to start exploring new topics it is hard to have a big picture without reading lots of articles. Furthermore, as one reads through literature, making mental connections is crucial to ask new questions which might lead to discoveries. In this work, I present a web tool which uses a Text Mining strategy to transform large collections of unstructured biomedical articles into structured data. Generated results give a quick overview on complex topics which can possibly suggest not explicitly reported information. In particular, I show two Data Science analyses. First, I present a literature based rare diseases network build using this tool in the hope that it will help clarify some aspects of these less popular pathologies. Secondly, I show how a literature based analysis conducted with PubSqueezer results allows to describe known facts about SARS-CoV-2. In one sentence, data generated with PubSqueezer make it easy to use scientific literate in any computational analysis such as machine learning, natural language processing etc. Availability: http://www.pubsqueezer.com
    Keywords Computer Science - Information Retrieval ; Computer Science - Computation and Language ; Quantitative Biology - Quantitative Methods ; H.3 ; I.2.7 ; E.0 ; J.3
    Subject code 028
    Publishing date 2020-11-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: A Computational Analysis of Natural Languages to Build a Sentence Structure Aware Artificial Neural Network

    Calderone, Alberto

    2019  

    Abstract: Natural languages are complexly structured entities. They exhibit characterising regularities that can be exploited to link them one another. In this work, I compare two morphological aspects of languages: Written Patterns and Sentence Structure. I show ... ...

    Abstract Natural languages are complexly structured entities. They exhibit characterising regularities that can be exploited to link them one another. In this work, I compare two morphological aspects of languages: Written Patterns and Sentence Structure. I show how languages spontaneously group by similarity in both analyses and derive an average language distance. Finally, exploiting Sentence Structure I developed an Artificial Neural Network capable of distinguishing languages suggesting that not only word roots but also grammatical sentence structure is a characterising trait which alone suffice to identify them.
    Keywords Computer Science - Computation and Language
    Publishing date 2019-06-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Using the MINT Database to Search Protein Interactions.

    Calderone, Alberto / Iannuccelli, Marta / Peluso, Daniele / Licata, Luana

    Current protocols in bioinformatics

    2020  Volume 69, Issue 1, Page(s) e93

    Abstract: The Molecular INTeractions Database (MINT) is a public database designed to store information about protein interactions. Protein interactions are extracted from scientific literature and annotated in the database by expert curators. Currently (October ... ...

    Abstract The Molecular INTeractions Database (MINT) is a public database designed to store information about protein interactions. Protein interactions are extracted from scientific literature and annotated in the database by expert curators. Currently (October 2019), MINT contains information on more than 26,000 proteins and more than 131,600 interactions in over 30 model organisms. This article provides protocols for searching MINT over the Internet, using the new MINT Web Page. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Searching MINT over the internet Alternate Protocol: MINT visualizer Basic Protocol 2: Submitting interaction data.
    MeSH term(s) DNA-Binding Proteins/metabolism ; Databases, Protein ; Internet ; Protein Interaction Mapping ; Proto-Oncogene Proteins c-akt/metabolism ; Search Engine
    Chemical Substances DNA-Binding Proteins ; Proto-Oncogene Proteins c-akt (EC 2.7.11.1)
    Language English
    Publishing date 2020-02-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2179022-X
    ISSN 1934-340X ; 1934-3396
    ISSN (online) 1934-340X
    ISSN 1934-3396
    DOI 10.1002/cpbi.93
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: SPV: a JavaScript Signaling Pathway Visualizer.

    Calderone, Alberto / Cesareni, Gianni

    Bioinformatics (Oxford, England)

    2017  Volume 34, Issue 15, Page(s) 2684–2686

    Abstract: Summary: The visualization of molecular interactions annotated in web resources is useful to offer to users such information in a clear intuitive layout. These interactions are frequently represented as binary interactions that are laid out in free ... ...

    Abstract Summary: The visualization of molecular interactions annotated in web resources is useful to offer to users such information in a clear intuitive layout. These interactions are frequently represented as binary interactions that are laid out in free space where, different entities, cellular compartments and interaction types are hardly distinguishable. Signaling Pathway Visualizer is a free open source JavaScript library, which offers a series of pre-defined elements, compartments and interaction types meant to facilitate the representation of signaling pathways consisting of causal interactions without neglecting simple protein-protein interaction networks.
    Availability and implementation: Freely available under Apache version 2 license; Source code: https://github.com/Sinnefa/SPV_Signaling_Pathway_Visualizer_v1.0. Language: JavaScript; Web technology: Scalable Vector Graphics; Libraries: D3.js.
    MeSH term(s) Computational Biology/methods ; Protein Interaction Maps ; Signal Transduction ; Software
    Language English
    Publishing date 2017-08-19
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/bty188
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Online: Analysis of Triplet Motifs in Biological Signed Oriented Graphs Suggests a Relationship Between Fine Topology and Function

    Calderone, Alberto / Cesareni, Gianni

    2018  

    Abstract: Background: Networks in different domains are characterized by similar global characteristics while differing in local structures. To further extend this concept, we investigated network regularities on a fine scale in order to examine the functional ... ...

