Enviar pesquisa
Carregar
Integration of heterogeneous data
•
Transferir como PPT, PDF
•
1 gostou
•
303 visualizações
Lars Juhl Jensen
Seguir
9th Course in Bioinformatics for Molecular Biologist, Bertinoro, Italy, March 22-26, 2009
Leia menos
Leia mais
Tecnologia
Denunciar
Compartilhar
Denunciar
Compartilhar
1 de 252
Baixar agora
Recomendados
From Advanced Queries to Algorithms and Graph-Based ML: Tackling Diabetes wit...
From Advanced Queries to Algorithms and Graph-Based ML: Tackling Diabetes wit...
Neo4j
NetBioSIG2014-Talk by Traver Hart
NetBioSIG2014-Talk by Traver Hart
Alexander Pico
Biological literature mining - from information retrieval to biological disco...
Biological literature mining - from information retrieval to biological disco...
Lars Juhl Jensen
Literature mining and large-scale data integration
Literature mining and large-scale data integration
Lars Juhl Jensen
Literature Mining and Systems Biology
Literature Mining and Systems Biology
Lars Juhl Jensen
Exploring proteins, chemicals and their interactions with STRING and STITCH
Exploring proteins, chemicals and their interactions with STRING and STITCH
biocs
Lack of association between CD45 C77G polymorphism and multiple sclerosis in ...
Lack of association between CD45 C77G polymorphism and multiple sclerosis in ...
ijtsrd
Biomedical literature mining
Biomedical literature mining
Lars Juhl Jensen
Recomendados
From Advanced Queries to Algorithms and Graph-Based ML: Tackling Diabetes wit...
From Advanced Queries to Algorithms and Graph-Based ML: Tackling Diabetes wit...
Neo4j
NetBioSIG2014-Talk by Traver Hart
NetBioSIG2014-Talk by Traver Hart
Alexander Pico
Biological literature mining - from information retrieval to biological disco...
Biological literature mining - from information retrieval to biological disco...
Lars Juhl Jensen
Literature mining and large-scale data integration
Literature mining and large-scale data integration
Lars Juhl Jensen
Literature Mining and Systems Biology
Literature Mining and Systems Biology
Lars Juhl Jensen
Exploring proteins, chemicals and their interactions with STRING and STITCH
Exploring proteins, chemicals and their interactions with STRING and STITCH
biocs
Lack of association between CD45 C77G polymorphism and multiple sclerosis in ...
Lack of association between CD45 C77G polymorphism and multiple sclerosis in ...
ijtsrd
Biomedical literature mining
Biomedical literature mining
Lars Juhl Jensen
Introduction to data integration in bioinformatics
Introduction to data integration in bioinformatics
Yan Xu
Naveen Kumar Resume
Naveen Kumar Resume
mekalanaveenkumar
NetBioSIG2013-Talk Thomas Kelder
NetBioSIG2013-Talk Thomas Kelder
Alexander Pico
NetBioSIG2014-Talk by Gerald Quon
NetBioSIG2014-Talk by Gerald Quon
Alexander Pico
E1062632
E1062632
IJERD Editor
Systems biology - Understanding biology at the systems level
Systems biology - Understanding biology at the systems level
Lars Juhl Jensen
Literature mining: what is it, and should I care?
Literature mining: what is it, and should I care?
Lars Juhl Jensen
NetBioSIG2013-KEYNOTE Stefan Schuster
NetBioSIG2013-KEYNOTE Stefan Schuster
Alexander Pico
Data analysis & integration challenges in genomics
Data analysis & integration challenges in genomics
mikaelhuss
Introduction to Bioinformatics.
Introduction to Bioinformatics.
Elena Sügis
Cross-species data integration
Cross-species data integration
Lars Juhl Jensen
STRING: Large-scale data and text mining
STRING: Large-scale data and text mining
Lars Juhl Jensen
'Stories that persuade with data' - talk at CENDI meeting January 9 2014
'Stories that persuade with data' - talk at CENDI meeting January 9 2014
Anita de Waard
Large-scale integration of data and text
Large-scale integration of data and text
Lars Juhl Jensen
Exploiting technical replicate variance in omics data analysis (RepExplore)
Exploiting technical replicate variance in omics data analysis (RepExplore)
Enrico Glaab
Weber-Thesis
Weber-Thesis
Anna Weber
Whole genome taxonomic classication for prokaryotic plant pathogens
Whole genome taxonomic classication for prokaryotic plant pathogens
Leighton Pritchard
BM405 Lecture Slides 21/11/2014 University of Strathclyde
BM405 Lecture Slides 21/11/2014 University of Strathclyde
Leighton Pritchard
The pLoc bal-mHum is a powerful web-serve for predicting the subcellular loca...
