Cross-species data integration

•Download as PPT, PDF•

2 likes•229 views

Lars Juhl Jensen

Centre for Molecular Biology and Neuroscience, Rikshospitalet-Radiumhospitalet, Oslo, Norway, October 25, 2006

Cross-species data integration Lars Juhl Jensen EMBL Heidelberg

Lars Juhl Jensen

promoter analysis

Jensen et al., Bioinformatics, 2000

genome visualization

Pedersen et al., Journal of Molecular Biology, 2000

protein function prediction

data integration

Jensen et al., Drug Discovery Today: Targets, 2004

cell cycle

temporal interaction network

de Lichtenberg et al., Science, 2005

cross-species comparison

Jensen et al., Nature, 2006

STRING

373 proteomes

Genome Reviews

RefSeq

Ensembl

model organism databases

functional interactions

genomic context methods

gene neighborhood

gene fusion

phylogenetic profiles

Cell Cellulosomes Cellulose

correct interactions

wrong associations

gene neighborhood

sum of intergenic distances

gene fusion

sequence similarity

phylogenetic profiles

SVD Singular Value Decomposition

Euclidian distance

raw quality scores

not comparable

sum of intergenic distances

sequence similarity

Euclidian distance

benchmarking

calibrate vs. gold standard

raw quality scores

probabilistic scores

curated knowledge

KEGG Kyoto Encyclopedia of Genes and Genomes

Reactome

MIPS Munich Information center for Protein Sequences

STKE Signal Transduction Knowledge Environment

primary experimental data

many sources

many parsers

physical protein interactions

BIND Biomolecular Interaction Network Database

GRID General Repository for Interaction Datasets

MINT Molecular Interactions Database

DIP Database of Interacting Proteins

HPRD Human Protein Reference Database

merge data by publication

topology-based scores

von Mering et al., Nucleic Acids Research, 2005

co-expression

GEO Gene Expression Omnibus

correlation coefficient

literature mining

different gene identifiers

synonyms lists

M EDLINE

SGD Saccharomyces Genome Database

The Interactive Fly

OMIM Online Mendelian Inheritance in Man

co-mentioning

NLP Natural Language Processing

[object Object],[object Object],[object Object],[object Object],[object Object]

calibrate vs. gold standard

combine all evidence

spread over many species

transfer by orthology

von Mering et al., Nucleic Acids Research, 2005

two modes

orthologous groups

von Mering et al., Nucleic Acids Research, 2005

fuzzy orthology

von Mering et al., Nucleic Acids Research, 2005

Bayesian scoring scheme

Bork et al., Current Opinion in Structural Biology, 2005

predicting “mode of action”

Jensen et al., Drug Discovery Today: Targets, 2004

Jensen et al., Drug Discovery Today: Targets, 2004

NetworKIN

the idea

mass spectrometry

phosphorylation sites

in vivo

kinases are unknown

sequence motifs

kinase families

overprediction

in vitro

protein networks

STRING

context

in vivo

the algorithm

benchmarking

Phospho.ELM

ATM signaling

ATM phosphorylates Rad50

summary

integration

high-throughput data

computational methods

biological discoveries

hypotheses

highly specific

testable

Acknowledgments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

More Related Content

What's hot

The STRING database

The STRING database

The STRING databaseLars Juhl Jensen

Introduction to STRING

Introduction to STRING

Introduction to STRINGLars Juhl Jensen

Protein association networks with STRING

Protein association networks with STRING

Protein association networks with STRINGLars Juhl Jensen

STRING/STITCH tutorial

STRING/STITCH tutorial

STRING/STITCH tutorialbiocs

The STRING database - Quality scores for heterogeneous interaction data

The STRING database - Quality scores for heterogeneous interaction data

The STRING database - Quality scores for heterogeneous interaction dataLars Juhl Jensen

