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Article ; Online: Prediction and Ranking of Biomarkers Using

Baltsavia, Ismini / Theodosiou, Theodosios / Papanikolaou, Nikolas / Pavlopoulos, Georgios A / Amoutzias, Grigorios D / Panagopoulou, Maria / Chatzaki, Ekaterini / Andreakos, Evangelos / Iliopoulos, Ioannis

International journal of molecular sciences

2022 Volume 23, Issue 19

Abstract: Protein-protein interactions (PPIs) are of key importance for understanding how cells and organisms function. Thus, in recent decades, many approaches have been developed for the identification and discovery of such interactions. These approaches ... ...

Abstract	Protein-protein interactions (PPIs) are of key importance for understanding how cells and organisms function. Thus, in recent decades, many approaches have been developed for the identification and discovery of such interactions. These approaches addressed the problem of PPI identification either by an experimental point of view or by a computational one. Here, we present an updated version of UniReD, a computational prediction tool which takes advantage of biomedical literature aiming to extract documented, already published protein associations and predict undocumented ones. The usefulness of this computational tool has been previously evaluated by experimentally validating predicted interactions and by benchmarking it against public databases of experimentally validated PPIs. In its updated form, UniReD allows the user to provide a list of proteins of known implication in, e.g., a particular disease, as well as another list of proteins that are potentially associated with the proteins of the first list. UniReD then automatically analyzes both lists and ranks the proteins of the second list by their association with the proteins of the first list, thus serving as a potential biomarker discovery/validation tool.
MeSH term(s)	Biomarkers ; Computational Biology ; Protein Interaction Mapping ; Proteins/metabolism
Chemical Substances	Biomarkers ; Proteins
Language	English
Publishing date	2022-09-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms231911112
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Genome urbanization: clusters of topologically co-regulated genes delineate functional compartments in the genome of Saccharomyces cerevisiae.

Tsochatzidou, Maria / Malliarou, Maria / Papanikolaou, Nikolas / Roca, Joaquim / Nikolaou, Christoforos

Nucleic acids research

2017 Volume 45, Issue 10, Page(s) 5818–5828

Abstract: The eukaryotic genome evolves under the dual constraint of maintaining coordinated gene transcription and performing effective DNA replication and cell division, the coupling of which brings about inevitable DNA topological tension. DNA supercoiling is ... ...

Abstract	The eukaryotic genome evolves under the dual constraint of maintaining coordinated gene transcription and performing effective DNA replication and cell division, the coupling of which brings about inevitable DNA topological tension. DNA supercoiling is resolved and, in some cases, even harnessed by the genome through the function of DNA topoisomerases, as has been shown in the concurrent transcriptional activation and suppression of genes upon transient deactivation of topoisomerase II (topoII). By analyzing a genome-wide transcription run-on experiment upon thermal inactivation of topoII in Saccharomyces cerevisiae we were able to define 116 gene clusters of consistent response (either positive or negative) to topological stress. A comprehensive analysis of these topologically co-regulated gene clusters reveals pronounced preferences regarding their functional, regulatory and structural attributes. Genes that negatively respond to topological stress, are positioned in gene-dense pericentromeric regions, are more conserved and associated to essential functions, while upregulated gene clusters are preferentially located in the gene-sparse nuclear periphery, associated with secondary functions and under complex regulatory control. We propose that genome architecture evolves with a core of essential genes occupying a compact genomic 'old town', whereas more recently acquired, condition-specific genes tend to be located in a more spacious 'suburban' genomic periphery.
MeSH term(s)	Amino Acid Sequence ; Cell Compartmentation/genetics ; Conserved Sequence ; DNA Replication ; DNA Topoisomerases, Type II/genetics ; DNA Topoisomerases, Type II/metabolism ; DNA, Fungal/genetics ; DNA, Fungal/metabolism ; Gene Expression Regulation, Fungal ; Gene Ontology ; Genome, Fungal ; Molecular Sequence Annotation ; Multigene Family ; Saccharomyces cerevisiae/genetics ; Saccharomyces cerevisiae/metabolism ; Saccharomyces cerevisiae Proteins/genetics ; Saccharomyces cerevisiae Proteins/metabolism ; Transcription, Genetic
Chemical Substances	DNA, Fungal ; Saccharomyces cerevisiae Proteins ; DNA Topoisomerases, Type II (EC 5.99.1.3)
Language	English
Publishing date	2017-06-02
Publishing country	England
Document type	Journal Article
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/gkx198
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Protein-protein interaction predictions using text mining methods.