The pLoc bal-mHum is a powerful web-serve for predicting the subcellular loca...
IJBNT Journal
iOmics
iOmics
InterpretOmics
Network integration of heterogeneous data
Network integration of heterogeneous data
Lars Juhl Jensen
Mining heterogeneous data: Understanding systems at the level of complexes an...
Mining heterogeneous data: Understanding systems at the level of complexes an...
Lars Juhl Jensen
Mais conteúdo relacionado
Mais procurados
Introduction to data integration in bioinformatics
Introduction to data integration in bioinformatics
Yan Xu
Naveen Kumar Resume
Naveen Kumar Resume
mekalanaveenkumar
NetBioSIG2013-Talk Thomas Kelder
NetBioSIG2013-Talk Thomas Kelder
Alexander Pico
NetBioSIG2014-Talk by Gerald Quon
NetBioSIG2014-Talk by Gerald Quon
Alexander Pico
E1062632
E1062632
IJERD Editor
Systems biology - Understanding biology at the systems level
Systems biology - Understanding biology at the systems level
Lars Juhl Jensen
Literature mining: what is it, and should I care?
Literature mining: what is it, and should I care?
Lars Juhl Jensen
NetBioSIG2013-KEYNOTE Stefan Schuster
NetBioSIG2013-KEYNOTE Stefan Schuster
Alexander Pico
Data analysis & integration challenges in genomics
Data analysis & integration challenges in genomics
mikaelhuss
Introduction to Bioinformatics.
Introduction to Bioinformatics.
Elena Sügis
Cross-species data integration
Cross-species data integration
Lars Juhl Jensen
STRING: Large-scale data and text mining
STRING: Large-scale data and text mining
Lars Juhl Jensen
'Stories that persuade with data' - talk at CENDI meeting January 9 2014
'Stories that persuade with data' - talk at CENDI meeting January 9 2014
Anita de Waard
Large-scale integration of data and text
Large-scale integration of data and text
Lars Juhl Jensen
Exploiting technical replicate variance in omics data analysis (RepExplore)
Exploiting technical replicate variance in omics data analysis (RepExplore)
Enrico Glaab
Weber-Thesis
Weber-Thesis
Anna Weber
Whole genome taxonomic classication for prokaryotic plant pathogens
Whole genome taxonomic classication for prokaryotic plant pathogens
Leighton Pritchard
BM405 Lecture Slides 21/11/2014 University of Strathclyde
BM405 Lecture Slides 21/11/2014 University of Strathclyde
Leighton Pritchard
The pLoc bal-mHum is a powerful web-serve for predicting the subcellular loca...
The pLoc bal-mHum is a powerful web-serve for predicting the subcellular loca...
IJBNT Journal
iOmics
iOmics
InterpretOmics
Mais procurados
(20)
Introduction to data integration in bioinformatics
Introduction to data integration in bioinformatics
Naveen Kumar Resume
Naveen Kumar Resume
NetBioSIG2013-Talk Thomas Kelder
NetBioSIG2013-Talk Thomas Kelder
NetBioSIG2014-Talk by Gerald Quon
NetBioSIG2014-Talk by Gerald Quon
E1062632
E1062632
Systems biology - Understanding biology at the systems level
Systems biology - Understanding biology at the systems level
Literature mining: what is it, and should I care?
Literature mining: what is it, and should I care?
NetBioSIG2013-KEYNOTE Stefan Schuster
NetBioSIG2013-KEYNOTE Stefan Schuster
Data analysis & integration challenges in genomics
Data analysis & integration challenges in genomics
Introduction to Bioinformatics.
Introduction to Bioinformatics.