Data integration and functional association networks

Data integration and functional association networks

Data integration and functional association networksLars Juhl Jensen

Systems biology: Bioinformatics on complete biological systems

Systems biology: Bioinformatics on complete biological systems

Systems biology: Bioinformatics on complete biological systemsLars Juhl Jensen

Gene association networks - Large-scale integration of data and text

Gene association networks - Large-scale integration of data and text

Gene association networks - Large-scale integration of data and textLars Juhl Jensen

Network biology - Large-scale integration of data and text

Network biology - Large-scale integration of data and text

Network biology - Large-scale integration of data and textLars Juhl Jensen

Unraveling cellular phosphorylation networks using computational biology

Unraveling cellular phosphorylation networks using computational biology

Unraveling cellular phosphorylation networks using computational biologyLars Juhl Jensen

STRING: Large-scale data and text mining

STRING: Large-scale data and text mining

STRING: Large-scale data and text miningLars Juhl Jensen

Large-scale integration of data and text

Large-scale integration of data and text

Large-scale integration of data and textLars Juhl Jensen

Large-scale integration of data and text

Large-scale integration of data and text

Large-scale integration of data and textLars Juhl Jensen

Systems biology: Bioinformatics on complete biological system

Systems biology: Bioinformatics on complete biological system

Systems biology: Bioinformatics on complete biological systemLars Juhl Jensen

Network Biology: Large-scale integration of data and text

Network Biology: Large-scale integration of data and text

Network Biology: Large-scale integration of data and textLars Juhl Jensen

Systems biology - Bioinformatics on complete biological systems

Systems biology - Bioinformatics on complete biological systems

Systems biology - Bioinformatics on complete biological systemsLars Juhl Jensen

Mining heterogeneous data: Understanding systems at the level of complexes an...

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

Networks of proteins and diseases

Networks of proteins and diseases

Networks of proteins and diseasesLars Juhl Jensen

Gene association networks - Large-scale integration of data and text

Gene association networks - Large-scale integration of data and text

Gene association networks - Large-scale integration of data and textLars Juhl Jensen

Network integration of heterogeneous data

Network integration of heterogeneous data

Network integration of heterogeneous dataLars Juhl Jensen

What's hot (20)

The STRING database

The STRING database

The STRING database

Introduction to STRING

Introduction to STRING

Introduction to STRING

Protein association networks with STRING

Protein association networks with STRING

Protein association networks with STRING

STRING/STITCH tutorial

STRING/STITCH tutorial

STRING/STITCH tutorial

The STRING database - Quality scores for heterogeneous interaction data

The STRING database - Quality scores for heterogeneous interaction data

The STRING database - Quality scores for heterogeneous interaction data

Data integration and functional association networks

Data integration and functional association networks

Data integration and functional association networks

Systems biology: Bioinformatics on complete biological systems

Systems biology: Bioinformatics on complete biological systems

Systems biology: Bioinformatics on complete biological systems

Gene association networks - Large-scale integration of data and text

Gene association networks - Large-scale integration of data and text

Gene association networks - Large-scale integration of data and text

Network biology - Large-scale integration of data and text

Network biology - Large-scale integration of data and text

Network biology - Large-scale integration of data and text

Unraveling cellular phosphorylation networks using computational biology

Unraveling cellular phosphorylation networks using computational biology

Unraveling cellular phosphorylation networks using computational biology

STRING: Large-scale data and text mining

STRING: Large-scale data and text mining

STRING: Large-scale data and text mining

Large-scale integration of data and text

Large-scale integration of data and text

Large-scale integration of data and text

Large-scale integration of data and text

Large-scale integration of data and text

Large-scale integration of data and text

Systems biology: Bioinformatics on complete biological system

Systems biology: Bioinformatics on complete biological system

Systems biology: Bioinformatics on complete biological system

Network Biology: Large-scale integration of data and text

Network Biology: Large-scale integration of data and text

Network Biology: Large-scale integration of data and text

Systems biology - Bioinformatics on complete biological systems

Systems biology - Bioinformatics on complete biological systems

Systems biology - Bioinformatics on complete biological systems

Mining heterogeneous data: Understanding systems at the level of complexes an...

Mining heterogeneous data: Understanding systems at the level of complexes an...