    Abstract Background: Networks in different domains are characterized by similar global characteristics while differing in local structures. To further extend this concept, we investigated network regularities on a fine scale in order to examine the functional impact of recurring motifs in signed oriented biological networks. In this work we generalize to signaling net works some considerations made on feedback and feed forward loops and extend them by adding a close scrutiny of Linear Triplets, which have not yet been investigate in detail. Results: We studied the role of triplets, either open or closed (Loops or linear events) by enumerating them in different biological signaling networks and by comparing their significance profiles. We compared different data sources and investigated the fine topology of protein networks representing causal relationships based on transcriptional control, phosphorylation, ubiquitination and binding. Not only were we able to generalize findings that have already been reported but we also highlighted a connection between relative motif abundance and node function. Furthermore, by analyzing for the first time Linear Triplets, we highlighted the relative importance of nodes sitting in specific positions in closed signaling triplets. Finally, we tried to apply machine learning to show that a combination of motifs features can be used to derive node function. Availability: The triplets counter used for this work is available as a Cytoscape App and as a standalone command line Java application. http://apps.cytoscape.org/apps/counttriplets Keywords: Graph theory, graph analysis, graph topology, machine learning, cytoscape
    Keywords Quantitative Biology - Molecular Networks ; Computer Science - Machine Learning ; 05C10 ; 05C30
    Subject code 006
    Publishing date 2018-03-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article: SPV: a JavaScript Signaling Pathway Visualizer

    Calderone, Alberto / Cesareni, Gianni / Stegle, Oliver

    Bioinformatics. 2018 Aug. 01, v. 34, no. 15

    2018  

    Abstract: The visualization of molecular interactions annotated in web resources is useful to offer to users such information in a clear intuitive layout. These interactions are frequently represented as binary interactions that are laid out in free space where, ... ...

    Abstract The visualization of molecular interactions annotated in web resources is useful to offer to users such information in a clear intuitive layout. These interactions are frequently represented as binary interactions that are laid out in free space where, different entities, cellular compartments and interaction types are hardly distinguishable. Signaling Pathway Visualizer is a free open source JavaScript library, which offers a series of pre-defined elements, compartments and interaction types meant to facilitate the representation of signaling pathways consisting of causal interactions without neglecting simple protein–protein interaction networks. Freely available under Apache version 2 license; Source code: https://github.com/Sinnefa/SPV_Signaling_Pathway_Visualizer_v1.0. Language: JavaScript; Web technology: Scalable Vector Graphics; Libraries: D3.js.
    Keywords bioinformatics ; computer software ; protein-protein interactions ; signal transduction ; world wide web
    Language English
    Dates of publication 2018-0801
    Size p. 2684-2686.
    Publishing place Oxford University Press
    Document type Article
    ZDB-ID 1468345-3
    ISSN 1460-2059 ; 1367-4811 ; 1367-4803
    ISSN (online) 1460-2059 ; 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/bty188
    Database NAL-Catalogue (AGRICOLA)

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  8. Article ; Online: The cell-autonomous mechanisms underlying the activity of metformin as an anticancer drug.

    Sacco, Francesca / Calderone, Alberto / Castagnoli, Luisa / Cesareni, Gianni

    British journal of cancer

    2016  Volume 115, Issue 12, Page(s) 1451–1456

    Abstract: The biguanide drug metformin profoundly affects cell metabolism, causing an impairment of the cell energy balance and triggering a plethora of pleiotropic effects that vary depending on the cellular or environmental context. Interestingly, a decade ago, ... ...