Cross-species data integration
Cross-species data integration
STRING: Large-scale data and text mining
STRING: Large-scale data and text mining
'Stories that persuade with data' - talk at CENDI meeting January 9 2014
'Stories that persuade with data' - talk at CENDI meeting January 9 2014
Large-scale integration of data and text
Large-scale integration of data and text
Exploiting technical replicate variance in omics data analysis (RepExplore)
Exploiting technical replicate variance in omics data analysis (RepExplore)
Weber-Thesis
Weber-Thesis
Whole genome taxonomic classication for prokaryotic plant pathogens
Whole genome taxonomic classication for prokaryotic plant pathogens
BM405 Lecture Slides 21/11/2014 University of Strathclyde
BM405 Lecture Slides 21/11/2014 University of Strathclyde
The pLoc bal-mHum is a powerful web-serve for predicting the subcellular loca...
The pLoc bal-mHum is a powerful web-serve for predicting the subcellular loca...
iOmics
iOmics
Destaque
Network integration of heterogeneous data
Network integration of heterogeneous data
Lars Juhl Jensen
Mining heterogeneous data: Understanding systems at the level of complexes an...
Mining heterogeneous data: Understanding systems at the level of complexes an...
Lars Juhl Jensen
Integrating and Interpreting Social Data from Heterogeneous Sources
Integrating and Interpreting Social Data from Heterogeneous Sources
Matthew Rowe
Chi-Square test of Homogeneity by Pops P. Macalino (TSU-MAEd)
Chi-Square test of Homogeneity by Pops P. Macalino (TSU-MAEd)
pops macalino
Heterogeneous data fusion with multiple kernel growing self organizing maps
Heterogeneous data fusion with multiple kernel growing self organizing maps
Pruthuvi Maheshakya Wijewardena
Statistical Software
Statistical Software
Dennis Sanchez
Statistical software packages
Statistical software packages
Km Ashif
Domain specific Software Architecture
Domain specific Software Architecture
DIPEN SAINI
Twitter For Business The What, Why And How To Get Started Jonnie Jensen I...
Twitter For Business The What, Why And How To Get Started Jonnie Jensen I...
jonnie jensen
Using side effects for drug target identification
Using side effects for drug target identification
Lars Juhl Jensen
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Lars Juhl Jensen
Aplicaciones de herramientas digitales en el aula
Aplicaciones de herramientas digitales en el aula
María Gabriela Galli
Beijing
Beijing
nachaycoka.blogspot.com
Live+Social - Being Remarkable - the key to social business success
Live+Social - Being Remarkable - the key to social business success
jonnie jensen
One tagger, many uses - Illustrating the power of ontologies in named entity ...
One tagger, many uses - Illustrating the power of ontologies in named entity ...
Lars Juhl Jensen
STRING - Protein networks from data and text mining
STRING - Protein networks from data and text mining
Lars Juhl Jensen
Destaque
(16)
Network integration of heterogeneous data
Network integration of heterogeneous data
Mining heterogeneous data: Understanding systems at the level of complexes an...
Mining heterogeneous data: Understanding systems at the level of complexes an...
Integrating and Interpreting Social Data from Heterogeneous Sources
Integrating and Interpreting Social Data from Heterogeneous Sources
Chi-Square test of Homogeneity by Pops P. Macalino (TSU-MAEd)
Chi-Square test of Homogeneity by Pops P. Macalino (TSU-MAEd)
Heterogeneous data fusion with multiple kernel growing self organizing maps
Heterogeneous data fusion with multiple kernel growing self organizing maps
Statistical Software
Statistical Software
Statistical software packages
Statistical software packages
Domain specific Software Architecture
Domain specific Software Architecture
Twitter For Business The What, Why And How To Get Started Jonnie Jensen I...
Twitter For Business The What, Why And How To Get Started Jonnie Jensen I...
Using side effects for drug target identification
Using side effects for drug target identification
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Aplicaciones de herramientas digitales en el aula
Aplicaciones de herramientas digitales en el aula
Beijing
Beijing
Live+Social - Being Remarkable - the key to social business success
Live+Social - Being Remarkable - the key to social business success
One tagger, many uses - Illustrating the power of ontologies in named entity ...
One tagger, many uses - Illustrating the power of ontologies in named entity ...