Mining heterogeneous data: Understanding systems at the level of complexes an...

Networks of proteins and diseases

Networks of proteins and diseases

Networks of proteins and diseases

Gene association networks - Large-scale integration of data and text

Gene association networks - Large-scale integration of data and text

Gene association networks - Large-scale integration of data and text

Network integration of heterogeneous data

Network integration of heterogeneous data

Network integration of heterogeneous data

Viewers also liked

Reproductive Biotechnology

Reproductive Biotechnology

Reproductive BiotechnologySt.Xavier's College (Autonomous),Palayamkottai

Genetic basis and improvement of reproductive traits

Genetic basis and improvement of reproductive traits

Genetic basis and improvement of reproductive traitsILRI

Farm Animal Reproduction

Farm Animal Reproduction

Farm Animal ReproductionSUNY Ulster

Population Structure & Genetic Improvement in Livestock

Population Structure & Genetic Improvement in Livestock

Population Structure & Genetic Improvement in LivestockGolden Helix Inc

Powerpointbong sinalubong

MICROSATELITE Markers for LIVESTOCK Genetic DIVERSITY ANALYSES

MICROSATELITE Markers for LIVESTOCK Genetic DIVERSITY ANALYSES

MICROSATELITE Markers for LIVESTOCK Genetic DIVERSITY ANALYSESKaran Veer Singh

Reproductive technology

Reproductive technology

Reproductive technologyparrc

Genetics of animal breeding 9

Genetics of animal breeding 9

Genetics of animal breeding 9zerdon

Viewers also liked (8)

Reproductive Biotechnology

Reproductive Biotechnology

Reproductive Biotechnology

Genetic basis and improvement of reproductive traits

Genetic basis and improvement of reproductive traits

Genetic basis and improvement of reproductive traits

Farm Animal Reproduction

Farm Animal Reproduction

Farm Animal Reproduction

Population Structure & Genetic Improvement in Livestock

Population Structure & Genetic Improvement in Livestock

Population Structure & Genetic Improvement in Livestock

Powerpoint

MICROSATELITE Markers for LIVESTOCK Genetic DIVERSITY ANALYSES

MICROSATELITE Markers for LIVESTOCK Genetic DIVERSITY ANALYSES

MICROSATELITE Markers for LIVESTOCK Genetic DIVERSITY ANALYSES

Reproductive technology

Reproductive technology

Reproductive technology

Genetics of animal breeding 9

Genetics of animal breeding 9

Genetics of animal breeding 9

Similar to Cross-species data integration

Integration of diverse large-scale datasets

Integration of diverse large-scale datasets

Integration of diverse large-scale datasetsLars Juhl Jensen

Integration of heterogeneous data

Integration of heterogeneous data

Integration of heterogeneous dataLars Juhl Jensen

Using networks to derive function

Using networks to derive function

Using networks to derive functionLars Juhl Jensen

Prediction of protein networks through data integration

Prediction of protein networks through data integration

Prediction of protein networks through data integrationLars Juhl Jensen

Large-scale data and text mining

Large-scale data and text mining

Large-scale data and text miningLars Juhl Jensen

Data integration - Integration of functional associations using STRING

Data integration - Integration of functional associations using STRING

Data integration - Integration of functional associations using STRINGLars Juhl Jensen

Integration of heterogeneous data

Integration of heterogeneous data

Integration of heterogeneous dataLars Juhl Jensen

Large-scale integration of data and text

Large-scale integration of data and text

Large-scale integration of data and textLars Juhl Jensen

Advanced bioinformaticsof proteomics datasets

Advanced bioinformaticsof proteomics datasets

Advanced bioinformaticsof proteomics datasetsLars Juhl Jensen

Networks of proteins and diseases

Networks of proteins and diseases

Networks of proteins and diseasesLars Juhl Jensen

Large-scale integration of data and text

Large-scale integration of data and text

Large-scale integration of data and textLars Juhl Jensen

Functional association networks - The STRING and STITCH web resources

Functional association networks - The STRING and STITCH web resources

Functional association networks - The STRING and STITCH web resourcesLars Juhl Jensen

