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  1. Artikel: 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
    Schlagwörter covid19
    Verlag ArXiv
    Dokumenttyp Artikel
    Datenquelle COVID19

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  2. Buch ; 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
    Schlagwörter Computer Science - Information Retrieval ; Computer Science - Computation and Language ; Quantitative Biology - Quantitative Methods ; H.3 ; I.2.7 ; E.0 ; J.3
    Thema/Rubrik (Code) 028
    Erscheinungsdatum 2020-11-05
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Buch ; 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.
    Schlagwörter Computer Science - Computation and Language
    Erscheinungsdatum 2019-06-13
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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

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

    Current protocols in bioinformatics

    2020  Band 69, Heft 1, Seite(n) 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-Begriff(e) DNA-Binding Proteins/metabolism ; Databases, Protein ; Internet ; Protein Interaction Mapping ; Proto-Oncogene Proteins c-akt/metabolism ; Search Engine
    Chemische Substanzen DNA-Binding Proteins ; Proto-Oncogene Proteins c-akt (EC 2.7.11.1)
    Sprache Englisch
    Erscheinungsdatum 2020-02-03
    Erscheinungsland United States
    Dokumenttyp 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
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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

    Calderone, Alberto / Cesareni, Gianni

    Bioinformatics (Oxford, England)

    2017  Band 34, Heft 15, Seite(n) 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-Begriff(e) Computational Biology/methods ; Protein Interaction Maps ; Signal Transduction ; Software
    Sprache Englisch
    Erscheinungsdatum 2017-08-19
    Erscheinungsland England
    Dokumenttyp 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
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Buch ; 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
    Schlagwörter Quantitative Biology - Molecular Networks ; Computer Science - Machine Learning ; 05C10 ; 05C30
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2018-03-17
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Artikel: 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.
    Schlagwörter bioinformatics ; computer software ; protein-protein interactions ; signal transduction ; world wide web
    Sprache Englisch
    Erscheinungsverlauf 2018-0801
    Umfang p. 2684-2686.
    Erscheinungsort Oxford University Press
    Dokumenttyp Artikel
    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
    Datenquelle NAL Katalog (AGRICOLA)

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  8. Artikel ; 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  Band 115, Heft 12, Seite(n) 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-Begriff(e) 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
    Chemische Substanzen Antineoplastic Agents ; Metformin (9100L32L2N)
    Sprache Englisch
    Erscheinungsdatum 2016-11-22
    Erscheinungsland England
    Dokumenttyp 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
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Artikel ; 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  Band 58, Seite(n) 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-Begriff(e) Biotechnology/methods ; Databases, Factual ; Databases, Protein ; Humans ; Proteins/metabolism ; Signal Transduction
    Chemische Substanzen Proteins
    Sprache Englisch
    Erscheinungsdatum 2017-06-27
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 1934-340X
    ISSN (online) 1934-340X
    DOI 10.1002/cpbi.28
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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

    Calderone, Alberto / Licata, Luana / Cesareni, Gianni

    Nucleic acids research

    2014  Band 43, Heft Database issue, Seite(n) 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-Begriff(e) Animals ; Databases, Protein ; Humans ; Internet ; Mice ; Protein Interaction Mapping ; Viral Proteins/metabolism ; Viruses/classification
    Chemische Substanzen Viral Proteins
    Sprache Englisch
    Erscheinungsdatum 2014-09-12
    Erscheinungsland England
    Dokumenttyp 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
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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