STRING - Protein networks from data and text mining
STRING - Protein networks from data and text mining
Semelhante a Integration of heterogeneous data
Data Integration and Systems Biology
Data Integration and Systems Biology
Lars Juhl Jensen
Unraveling cellular phosphorylation networks using computational biology
Unraveling cellular phosphorylation networks using computational biology
Lars Juhl Jensen
Unraveling signaling networks by large-scale data integration
Unraveling signaling networks by large-scale data integration
Lars Juhl Jensen
Unraveling signal transduction networks through data integration
Unraveling signal transduction networks through data integration
Lars Juhl Jensen
Computational Biology - Signaling networks and drug repositioning
Computational Biology - Signaling networks and drug repositioning
Lars Juhl Jensen
Unraveling signaling networks by data integration
Unraveling signaling networks by data integration
Lars Juhl Jensen
Data integration and functional association networks
Data integration and functional association networks
Lars Juhl Jensen
From phosphoproteomics to signaling networks
From phosphoproteomics to signaling networks
Lars Juhl Jensen
Combining sequence motifs and protein interactions to unravel complex phospho...
Combining sequence motifs and protein interactions to unravel complex phospho...
Lars Juhl Jensen
Network biology: A basis for large-scale biomedical data mining
Network biology: A basis for large-scale biomedical data mining
Lars Juhl Jensen
Network biology: A basis for large-scale biomedical data mining
Network biology: A basis for large-scale biomedical data mining
Lars Juhl Jensen
Network biology: A basis for large-scale biomedical data mining
Network biology: A basis for large-scale biomedical data mining
Lars Juhl Jensen
Network biology - A basis for large-scale biomedica data mining
Network biology - A basis for large-scale biomedica data mining
Lars Juhl Jensen
Network biology
Network biology
Lars Juhl Jensen
Network biology: Large-scale biomedical data and text mining
Network biology: Large-scale biomedical data and text mining
Lars Juhl Jensen
Using networks to derive function
Using networks to derive function
Lars Juhl Jensen
Network biology
Network biology
Lars Juhl Jensen
Integration of heterogeneous data
Integration of heterogeneous data
Lars Juhl Jensen
Cellular network biology: Proteome-wide analysis of heterogeneous data
Cellular network biology: Proteome-wide analysis of heterogeneous data
Lars Juhl Jensen
STRING - Modeling of biological systems through cross-species data integ...
STRING - Modeling of biological systems through cross-species data integ...
Lars Juhl Jensen
Semelhante a Integration of heterogeneous data
(20)
Data Integration and Systems Biology
Data Integration and Systems Biology
Unraveling cellular phosphorylation networks using computational biology
Unraveling cellular phosphorylation networks using computational biology
Unraveling signaling networks by large-scale data integration
Unraveling signaling networks by large-scale data integration
Unraveling signal transduction networks through data integration
Unraveling signal transduction networks through data integration
Computational Biology - Signaling networks and drug repositioning
Computational Biology - Signaling networks and drug repositioning
Unraveling signaling networks by data integration
Unraveling signaling networks by data integration
Data integration and functional association networks
Data integration and functional association networks
From phosphoproteomics to signaling networks
From phosphoproteomics to signaling networks
Combining sequence motifs and protein interactions to unravel complex phospho...
Combining sequence motifs and protein interactions to unravel complex phospho...
Network biology: A basis for large-scale biomedical data mining
Network biology: A basis for large-scale biomedical data mining
Network biology: A basis for large-scale biomedical data mining
Network biology: A basis for large-scale biomedical data mining
Network biology: A basis for large-scale biomedical data mining
Network biology: A basis for large-scale biomedical data mining
Network biology - A basis for large-scale biomedica data mining
Network biology - A basis for large-scale biomedica data mining
Network biology
Network biology
Network biology: Large-scale biomedical data and text mining
Network biology: Large-scale biomedical data and text mining
Using networks to derive function
Using networks to derive function
Network biology
Network biology
Integration of heterogeneous data
Integration of heterogeneous data
Cellular network biology: Proteome-wide analysis of heterogeneous data
Cellular network biology: Proteome-wide analysis of heterogeneous data
STRING - Modeling of biological systems through cross-species data integ...
STRING - Modeling of biological systems through cross-species data integ...
Mais de Lars Juhl Jensen
One tagger, many uses: Illustrating the power of dictionary-based named entit...