Exploring proteins, chemicals and their interactions with STRING and STITCH

Exploring proteins, chemicals and their interactions with STRING and STITCH

Exploring proteins, chemicals and their interactions with STRING and STITCHbiocs

Information integration

Information integration

Information integrationLars Juhl Jensen

Protein interaction networks

Protein interaction networks

Protein interaction networksLars Juhl Jensen

Large-scale biomedical data and text integration

Large-scale biomedical data and text integration

Large-scale biomedical data and text integrationLars Juhl Jensen

Data Integration and Systems Biology

Data Integration and Systems Biology

Data Integration and Systems BiologyLars Juhl Jensen

Unraveling signaling networks by data integration

Unraveling signaling networks by data integration

Unraveling signaling networks by data integrationLars Juhl Jensen

Protein interaction networks from yeast to human

Protein interaction networks from yeast to human

Protein interaction networks from yeast to humanLars Juhl Jensen

Network biology: Large-scale data and text mining

Network biology: Large-scale data and text mining

Network biology: Large-scale data and text miningLars Juhl Jensen

Similar to Cross-species data integration (20)

Integration of diverse large-scale datasets

Integration of diverse large-scale datasets

Integration of diverse large-scale datasets

Integration of heterogeneous data

Integration of heterogeneous data

Integration of heterogeneous data

Using networks to derive function

Using networks to derive function

Using networks to derive function

Prediction of protein networks through data integration

Prediction of protein networks through data integration

Prediction of protein networks through data integration

Large-scale data and text mining

Large-scale data and text mining

Large-scale data and text mining

Data integration - Integration of functional associations using STRING

Data integration - Integration of functional associations using STRING

Data integration - Integration of functional associations using STRING

Integration of heterogeneous data

Integration of heterogeneous data

Integration of heterogeneous data

Large-scale integration of data and text

Large-scale integration of data and text

Large-scale integration of data and text

Advanced bioinformaticsof proteomics datasets

Advanced bioinformaticsof proteomics datasets

Advanced bioinformaticsof proteomics datasets

Networks of proteins and diseases

Networks of proteins and diseases

Networks of proteins and diseases

Large-scale integration of data and text

Large-scale integration of data and text

Large-scale integration of data and text

Functional association networks - The STRING and STITCH web resources

Functional association networks - The STRING and STITCH web resources

Functional association networks - The STRING and STITCH web resources

Exploring proteins, chemicals and their interactions with STRING and STITCH

Exploring proteins, chemicals and their interactions with STRING and STITCH

Exploring proteins, chemicals and their interactions with STRING and STITCH

Information integration

Information integration

Information integration

Protein interaction networks

Protein interaction networks

Protein interaction networks

Large-scale biomedical data and text integration

Large-scale biomedical data and text integration

Large-scale biomedical data and text integration

Data Integration and Systems Biology

Data Integration and Systems Biology

Data Integration and Systems Biology

Unraveling signaling networks by data integration

Unraveling signaling networks by data integration

Unraveling signaling networks by data integration

Protein interaction networks from yeast to human

Protein interaction networks from yeast to human

Protein interaction networks from yeast to human

Network biology: Large-scale data and text mining

Network biology: Large-scale data and text mining

Network biology: Large-scale data and text mining

More from 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...

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

One tagger, many uses: Simple text-mining strategies for biomedicineLars Juhl Jensen

Extract 2.0: Text-mining-assisted interactive annotation

Extract 2.0: Text-mining-assisted interactive annotation

Extract 2.0: Text-mining-assisted interactive annotationLars Juhl Jensen

Network visualization: A crash course on using Cytoscape

Network visualization: A crash course on using Cytoscape

Network visualization: A crash course on using CytoscapeLars Juhl Jensen

STRING & STITCH: Network integration of heterogeneous data

STRING & STITCH: Network integration of heterogeneous data

STRING & STITCH: Network integration of heterogeneous dataLars Juhl Jensen

Biomedical text mining: Automatic processing of unstructured text

Biomedical text mining: Automatic processing of unstructured text

Biomedical text mining: Automatic processing of unstructured textLars 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...