    Abstract The biguanide drug metformin profoundly affects cell metabolism, causing an impairment of the cell energy balance and triggering a plethora of pleiotropic effects that vary depending on the cellular or environmental context. Interestingly, a decade ago, it was observed that metformin-treated diabetic patients have a significantly lower cancer risk. Although a variety of in vivo and in vitro observations emphasising the role of metformin as anticancer drug have been reported, the underlying mechanisms are still poorly understood. Here, we discuss our current understanding of the molecular mechanisms that are perturbed by metformin treatment and that might be relevant to understand its antitumour activities. We focus on the cell-autonomous mechanisms modulating growth and death of cancer cells.
    MeSH term(s) Antineoplastic Agents/pharmacology ; Antineoplastic Agents/therapeutic use ; Cell Death/drug effects ; Cell Proliferation/drug effects ; Disease Progression ; Humans ; Metformin/pharmacology ; Metformin/therapeutic use ; Neoplasms/drug therapy ; Neoplasms/pathology
    Chemical Substances Antineoplastic Agents ; Metformin (9100L32L2N)
    Language English
    Publishing date 2016-11-22
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 80075-2
    ISSN 1532-1827 ; 0007-0920
    ISSN (online) 1532-1827
    ISSN 0007-0920
    DOI 10.1038/bjc.2016.385
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  9. Article ; Online: SIGNOR: A Database of Causal Relationships Between Biological Entities-A Short Guide to Searching and Browsing.

    Lo Surdo, Prisca / Calderone, Alberto / Cesareni, Gianni / Perfetto, Livia

    Current protocols in bioinformatics

    2017  Volume 58, Page(s) 8.23.1–8.23.16

    Abstract: SIGNOR (http://signor.uniroma2.it), the SIGnaling Network Open Resource, is a database designed to store experimentally validated causal interactions, i.e., interactions where a source entity has a regulatory effect (up-regulation, down-regulation, etc.) ...

    Abstract SIGNOR (http://signor.uniroma2.it), the SIGnaling Network Open Resource, is a database designed to store experimentally validated causal interactions, i.e., interactions where a source entity has a regulatory effect (up-regulation, down-regulation, etc.) on a second target entity. SIGNOR acts both as a source of signaling information and a support for data analysis, modeling, and prediction. A user-friendly interface features the ability to search entries for any given protein or group of proteins and to display their interactions graphically in a network view. At the time of writing, SIGNOR stores approximately 16,000 manually curated interactions connecting more than 4,000 biological entities (proteins, chemicals, protein complexes, etc.) that play a role in signal transduction. SIGNOR also offers a collection of 37 signaling pathways. SIGNOR can be queried by three search tools: "single-entity" search, "multiple-entity" search, and "pathway" search. This manuscript describes two basic protocols detailing how to navigate and search the SIGNOR database and how to download the annotated dataset for local use. Finally, the support protocol reviews the utilities of the graphic visualizer. © 2017 by John Wiley & Sons, Inc.
    MeSH term(s) Biotechnology/methods ; Databases, Factual ; Databases, Protein ; Humans ; Proteins/metabolism ; Signal Transduction
    Chemical Substances Proteins
    Language English
    Publishing date 2017-06-27
    Publishing country United States
    Document type Journal Article
    ISSN 1934-340X
    ISSN (online) 1934-340X
    DOI 10.1002/cpbi.28
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: VirusMentha: a new resource for virus-host protein interactions.

    Calderone, Alberto / Licata, Luana / Cesareni, Gianni

    Nucleic acids research

    2014  Volume 43, Issue Database issue, Page(s) D588–92

    Abstract: Viral infections often cause diseases by perturbing several cellular processes in the infected host. Viral proteins target host proteins and either form new complexes or modulate the formation of functional host complexes. Describing and understanding ... ...

    Abstract Viral infections often cause diseases by perturbing several cellular processes in the infected host. Viral proteins target host proteins and either form new complexes or modulate the formation of functional host complexes. Describing and understanding the perturbation of the host interactome following viral infection is essential for basic virology and for the development of antiviral therapies. In order to provide a general overview of such interactions, a few years ago we developed VirusMINT. We have now extended the scope and coverage of VirusMINT and established VirusMentha, a new virus-virus and virus-host interaction resource build on the detailed curation protocols of the IMEx consortium and on the integration strategies developed for mentha. VirusMentha is regularly and automatically updated every week by capturing, via the PSICQUIC protocol, interactions curated by five different databases that are part of the IMEx consortium. VirusMentha can be freely browsed at http://virusmentha.uniroma2.it/ and its complete data set is available for download.
    MeSH term(s) Animals ; Databases, Protein ; Humans ; Internet ; Mice ; Protein Interaction Mapping ; Viral Proteins/metabolism ; Viruses/classification
    Chemical Substances Viral Proteins
    Language English
    Publishing date 2014-09-12
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 186809-3
    ISSN 1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
    ISSN (online) 1362-4962 ; 1362-4954
    ISSN 0301-5610 ; 0305-1048
    DOI 10.1093/nar/gku830
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1067: Show issues Location:
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