One tagger, many uses: Illustrating the power of dictionary-based named entit...
Lars Juhl Jensen
One tagger, many uses: Simple text-mining strategies for biomedicine
One tagger, many uses: Simple text-mining strategies for biomedicine
Lars Juhl Jensen
Extract 2.0: Text-mining-assisted interactive annotation
Extract 2.0: Text-mining-assisted interactive annotation
Lars Juhl Jensen
Network visualization: A crash course on using Cytoscape
Network visualization: A crash course on using Cytoscape
Lars Juhl Jensen
STRING & STITCH: Network integration of heterogeneous data
STRING & STITCH: Network integration of heterogeneous data
Lars Juhl Jensen
Biomedical text mining: Automatic processing of unstructured text
Biomedical text mining: Automatic processing of unstructured text
Lars Juhl Jensen
Medical network analysis: Linking diseases and genes through data and text mi...
Medical network analysis: Linking diseases and genes through data and text mi...
Lars Juhl Jensen
Network Biology: A crash course on STRING and Cytoscape
Network Biology: A crash course on STRING and Cytoscape
Lars Juhl Jensen
Cellular networks
Cellular networks
Lars Juhl Jensen
Cellular Network Biology: Large-scale integration of data and text
Cellular Network Biology: Large-scale integration of data and text
Lars Juhl Jensen
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
Lars Juhl Jensen
STRING & related databases: Large-scale integration of heterogeneous data
STRING & related databases: Large-scale integration of heterogeneous data
Lars Juhl Jensen
Tagger: Rapid dictionary-based named entity recognition
Tagger: Rapid dictionary-based named entity recognition
Lars Juhl Jensen
Network Biology: Large-scale integration of data and text
Network Biology: Large-scale integration of data and text
Lars Juhl Jensen
Medical text mining: Linking diseases, drugs, and adverse reactions
Medical text mining: Linking diseases, drugs, and adverse reactions
Lars Juhl Jensen
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and text
Lars Juhl Jensen
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Lars Juhl Jensen
Cellular Network Biology
Cellular Network Biology
Lars Juhl Jensen
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and text
Lars Juhl Jensen
Biomarker bioinformatics: Network-based candidate prioritization
Biomarker bioinformatics: Network-based candidate prioritization
Lars Juhl Jensen
Mais de Lars Juhl Jensen
(20)
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
Extract 2.0: Text-mining-assisted interactive annotation
Extract 2.0: Text-mining-assisted interactive annotation
Network visualization: A crash course on using Cytoscape
Network visualization: A crash course on using Cytoscape
STRING & STITCH: Network integration of heterogeneous data
STRING & STITCH: Network integration of heterogeneous data
Biomedical text mining: Automatic processing of unstructured text
Biomedical text mining: Automatic processing of unstructured text
Medical network analysis: Linking diseases and genes through data and text mi...
Medical network analysis: Linking diseases and genes through data and text mi...
Network Biology: A crash course on STRING and Cytoscape
Network Biology: A crash course on STRING and Cytoscape
Cellular networks
Cellular networks
Cellular Network Biology: Large-scale integration of data and text
Cellular Network Biology: Large-scale integration of data and text
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
STRING & related databases: Large-scale integration of heterogeneous data
STRING & related databases: Large-scale integration of heterogeneous data
Tagger: Rapid dictionary-based named entity recognition
Tagger: Rapid dictionary-based named entity recognition
Network Biology: Large-scale integration of data and text
Network Biology: Large-scale integration of data and text
Medical text mining: Linking diseases, drugs, and adverse reactions
Medical text mining: Linking diseases, drugs, and adverse reactions
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and text
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Cellular Network Biology
Cellular Network Biology
Network biology: Large-scale integration of data and text
Network biology: Large-scale integration of data and text
Biomarker bioinformatics: Network-based candidate prioritization
Biomarker bioinformatics: Network-based candidate prioritization
Último
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
Gabriella Davis
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
giselly40
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
wesley chun
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
RTylerCroy
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Drew Madelung
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
debabhi2
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Enterprise Knowledge
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
Pixlogix Infotech
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
Enterprise Knowledge
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Rafal Los
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
wesley chun
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
Igalia
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
Antenna Manufacturer Coco
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Delhi Call girls
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
vu2urc
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
Earley Information Science
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
Delhi Call girls
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
Delhi Call girls
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Radu Cotescu
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Khem
Último
(20)
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Integration of heterogeneous data
1.
