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Article: Network module identification—A widespread theoretical bias and best practices

Nikolayeva, Iryna / Guitart Pla, Oriol / Schwikowski, Benno

Methods. 2018 Jan. 01, v. 132

2018

Abstract: Biological processes often manifest themselves as coordinated changes across modules, i.e., sets of interacting genes. Commonly, the high dimensionality of genome-scale data prevents the visual identification of such modules, and straightforward ... ...

Abstract	Biological processes often manifest themselves as coordinated changes across modules, i.e., sets of interacting genes. Commonly, the high dimensionality of genome-scale data prevents the visual identification of such modules, and straightforward computational search through a set of known pathways is a limited approach. Therefore, tools for the data-driven, computational, identification of modules in gene interaction networks have become popular components of visualization and visual analytics workflows. However, many such tools are known to result in modules that are large, and therefore hard to interpret biologically.Here, we show that the empirically known tendency towards large modules can be attributed to a statistical bias present in many module identification tools, and discuss possible remedies from a mathematical perspective. In the current absence of a straightforward practical solution, we outline our view of best practices for the use of the existing tools.
Keywords	bias ; gene interaction ; genes
Language	English
Dates of publication	2018-0101
Size	p. 19-25.
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.2017.08.008
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Article ; Online: Network module identification-A widespread theoretical bias and best practices.

Nikolayeva, Iryna / Guitart Pla, Oriol / Schwikowski, Benno

Methods (San Diego, Calif.)

2017 Volume 132, Page(s) 19–25

Abstract	Biological processes often manifest themselves as coordinated changes across modules, i.e., sets of interacting genes. Commonly, the high dimensionality of genome-scale data prevents the visual identification of such modules, and straightforward computational search through a set of known pathways is a limited approach. Therefore, tools for the data-driven, computational, identification of modules in gene interaction networks have become popular components of visualization and visual analytics workflows. However, many such tools are known to result in modules that are large, and therefore hard to interpret biologically. Here, we show that the empirically known tendency towards large modules can be attributed to a statistical bias present in many module identification tools, and discuss possible remedies from a mathematical perspective. In the current absence of a straightforward practical solution, we outline our view of best practices for the use of the existing tools.
MeSH term(s)	Algorithms ; Bias ; Computational Biology/methods ; Gene Expression Profiling ; Gene Regulatory Networks ; Humans
Language	English
Publishing date	2017-09-21
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2017.08.008
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Article ; Online: Exploring the OncoGenomic Landscape of cancer.

Mateo, Lidia / Guitart-Pla, Oriol / Duran-Frigola, Miquel / Aloy, Patrick

Genome medicine

2018 Volume 10, Issue 1, Page(s) 61

Abstract: Background: The widespread incorporation of next-generation sequencing into clinical oncology has yielded an unprecedented amount of molecular data from thousands of patients. A main current challenge is to find out reliable ways to extrapolate results ... ...

Abstract	Background: The widespread incorporation of next-generation sequencing into clinical oncology has yielded an unprecedented amount of molecular data from thousands of patients. A main current challenge is to find out reliable ways to extrapolate results from one group of patients to another and to bring rationale to individual cases in the light of what is known from the cohorts. Results: We present OncoGenomic Landscapes, a framework to analyze and display thousands of cancer genomic profiles in a 2D space. Our tool allows users to rapidly assess the heterogeneity of large cohorts, enabling the comparison to other groups of patients, and using driver genes as landmarks to aid in the interpretation of the landscapes. In our web-server, we also offer the possibility of mapping new samples and cohorts onto 22 predefined landscapes related to cancer cell line panels, organoids, patient-derived xenografts, and clinical tumor samples. Conclusions: Contextualizing individual subjects in a more general landscape of human cancer is a valuable aid for basic researchers and clinical oncologists trying to identify treatment opportunities, maybe yet unapproved, for patients that ran out of standard therapeutic options. The web-server can be accessed at https://oglandscapes.irbbarcelona.org /.
MeSH term(s)	Biomarkers, Tumor/genetics ; Databases, Genetic ; Genome, Human ; Genomics/methods ; Humans ; Neoplasms/genetics ; Polymorphism, Genetic ; Software
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2018-08-03
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2484394-5
ISSN	1756-994X ; 1756-994X
ISSN (online)	1756-994X
ISSN	1756-994X
DOI	10.1186/s13073-018-0571-0
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Article ; Online: Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.

Duran-Frigola, Miquel / Pauls, Eduardo / Guitart-Pla, Oriol / Bertoni, Martino / Alcalde, Víctor / Amat, David / Juan-Blanco, Teresa / Aloy, Patrick

Nature biotechnology

2020 Volume 38, Issue 9, Page(s) 1087–1096

Abstract: Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and ... ...