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

Network Biology: A crash course on STRING and CytoscapeLars Juhl Jensen

Cellular networks

Cellular networks

Cellular networksLars Juhl Jensen

Cellular Network Biology: Large-scale integration of data and text

Cellular Network Biology: Large-scale integration of data and text

Cellular Network Biology: Large-scale integration of data and textLars 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...

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

STRING & related databases: Large-scale integration of heterogeneous dataLars Juhl Jensen

Tagger: Rapid dictionary-based named entity recognition

Tagger: Rapid dictionary-based named entity recognition

Tagger: Rapid dictionary-based named entity recognitionLars Juhl Jensen

Medical text mining: Linking diseases, drugs, and adverse reactions

Medical text mining: Linking diseases, drugs, and adverse reactions

Medical text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen

Network biology: Large-scale integration of data and text

Network biology: Large-scale integration of data and text

Network biology: Large-scale integration of data and textLars Juhl Jensen

Medical data and text mining: Linking diseases, drugs, and adverse reactions

Medical data and text mining: Linking diseases, drugs, and adverse reactions

Medical data and text mining: Linking diseases, drugs, and adverse reactionsLars Juhl Jensen

Cellular Network Biology

Cellular Network Biology

Cellular Network BiologyLars Juhl Jensen

Network biology: Large-scale integration of data and text

Network biology: Large-scale integration of data and text

Network biology: Large-scale integration of data and textLars Juhl Jensen

Biomarker bioinformatics: Network-based candidate prioritization

Biomarker bioinformatics: Network-based candidate prioritization

Biomarker bioinformatics: Network-based candidate prioritizationLars Juhl Jensen

The Art of Counting: Scoring and ranking co-occurrences in literature

The Art of Counting: Scoring and ranking co-occurrences in literature

The Art of Counting: Scoring and ranking co-occurrences in literatureLars Juhl Jensen

More from 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: 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

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

Extract 2.0: Text-mining-assisted interactive annotation

Network visualization: A crash course on using Cytoscape

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

STRING & STITCH: Network integration of heterogeneous data

Biomedical text mining: Automatic processing of unstructured text

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...

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

Network Biology: A crash course on STRING and Cytoscape

Cellular networks

Cellular networks

Cellular networks

Cellular Network Biology: Large-scale integration of data and text

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...

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

STRING & related databases: Large-scale integration of heterogeneous data

Tagger: Rapid dictionary-based named entity recognition

Tagger: Rapid dictionary-based named entity recognition

Tagger: Rapid dictionary-based named entity recognition

Medical text mining: Linking diseases, drugs, and adverse reactions

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

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

Medical data and text mining: Linking diseases, drugs, and adverse reactions

Cellular Network Biology

Cellular Network Biology

Cellular Network Biology

Network biology: Large-scale integration of data and text

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

Biomarker bioinformatics: Network-based candidate prioritization

The Art of Counting: Scoring and ranking co-occurrences in literature

The Art of Counting: Scoring and ranking co-occurrences in literature

The Art of Counting: Scoring and ranking co-occurrences in literature

Recently uploaded

Developer Data Modeling Mistakes: From Postgres to NoSQL

Developer Data Modeling Mistakes: From Postgres to NoSQL

Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB

AI as an Interface for Commercial Buildings

AI as an Interface for Commercial Buildings

AI as an Interface for Commercial BuildingsMemoori

Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service

Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service

Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service9953056974 Low Rate Call Girls In Saket, Delhi NCR

Commit 2024 - Secret Management made easy

Commit 2024 - Secret Management made easy

Commit 2024 - Secret Management made easyAlfredo García Lavilla

Nell’iperspazio con Rocket: il Framework Web di Rust!

Nell’iperspazio con Rocket: il Framework Web di Rust!

Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University

Powerpoint exploring the locations used in television show Time Clash

Powerpoint exploring the locations used in television show Time Clash

Powerpoint exploring the locations used in television show Time Clashcharlottematthew16

Connect Wave/ connectwave Pitch Deck Presentation

Connect Wave/ connectwave Pitch Deck Presentation

Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation

Are Multi-Cloud and Serverless Good or Bad?