Lars Juhl Jensen
Integration of heterogeneous data
2.
Lars Juhl Jensen
Integration of heterogeneous data
3.
Lars Juhl Jensen
Integration of heterogeneous data
4.
5.
6.
what went wrong?
7.
a good question
8.
signaling networks
9.
Oda & Kitano,
Molecular Systems Biology , 2006
10.
long way to
go
11.
mass spectrometry
12.
Linding, Jensen, Ostheimer
et al., Cell , 2007
13.
phosphorylation sites
14.
in vivo
15.
kinases are unknown
16.
peptide assays
17.
Miller, Jensen et
al., Science Signaling , 2008
18.
sequence specificity
19.
kinase-specific
20.
in vitro
21.
no context
22.
what a kinase
could do
23.
not what it
actually does
24.
computational methods
25.
sequence specificity
26.
Miller, Jensen et
al., Science Signaling , 2008
27.
kinase-specific
28.
no context
29.
what a kinase
could do
30.
not what it
actually does
31.
in vitro
32.
in vivo
33.
context
34.
co-activators
35.
scaffolders
36.
expression
37.
association networks
38.
Linding, Jensen, Ostheimer
et al., Cell , 2007
39.
a good idea
40.
Linding, Jensen, Ostheimer
et al., Cell , 2007
41.
Part I sequence
motifs
42.
curated motifs
43.
PROSITE
44.
ELM
45.
HPRD
46.
regular expressions
47.
[ST]P.[KR]
48.
no score
49.
Miller, Jensen et
al., Science Signaling , 2008
50.
insufficient
51.
machine learning
52.
NetPhosK
53.
PredPhospho
54.
PHOSITE
55.
GPS
56.
KinasePhos
57.
PPSP
58.
GANNPhos
59.
PhoScan
60.
no regular updates
61.
NetPhorest
62.
Miller, Jensen et
al., Science Signaling , 2008
63.
data sources
64.
Phospho.ELM
65.
Diella et al.,
Nucleic Acids Res. , 2008
66.
Diella et al.,
Nucleic Acids Res. , 2008
67.
Scansite
68.
Obenauer et al.,
Nucleic Acids Res. , 2003
69.
Miller, Jensen et
al., Science Signaling , 2008
70.
common basis
71.
Miller, Jensen et
al., Science Signaling , 2008
72.
automated pipeline
73.
compilation of datasets
74.
classification vs. prediction
75.
Miller, Jensen et
al., Science Signaling , 2008
76.
homology reduction
77.
Miller, Jensen et
al., Science Signaling , 2008
78.
training and evaluation
79.
cross-validation
80.
Miller, Jensen et
al., Science Signaling , 2008
81.
classifier selection
82.
Miller, Jensen et
al., Science Signaling , 2008
83.
motif atlas
84.
85.
179 kinases
86.
93 SH2 domains
87.
8 PTB domains
88.
BRCT domains
89.
WW domains
90.
14-3-3 proteins
91.
phosphatases
92.
model organisms
93.
S. cerevisiae
94.
D. melanogaster
95.
C. elegans
96.
biological insights
97.
docking domains
98.
Miller, Jensen et
al., Science Signaling , 2008
99.
disease-related kinases
100.
Miller, Jensen et
al., Science Signaling , 2008
101.
predictive power
102.
ROC curves
103.
Miller, Jensen et
al., Science Signaling , 2008
104.
comparison
105.
Miller, Jensen et
al., Science Signaling , 2008
106.
conclusions
107.
data collection
108.
automation
109.
benchmarking
110.
homology reduction!
111.
Part II association
networks
112.
STRING
113.
Jensen, Kuhn et
al., Nucleic Acids Research , 2009
114.
functional associations
115.
data integration
116.
common basis
117.
630 genomes
118.
model organism databases
119.
Ensembl
120.
RefSeq
121.
genomic context methods
122.
gene fusion
123.
Korbel et al.,
Nature Biotechnology , 2004
124.
conserved neighborhood
125.
operons
126.
Korbel et al.,
Nature Biotechnology , 2004
127.
bidirectional promoters
128.
