Systematic discovery of phosphorylation networks - Combining linear motifs and protein interactions
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Lars Juhl Jensen presents a method called NetworKIN that combines protein interaction data from STRING with phosphorylation site sequence motifs and kinase specificity data to predict phosphorylation networks. NetworKIN was able to make over 7,000 predictions of kinase-substrate relationships with over 2.5 times greater accuracy than previous methods. Incorporating protein interaction context information was crucial to improve prediction accuracy compared to only using sequence motif data.
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A Infodesk é uma empresa especializada em software de gestão empresarial para pequenas e médias empresas. Há 9 anos em Campinas, a Infodesk oferece soluções personalizadas para varejistas, assistência técnica, distribuidoras e indústrias. Seu objetivo é fornecer ferramentas e consultoria de gestão empresarial com baixo investimento e tecnologia para ajudar empresas menores a tomarem decisões melhores.
Bendigoswsc Vision
This document discusses considerations for creating a virtual classroom. It emphasizes personalized and collaborative learning through open-ended questions, global communities, problem solving challenges, and reflection. A virtual classroom using Web 2.0 tools could transform learning by extending classroom time and allowing teachers and students to co-construct knowledge through blogs, wikis, and other digital media.
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\"Rutas por el pasado de América: adorno y cuerpo\".
\"Rutas por el pasado de América: adorno y cuerpo\".
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Informal presentation on bioinformatics
Here are some suggestions for open online bioinformatics lectures and courses from famous universities:
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- EMBL-
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More from Lars Juhl Jensen
One tagger, many uses: Illustrating the power of dictionary-based named entit...
This document summarizes a Twitter thread discussing the uses of a dictionary-based named entity recognition tool called Tagger. Tagger can recognize genes, proteins, diseases and other biomedical entities. It is open source, runs quickly processing over 1000 abstracts per second, and achieves 70-80% recall and 80-90% precision. Tagger has been applied to tasks like identifying drug-disease associations, adverse drug events, and protein-protein interactions. It is available as a Docker container or web service.
One tagger, many uses: Simple text-mining strategies for biomedicine
The document summarizes a text mining tool called a tagger that can be used for named entity recognition in biomedical texts. It recognizes genes, proteins, chemicals, diseases, and other entities. The tagger is open source, runs quickly at over 1000 abstracts per second, and has 70-80% recall and 80-90% precision. It comes with Python and Docker implementations and can be accessed via a web service. It is useful for tasks like extracting functional associations from literature and electronic health records.
Extract 2.0: Text-mining-assisted interactive annotation
This document describes Extract 2.0, a text-mining tool that can assist with interactive annotation of documents. It uses dictionary-based tagging to identify relevant entities like genes and diseases. It achieves 70-80% recall and 80-90% precision on entity extraction and was evaluated in BioCreative challenges where it received positive feedback from curators. The tool is open source and available as a web service or Python wrapper.
Network visualization: A crash course on using Cytoscape
This document discusses using Cytoscape, a network analysis tool, to import and visualize networks from STRING and STITCH databases. It provides three examples of networks created from literature and disease queries, demonstrating how to import networks and tables, apply node attributes and visual styles, perform enrichment analysis, and more.
STRING & STITCH : Network integration of heterogeneous data
The document discusses STRING and STITCH, two online databases that integrate data on protein-protein interactions, pathways, and functional associations from various sources. STRING collects data on over 9.6 million proteins and 430 thousand chemicals from sources like text mining, experimental assays, and co-expression analyses. It aims to provide a comprehensive global view of known and predicted protein associations. STITCH also integrates interaction data but focuses more on chemical-protein interactions. Both databases provide user-friendly web interfaces for browsing and visualizing interaction networks.
Biomedical text mining: Automatic processing of unstructured text
1) Lars Juhl Jensen discusses biomedical text mining and automatic processing of unstructured text such as patent literature, grant proposals, FDA product labels, and electronic medical records.
2) Named entity recognition is used to identify genes/proteins, chemical compounds, diseases, and other entities in text through comprehensive dictionaries and flexible matching rules that account for variations.
3) Relation extraction uses natural language processing techniques like part-of-speech tagging and sentence parsing along with manually crafted rules and machine learning to identify implicit relations between entities in text such as transcription factor targets, kinase substrates, and protein-protein interactions.
Medical network analysis: Linking diseases and genes through data and text mi...
The document summarizes the work of Lars Juhl Jensen and others on medical network analysis and linking diseases and genes through data and text mining of electronic health records. It discusses how they have used Danish national health registries containing data on over 6 million patients and 119 million diagnoses over 14 years to study disease trajectories and comorbidities. It also describes how they have developed methods to integrate data from various sources to generate networks linking diseases and genes.
Network Biology: A crash course on STRING and Cytoscape
This document provides an overview of STRING, a protein-protein association database, and Cytoscape, a network visualization tool. It describes how STRING contains functional associations between proteins derived from genomic context, co-expression and curated databases. Cytoscape can import STRING networks and external data to map onto nodes. It offers visualization of networks through layouts and attributes, and analysis through clustering, selection filters and enrichment. The document recommends using these tools together to explore protein association networks.
Cellular networks
This document discusses different approaches to visualizing cellular networks and the molecular interactions between proteins. It notes that there are many different types of data that could be shown, such as protein names, functions, localization, expression, modifications, and interaction types. However, it is impossible to show all this information at once. The document recommends using different visualizations like force-directed layouts to distribute proteins in 2D or lining up interactions in 1D. It acknowledges open challenges like showing time-course data and modification sites. In the end, the document thanks several researchers who have contributed to mapping and visualizing cellular networks.