Papanikolaou, Nikolas / Pavlopoulos, Georgios A / Theodosiou, Theodosios / Iliopoulos, Ioannis

Methods (San Diego, Calif.)

2015 Volume 74, Page(s) 47–53

Abstract: It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, ... ...

Abstract	It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools.
MeSH term(s)	Animals ; Data Mining/methods ; Data Mining/trends ; Databases, Protein/trends ; Forecasting ; Humans ; Protein Interaction Mapping/methods ; Protein Interaction Mapping/trends
Language	English
Publishing date	2015-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2014.10.026
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Immune response (IgG) following full inoculation with BNT162b2 COVID‑19 mRNA among healthcare professionals.

Tsatsakis, Aristidis / Vakonaki, Elena / Tzatzarakis, Manolis / Flamourakis, Matthaios / Nikolouzakis, Taxiarchis Konstantinos / Poulas, Konstantinos / Papazoglou, Georgios / Hatzidaki, Eleftheria / Papanikolaou, Nikolas C / Drakoulis, Nikolaos / Iliaki, Evangelia / Goulielmos, Georgios N / Kallionakis, Manolis / Lazopoulos, Georgios / Kteniadakis, Stelios / Alegkakis, Athanasios / Farsalinos, Konstantinos / Spandidos, Demetrios A

International journal of molecular medicine

2021 Volume 48, Issue 5

Abstract: Soon after the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) pandemic in December, 2019, numerous research teams, assisted by vast capital investments, achieved vaccine development in a fraction of time. However, almost 8 ... ...

Abstract	Soon after the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) pandemic in December, 2019, numerous research teams, assisted by vast capital investments, achieved vaccine development in a fraction of time. However, almost 8 months following the initiation of the European vaccination programme, the need for prospective monitoring of the vaccine‑induced immune response, its determinants and related side‑effects remains a priority. The present study aimed to quantify the immune response following full vaccination with the BNT162b2 coronavirus disease 2019 (COVID‑19) mRNA vaccine by measuring the levels of immunoglobulin G (IgG) titers in healthcare professionals. Moreover, common side‑effects and factors associated with IgG titers were identified. For this purpose, blood samples from 517 individuals were obtained and analysed. Blood sampling was performed at a mean period of 69.0±23.5 days following the second dose of the vaccine. SARS‑CoV‑2 IgG titers had an overall mean value of 4.23±2.76. Females had higher titers than males (4.44±2.70 and 3.89 ±2.84, respectively; P=0.007), while non‑smokers had higher titers than smokers (4.48±2.79 and 3.80±2.64, respectively; P=0.003). An older age was also associated with lower antibody titers (P<0.001). Moreover, the six most prevalent adverse effects were pain at the injection site (72.1%), generalized fatigue (40.5%), malaise (36.3%), myalgia (31,0%), headache (25.8%) and dizziness/weakness (21.6%). The present study demonstrated that the immune response after receiving the BNT162b2 COVID‑19 mRNA vaccine is dependent on various modifiable and non‑modifiable factors. Overall, the findings of the present study highlight two key aspects of the vaccination programs: First, the need for prospective immunosurveillance studies in order to estimate the duration of immunity, and second, the need to identify those individuals who are at a greater risk of developing low IgG titers in order to evaluate the need for a third dose of the vaccine.
Language	English
Publishing date	2021-09-13
Publishing country	Greece
Document type	Journal Article
ZDB-ID	1444428-8
ISSN	1791-244X ; 1107-3756
ISSN (online)	1791-244X
ISSN	1107-3756
DOI	10.3892/ijmm.2021.5033
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: DrugQuest - a text mining workflow for drug association discovery.