Are Multi-Cloud and Serverless Good or Bad?

Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson

Scanning the Internet for External Cloud Exposures via SSL Certs

Scanning the Internet for External Cloud Exposures via SSL Certs

Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed

CloudStudio User manual (basic edition):

CloudStudio User manual (basic edition):

CloudStudio User manual (basic edition):comworks

Bun (KitWorks Team Study 노별마루 발표 2024.4.22)

Bun (KitWorks Team Study 노별마루 발표 2024.4.22)

Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Wonjun Hwang

Advanced Test Driven-Development @ php[tek] 2024

Advanced Test Driven-Development @ php[tek] 2024

Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren

"Debugging python applications inside k8s environment", Andrii Soldatenko

"Debugging python applications inside k8s environment", Andrii Soldatenko

"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays

"Federated learning: out of reach no matter how close",Oleksandr Lapshyn

"Federated learning: out of reach no matter how close",Oleksandr Lapshyn

"Federated learning: out of reach no matter how close",Oleksandr LapshynFwdays

Vertex AI Gemini Prompt Engineering Tips

Vertex AI Gemini Prompt Engineering Tips

Vertex AI Gemini Prompt Engineering TipsMiki Katsuragi

Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365

Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365

Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited

Dev Dives: Streamline document processing with UiPath Studio Web

Dev Dives: Streamline document processing with UiPath Studio Web

Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity

New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024

New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024

New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays

Understanding the Laravel MVC Architecture

Understanding the Laravel MVC Architecture

Understanding the Laravel MVC ArchitecturePixlogix Infotech

Recently uploaded (20)

Developer Data Modeling Mistakes: From Postgres to NoSQL

Developer Data Modeling Mistakes: From Postgres to NoSQL

Developer Data Modeling Mistakes: From Postgres to NoSQL

AI as an Interface for Commercial Buildings

AI as an Interface for Commercial Buildings

AI as an Interface for Commercial Buildings

Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service

Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service

Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service

Commit 2024 - Secret Management made easy

Commit 2024 - Secret Management made easy

Commit 2024 - Secret Management made easy

Nell’iperspazio con Rocket: il Framework Web di Rust!

Nell’iperspazio con Rocket: il Framework Web di Rust!

Nell’iperspazio con Rocket: il Framework Web di Rust!

Powerpoint exploring the locations used in television show Time Clash

Powerpoint exploring the locations used in television show Time Clash

Powerpoint exploring the locations used in television show Time Clash

Connect Wave/ connectwave Pitch Deck Presentation

Connect Wave/ connectwave Pitch Deck Presentation

Connect Wave/ connectwave Pitch Deck Presentation

Are Multi-Cloud and Serverless Good or Bad?

Are Multi-Cloud and Serverless Good or Bad?

Are Multi-Cloud and Serverless Good or Bad?

Scanning the Internet for External Cloud Exposures via SSL Certs

Scanning the Internet for External Cloud Exposures via SSL Certs

Scanning the Internet for External Cloud Exposures via SSL Certs

CloudStudio User manual (basic edition):

CloudStudio User manual (basic edition):

CloudStudio User manual (basic edition):

Bun (KitWorks Team Study 노별마루 발표 2024.4.22)

Bun (KitWorks Team Study 노별마루 발표 2024.4.22)

Bun (KitWorks Team Study 노별마루 발표 2024.4.22)