Abstract	Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.
MeSH term(s)	Biological Products/chemistry ; Biological Products/metabolism ; Biological Products/therapeutic use ; Biomarkers, Pharmacological/metabolism ; Databases, Factual ; Drug Discovery ; Drug Therapy ; Humans ; Pharmaceutical Preparations/chemistry ; Pharmaceutical Preparations/metabolism ; Small Molecule Libraries/chemistry ; Small Molecule Libraries/metabolism ; Small Molecule Libraries/therapeutic use
Chemical Substances	Biological Products ; Biomarkers, Pharmacological ; Pharmaceutical Preparations ; Small Molecule Libraries
Language	English
Publishing date	2020-05-18
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1311932-1
ISSN	1546-1696 ; 1087-0156
ISSN (online)	1546-1696
ISSN	1087-0156
DOI	10.1038/s41587-020-0502-7
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Article ; Online: Publisher Correction: Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.

Duran-Frigola, Miquel / Pauls, Eduardo / Guitart-Pla, Oriol / Bertoni, Martino / Alcalde, Víctor / Amat, David / Juan-Blanco, Teresa / Aloy, Patrick

Nature biotechnology

2020 Volume 38, Issue 9, Page(s) 1098

Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper. ...

Abstract	An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Language	English
Publishing date	2020-05-21
Publishing country	United States
Document type	Published Erratum
ZDB-ID	1311932-1
ISSN	1546-1696 ; 1087-0156
ISSN (online)	1546-1696
ISSN	1087-0156
DOI	10.1038/s41587-020-0564-6
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Article ; Online: A PanorOmic view of personal cancer genomes.

Mateo, Lidia / Guitart-Pla, Oriol / Pons, Carles / Duran-Frigola, Miquel / Mosca, Roberto / Aloy, Patrick

Nucleic acids research

2017 Volume 45, Issue W1, Page(s) W195–W200

Abstract: The massive molecular profiling of thousands of cancer patients has led to the identification of many tumor type specific driver genes. However, only a few (or none) of them are present in each individual tumor and, to enable precision oncology, we need ... ...

Abstract	The massive molecular profiling of thousands of cancer patients has led to the identification of many tumor type specific driver genes. However, only a few (or none) of them are present in each individual tumor and, to enable precision oncology, we need to interpret the alterations found in a single patient. Cancer PanorOmics (http://panoromics.irbbarcelona.org) is a web-based resource to contextualize genomic variations detected in a personal cancer genome within the body of clinical and scientific evidence available for 26 tumor types, offering complementary cohort- and patient-centric views. Additionally, it explores the cellular environment of mutations by mapping them on the human interactome and providing quasi-atomic structural details, whenever available. This 'PanorOmic' molecular view of individual tumors, together with the appropriate genetic counselling and medical advice, should contribute to the identification of actionable alterations ultimately guiding the clinical decision-making process.
MeSH term(s)	Genes, Neoplasm ; Genome, Human ; High-Throughput Nucleotide Sequencing ; Humans ; Internet ; Kaplan-Meier Estimate ; Mutation ; Neoplasm Proteins/chemistry ; Neoplasm Proteins/metabolism ; Neoplasms/genetics ; Neoplasms/metabolism ; Neoplasms/mortality ; Protein Interaction Mapping ; Software
Chemical Substances	Neoplasm Proteins
Language	English
Publishing date	2017-05-24
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/gkx311
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Article ; Online: Bioactivity descriptors for uncharacterized chemical compounds.

Bertoni, Martino / Duran-Frigola, Miquel / Badia-I-Mompel, Pau / Pauls, Eduardo / Orozco-Ruiz, Modesto / Guitart-Pla, Oriol / Alcalde, Víctor / Diaz, Víctor M / Berenguer-Llergo, Antoni / Brun-Heath, Isabelle / Villegas, Núria / de Herreros, Antonio García / Aloy, Patrick

Nature communications

2021 Volume 12, Issue 1, Page(s) 3932

Abstract: Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical ...

Abstract	Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.
MeSH term(s)	Cell Line, Tumor ; Databases, Pharmaceutical ; Drug Evaluation, Preclinical/methods ; Humans ; Small Molecule Libraries/chemistry ; Small Molecule Libraries/pharmacology ; Snail Family Transcription Factors/antagonists & inhibitors ; Snail Family Transcription Factors/genetics ; Snail Family Transcription Factors/metabolism ; Structure-Activity Relationship
Chemical Substances	SNAI1 protein, human ; Small Molecule Libraries ; Snail Family Transcription Factors
Language	English
Publishing date	2021-06-24
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-24150-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The Cyni framework for network inference in Cytoscape.

Guitart-Pla, Oriol / Kustagi, Manjunath / Rügheimer, Frank / Califano, Andrea / Schwikowski, Benno

Bioinformatics (Oxford, England)

2014 Volume 31, Issue 9, Page(s) 1499–1501

Abstract: Motivation: Research on methods for the inference of networks from biological data is making significant advances, but the adoption of network inference in biomedical research practice is lagging behind. Here, we present Cyni, an open-source 'fill-in- ... ...