Papanikolaou, Nikolas / Pavlopoulos, Georgios A / Theodosiou, Theodosios / Vizirianakis, Ioannis S / Iliopoulos, Ioannis

BMC bioinformatics

2016 Volume 17 Suppl 5, Page(s) 182

Abstract: Background: Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract ... ...

Abstract	Background: Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Results: Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. Conclusions: DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .
MeSH term(s)	Algorithms ; Cluster Analysis ; Databases, Factual ; Drug Discovery ; Humans ; Internet ; Pharmaceutical Preparations/chemistry ; Pharmaceutical Preparations/metabolism ; User-Computer Interface
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2016-06-06
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-016-1041-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Proteinâprotein interaction predictions using text mining methods

Papanikolaou, Nikolas / Georgios A. Pavlopoulos / Ioannis Iliopoulos / Theodosios Theodosiou

Methods. 2015 Mar. 01, v. 74

2015

Abstract	It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting proteinâprotein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools.
Keywords	data collection ; databases ; prediction ; protein-protein interactions ; proteins
Language	English
Dates of publication	2015-0301
Size	p. 47-53.
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2014.10.026
Database	NAL-Catalogue (AGRICOLA)

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Article: Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future.

Pavlopoulos, Georgios A / Malliarakis, Dimitris / Papanikolaou, Nikolas / Theodosiou, Theodosis / Enright, Anton J / Iliopoulos, Ioannis

GigaScience

2015 Volume 4, Page(s) 38

Abstract: Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion ...

Abstract	"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.
MeSH term(s)	Genome ; Systems Biology
Language	English
Publishing date	2015
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2708999-X
ISSN	2047-217X
ISSN	2047-217X
DOI	10.1186/s13742-015-0077-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks.

Theodosiou, Theodosios / Efstathiou, Georgios / Papanikolaou, Nikolas / Kyrpides, Nikos C / Bagos, Pantelis G / Iliopoulos, Ioannis / Pavlopoulos, Georgios A

BMC research notes

2017 Volume 10, Issue 1, Page(s) 278

Abstract: Objective: Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a ...

Abstract	Objective: Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. Results: Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .
Language	English
Publishing date	2017-07-14
Publishing country	England
Document type	Journal Article
ZDB-ID	2413336-X
ISSN	1756-0500 ; 1756-0500
ISSN (online)	1756-0500
ISSN	1756-0500
DOI	10.1186/s13104-017-2607-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: NAP

Theodosios Theodosiou / Georgios Efstathiou / Nikolas Papanikolaou / Nikos C. Kyrpides / Pantelis G. Bagos / Ioannis Iliopoulos / Georgios A. Pavlopoulos

BMC Research Notes, Vol 10, Iss 1, Pp 1-

The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks

2017 Volume 9

Abstract: Abstract Objective Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better ... ...

Abstract	Abstract Objective Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network’s size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. Results Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .
Keywords	Network biology ; Network topology ; Node and edge ranking ; Centralities ; Network comparison ; Medicine ; R ; Biology (General) ; QH301-705.5 ; Science (General) ; Q1-390
Subject code	000
Language	English
Publishing date	2017-07-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Metagenomics

Anastasis Oulas / Christina Pavloudi / Paraskevi Polymenakou / Georgios A. Pavlopoulos / Nikolas Papanikolaou / Georgios Kotoulas / Christos Arvanitidis / Ioannis Iliopoulos

Bioinformatics and Biology Insights, Vol 2015, Iss 9, Pp 75-

Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies

2015 Volume 88

Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2015-05-01T00:00:00Z
Publisher	SAGE Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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