Korbel et al.,
Nature Biotechnology , 2004
129.
phylogenetic profiles
130.
Korbel et al.,
Nature Biotechnology , 2004
131.
primary experimental data
132.
protein interactions
133.
yeast two-hybrid
134.
affinity purification
135.
fragment complementation
136.
Jensen & Bork,
Science , 2008
137.
genetic interactions
138.
Beyer et al.,
Nature Reviews Genetics , 2007
139.
BIND Biomolecular Interaction
Network Database
140.
BioGRID General Repository
for Interaction Datasets
141.
DIP Database of
Interacting Proteins
142.
IntAct
143.
MINT Molecular Interactions
Database
144.
HPRD Human Protein
Reference Database
145.
PDB Protein Data
Bank
146.
inferred associations
147.
gene coexpression
148.
149.
GEO Gene Expression
Omnibus
150.
expression compendia
151.
curated knowledge
152.
complexes
153.
MIPS Munich Information
center for Protein Sequences
154.
Gene Ontology
155.
pathways
156.
Letunic & Bork,
Trends in Biochemical Sciences , 2008
157.
KEGG Kyoto Encyclopedia
of Genes and Genomes
158.
MetaCyc
159.
Reactome
160.
PID NCI-Nature Pathway
Interaction Database
161.
literature mining
162.
M EDLINE
163.
SGD Saccharomyces Genome
Database
164.
The Interactive Fly
165.
OMIM Online Mendelian
Inheritance in Man
166.
co-mentioning
167.
statistical methods
168.
NLP Natural Language
Processing
169.
170.
171.
easy in theory
…
172.
… but
not in practice
173.
different formats
174.
parsers
175.
different identifiers
176.
thesaurus
177.
redundant sources
178.
book keeping
179.
variable quality
180.
raw quality scores
181.
reproducibility
182.
von Mering et
al., Nucleic Acids Research , 2005
183.
benchmarking
184.
von Mering et
al., Nucleic Acids Research , 2005
185.
spread over 630
genomes
186.
transfer by orthology
187.
von Mering et
al., Nucleic Acids Research , 2005
188.
two modes
189.
COG mode
190.
von Mering et
al., Nucleic Acids Research , 2005
191.
protein mode
192.
von Mering et
al., Nucleic Acids Research , 2005
193.
combine all evidence
194.
visualize
195.
Frishman et al.,
Modern Genome Annotation , 2009
196.
STITCH
197.
198.
metabolite–enzyme links
199.
pathway databases
200.
Letunic & Bork,
Trends in Biochemical Sciences , 2008
201.
drug–target links
202.
Drugbank
203.
PDSP K i
204.
MATADOR
205.
Campillos & Kuhn
et al., Science , 2008
206.
chemical–chemical links
207.
shared targets
208.
fingerprint similarity
209.
chemical–protein network
210.
211.
conclusions
212.
more data is
better
213.
quality scores
214.
benchmarking
215.
cross-species integration
216.
Part III putting
it all together
217.
Linding, Jensen, Ostheimer
et al., Cell , 2007
218.
NetworKIN
219.
220.
benchmarking
221.
Linding, Jensen, Ostheimer
et al., Cell , 2007
222.
2.5-fold better accuracy
223.
context is crucial
224.
localization
225.
Linding, Jensen, Ostheimer
et al., Cell , 2007
226.
DNA damage response
227.
Linding, Jensen, Ostheimer
et al., Cell , 2007
228.
Linding, Jensen, Ostheimer
et al., Cell , 2007
229.
small-scale validation
230.
ATM phosphorylates Rad50
231.
Linding, Jensen, Ostheimer
et al., Cell , 2007
232.
Cdk1 phosphorylates 53BP1
233.
Linding, Jensen, Ostheimer
et al., Cell , 2007
234.
high-throughput validation
235.
multiple reaction monitoring
236.
Linding, Jensen, Ostheimer
et al., Cell , 2007
237.
systematic validation
238.
kinase inhibitor matrix
239.
Fedorov et al.,
PNAS , 2007
240.
design optimal experiments
241.
integration with literature
242.
Reflect
243.
244.
245.
246.
conclusions
247.
complementary data
248.
visualization
249.
a good question
250.
251.
252.
Baixar agora