Abstract	Motivation: Research on methods for the inference of networks from biological data is making significant advances, but the adoption of network inference in biomedical research practice is lagging behind. Here, we present Cyni, an open-source 'fill-in-the-algorithm' framework that provides common network inference functionality and user interface elements. Cyni allows the rapid transformation of Java-based network inference prototypes into apps of the popular open-source Cytoscape network analysis and visualization ecosystem. Merely placing the resulting app in the Cytoscape App Store makes the method accessible to a worldwide community of biomedical researchers by mouse click. In a case study, we illustrate the transformation of an ARACNE implementation into a Cytoscape app. Availability and implementation: Cyni, its apps, user guides, documentation and sample code are available from the Cytoscape App Store http://apps.cytoscape.org/apps/cynitoolbox Contact: benno.schwikowski@pasteur.fr.
MeSH term(s)	Algorithms ; Gene Regulatory Networks ; Software
Language	English
Publishing date	2014-12-18
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btu812
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Article ; Online: A community challenge for a pancancer drug mechanism of action inference from perturbational profile data.

Douglass, Eugene F / Allaway, Robert J / Szalai, Bence / Wang, Wenyu / Tian, Tingzhong / Fernández-Torras, Adrià / Realubit, Ron / Karan, Charles / Zheng, Shuyu / Pessia, Alberto / Tanoli, Ziaurrehman / Jafari, Mohieddin / Wan, Fangping / Li, Shuya / Xiong, Yuanpeng / Duran-Frigola, Miquel / Bertoni, Martino / Badia-I-Mompel, Pau / Mateo, Lídia /

Guitart-Pla, Oriol / Chung, Verena / Tang, Jing / Zeng, Jianyang / Aloy, Patrick / Saez-Rodriguez, Julio / Guinney, Justin / Gerhard, Daniela S / Califano, Andrea

Cell reports. Medicine

2022 Volume 3, Issue 1, Page(s) 100492

Abstract: The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific ... ...

Abstract	The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for
MeSH term(s)	Algorithms ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Humans ; Neoplasms/drug therapy ; Neural Networks, Computer ; Polypharmacology ; Protein Kinases/metabolism ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; Transcription, Genetic
Chemical Substances	RNA, Messenger ; Protein Kinases (EC 2.7.-)
Language	English
Publishing date	2022-01-18
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2666-3791
ISSN (online)	2666-3791
DOI	10.1016/j.xcrm.2021.100492
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Article: Network-based analysis of omics data: the LEAN method

Gwinner, Frederik / Boulday, Gwénola / Vandiedonck, Claire / Arnould, Minh / Cardoso, Cécile / Nikolayeva, Iryna / Guitart-Pla, Oriol / Denis, Cécile V / Christophe, Olivier D / Beghain, Johann / Tournier-Lasserve, Elisabeth / Schwikowski, Benno

Bioinformatics. 2017 Mar. 01, v. 33, no. 5

2017

Abstract: Motivation: Most computational approaches for the analysis of omics data in the context of interaction networks have very long running times, provide single or partial, often heuristic, solutions and/or contain user-tuneable parameters. Results: We ... ...

Abstract	Motivation: Most computational approaches for the analysis of omics data in the context of interaction networks have very long running times, provide single or partial, often heuristic, solutions and/or contain user-tuneable parameters. Results: We introduce local enrichment analysis (LEAN) for the identification of dysregulated subnetworks from genome-wide omics datasets. By substituting the common subnetwork model with a simpler local subnetwork model, LEAN allows exact, parameter-free, efficient and exhaustive identification of local subnetworks that are statistically dysregulated, and directly implicates single genes for follow-up experiments. Evaluation on simulated and biological data suggests that LEAN generally detects dysregulated subnetworks better, and reflects biological similarity between experiments more clearly than standard approaches. A strong signal for the local subnetwork around Von Willebrand Factor (VWF), a gene which showed no change on the mRNA level, was identified by LEAN in transcriptome data in the context of the genetic disease Cerebral Cavernous Malformations (CCM). This signal was experimentally found to correspond to an unexpected strong cellular effect on the VWF protein. LEAN can be used to pinpoint statistically significant local subnetworks in any genome-scale dataset. Availability and Implementation: The R-package LEANR implementing LEAN is supplied as supplementary material and available on CRAN (https://cran.r-project.org). Contacts: benno@pasteur.fr or tournier-lasserve@univ-paris-diderot.fr Supplementary information: Supplementary data are available at Bioinformatics online.
Keywords	abnormal development ; bioinformatics ; blood coagulation factors ; computer software ; data collection ; gene expression ; genes ; genetic disorders ; messenger RNA ; models ; transcriptome
Language	English
Dates of publication	2017-0301
Size	p. 701-709.
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/btw676
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