Cellular Network Biology: Large-scale integration of data and text
The document discusses various community resources and software tools for integrating large-scale data and text, including STRING for protein networks, STITCH for chemical networks, COMPARTMENTS for subcellular localization, TISSUES for tissue expression, and DISEASES for disease associations. It provides an overview of text mining techniques used to extract information from literature to build networks in these resources. The presenter demonstrates the Cytoscape App which can import and analyze networks from STRING, perform queries, and analyze subcellular localization, tissue expression, and disease enrichment.
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The document discusses the STRING database, which integrates heterogeneous biological data to generate association networks for proteins. It describes how STRING collects and connects curated knowledge, experimental data, and predicted interactions from genomic context, co-expression and text mining. The document also outlines exercises for users to explore protein-protein associations in STRING and related databases that integrate data on subcellular localization, tissue expression, and disease associations.
Tagger: Rapid dictionary-based named entity recognition
Tagger is a named entity recognition tool that can process over 1000 abstracts per second using a dictionary-based approach. It achieves 70-80% recall and 80-90% precision using comprehensive dictionaries, expansion rules, and a curated blacklist to identify entity types like genes, proteins, chemicals, and diseases. The tool has a C++ engine, is inherently thread-safe, and includes interactive annotation, Python wrappers, and a REST API.
Network Biology: Large-scale integration of data and text
Lars Juhl Jensen leads a group that conducts large-scale integration of biological and medical data using proteomics, text mining, and medical data mining. The group develops protein interaction networks, disease networks, and association networks. They collaborate internationally on projects involving over 9.6 million proteins and 2000 genomes. The group works to integrate data from many sources in different formats to build comprehensive networks and knowledgebases, and also mines biomedical text to link genes and proteins with diseases.
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This document discusses medical text mining and linking diseases, drugs, and adverse reactions. It describes using text mining on clinical narratives in Danish to recognize named entities like drugs and diseases, identify relationships between them like adverse drug reactions, and discover new ADRs. The goal is to generate structured data on topics like comorbidities, diagnosis trajectories, and reimbursement to supplement limited structured data and help busy doctors by analyzing large amounts of unstructured text.
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Network biology: Large-scale integration of data and text
This document discusses natural language processing (NLP) techniques for extracting information from biomedical literature and integrating it with network and interaction data. It describes how NLP is used to identify entities like genes and proteins, extract relationships between entities, and integrate this text-mined information with existing interaction networks from databases like STRING to expand knowledge of protein interactions, complexes, pathways and associations with diseases. The document provides examples of using NLP analysis on sentences and the STRING and Tissues databases to explore tissue specificity and disease relationships for insulin and the insulin receptor.
Biomarker bioinformatics: Network-based candidate prioritization
The document discusses three parts of biomarker bioinformatics: data integration from multiple databases, text mining of scientific literature, and using that integrated data to prioritize biomarker candidates. It describes combining data on 9.6 million proteins from curated databases, using text mining to extract named entities from over 10,000 papers, and then using network and heat diffusion approaches to rank candidates based on evidence in the integrated data. The goal is to help identify new biomarker candidates from large amounts of biological data.
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One tagger, many uses: Illustrating the power of dictionary-based named entit...
One tagger, many uses: Illustrating the power of dictionary-based named entit...
One tagger, many uses: Simple text-mining strategies for biomedicine
One tagger, many uses: Simple text-mining strategies for biomedicine
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Extract 2.0: Text-mining-assisted interactive annotation
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Cellular Network Biology: Large-scale integration of data and text
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Presented by Vladimir Iglovikov:
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Systematic discovery of phosphorylation networks - Combining linear motifs and protein interactions
- 1. Systematic discovery of phosphorylation networks Combining linear motifs and protein interactions Lars Juhl Jensen EMBL Heidelberg
- 13. data integration
- 26. in vivo
- 29. in vitro
- 30. no context
- 31. what a kinase could do
- 32. not what it actually does
- 34. sequence motifs
- 36. kinase families
- 38. overprediction
- 39. no context
- 40. what a kinase could do
- 41. not what it actually does
- 42. in vitro
- 43. in vivo
- 44. context
- 45. localization
- 46. expression
- 47. co-activators
- 48. scaffolders
- 49. protein networks
- 51. the idea
- 55. sequence motifs
- 57. kinase families
- 58. protein networks
- 60. context
- 61. in vitro
- 62. in vivo
- 63. “ shake and bake”
- 65. NetworKIN
- 67. STRING
- 69. 373 genomes
- 74. gene fusion
- 89. many sources
- 92. redundancy
- 93. variable quality
- 94. spread over many species
- 95. benchmarking
- 101. the results
- 103. 7797 predictions
- 104. 1790 substrates
- 105. 69 kinases
- 107. benchmarking
- 108. Phospho.ELM
- 111. context is crucial
- 112. localization
- 114. visualization
- 116. ATM signaling
- 126. the future
- 127. more sequence motifs
- 128. in vivo
- 129. in vitro
- 130. automatic pipeline
- 132. data organization
- 134. benchmarking
- 136. selection
- 138. 179 kinases
- 139. 89 SH2 domains
- 140. 8 PTB domains
- 141. upstream signaling
- 142. downstream signaling
- 144. signaling pathways