Advanced Test Driven-Development @ php[tek] 2024

Advanced Test Driven-Development @ php[tek] 2024

Advanced Test Driven-Development @ php[tek] 2024

"Debugging python applications inside k8s environment", Andrii Soldatenko

"Debugging python applications inside k8s environment", Andrii Soldatenko

"Debugging python applications inside k8s environment", Andrii Soldatenko

"Federated learning: out of reach no matter how close",Oleksandr Lapshyn

"Federated learning: out of reach no matter how close",Oleksandr Lapshyn

"Federated learning: out of reach no matter how close",Oleksandr Lapshyn

Vertex AI Gemini Prompt Engineering Tips

Vertex AI Gemini Prompt Engineering Tips

Vertex AI Gemini Prompt Engineering Tips

Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365

Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365

Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365

Dev Dives: Streamline document processing with UiPath Studio Web

Dev Dives: Streamline document processing with UiPath Studio Web

Dev Dives: Streamline document processing with UiPath Studio Web

New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024

New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024

New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack

Understanding the Laravel MVC Architecture

Understanding the Laravel MVC Architecture

Understanding the Laravel MVC Architecture

Cross-species data integration

1. Cross-species data integration Lars Juhl Jensen EMBL Heidelberg

2. Lars Juhl Jensen

3.

4.

5. promoter analysis

6. Jensen et al., Bioinformatics, 2000

7. genome visualization

8. Pedersen et al., Journal of Molecular Biology, 2000

9. protein function prediction

10.

11.

12.

13. data integration

14. Jensen et al., Drug Discovery Today: Targets, 2004

16. temporal interaction network

17. de Lichtenberg et al., Science, 2005

18. cross-species comparison

19. Jensen et al., Nature, 2006

21.

22. 373 proteomes

23. Genome Reviews

26. model organism databases

27. functional interactions

28. genomic context methods

29. gene neighborhood

30.

31. gene fusion

32.

33. phylogenetic profiles

34.

35.

36.

37.

38. Cell Cellulosomes Cellulose

39. correct interactions

40. wrong associations

41. gene neighborhood

42. sum of intergenic distances

43.

44. gene fusion

45. sequence similarity

46.

47. phylogenetic profiles

48. SVD Singular Value Decomposition

49. Euclidian distance

50.

51. raw quality scores

52. not comparable

53. sum of intergenic distances

54. sequence similarity

55. Euclidian distance

56. benchmarking

57. calibrate vs. gold standard

58.

59. raw quality scores

60. probabilistic scores

61. curated knowledge

62. KEGG Kyoto Encyclopedia of Genes and Genomes

64. MIPS Munich Information center for Protein Sequences

65. STKE Signal Transduction Knowledge Environment

66. primary experimental data

67. many sources

68. many parsers

69. physical protein interactions

70. BIND Biomolecular Interaction Network Database

71. GRID General Repository for Interaction Datasets

72. MINT Molecular Interactions Database

73. DIP Database of Interacting Proteins

74. HPRD Human Protein Reference Database

75. merge data by publication

76. topology-based scores

77. von Mering et al., Nucleic Acids Research, 2005

78. co-expression

79. GEO Gene Expression Omnibus

80. correlation coefficient

81. literature mining

82. different gene identifiers

83. synonyms lists

85. SGD Saccharomyces Genome Database

86. The Interactive Fly

87. OMIM Online Mendelian Inheritance in Man

88. co-mentioning

89. NLP Natural Language Processing

90.

91. calibrate vs. gold standard

92.

93. combine all evidence

94. spread over many species

95. transfer by orthology

96. von Mering et al., Nucleic Acids Research, 2005

98.

99. orthologous groups

100. von Mering et al., Nucleic Acids Research, 2005

101.

102. fuzzy orthology

103. von Mering et al., Nucleic Acids Research, 2005

104. Bayesian scoring scheme

105. Bork et al., Current Opinion in Structural Biology, 2005

106. predicting “mode of action”

107. Jensen et al., Drug Discovery Today: Targets, 2004

108. Jensen et al., Drug Discovery Today: Targets, 2004

111. mass spectrometry

112.

113. phosphorylation sites

115. kinases are unknown

116. sequence motifs

117.

118. kinase families

119. overprediction

121. protein networks

123.

126. the algorithm

127.

128. benchmarking

129. Phospho.ELM

130.

131.

132. ATM signaling

133.

134. ATM phosphorylates Rad50

135.

137. integration

138. high-throughput data

139. computational methods

140. biological discoveries

141. hypotheses

142. highly specific

144.