Network biology: Large-scale data and text mining

•Download as PPT, PDF•

1 like•656 views

This document discusses network biology and large-scale data and text mining. It describes how Lars Jensen uses computational predictions from over 1100 genomes along with experimental data and information extracted from text to build protein-protein association networks in STRING. These networks integrate known and predicted protein-protein interactions with functional associations, and are used to study biological systems at the network level.

Network biology
Large-scale data and
text mining
Lars Juhl Jensen

Korbel et al., Nature Biotechnology, 2004

Letunic & Bork, Trends in Biochemical Sciences, 2008

von Mering et al., Nucleic Acids Research, 2005

Franceschini et al., Nucleic Acids Research, 2013

Gene and protein names
Cue words for entity
recognition
Verbs for relation extraction
[nxexpr The expression of
[nxgene the cytochrome
genes
[nxpg CYC1 and CYC7]]]
is controlled by
[nxpg HAP1]

de Lichtenberg, Jensen et al., Science, 2005

Acknowledgments
STRING and STITCH
Michael Kuhn
Damian Szklarczyk
Andrea Franceschini
Milan Simonovic
Alexander Roth
Sune Pletscher-
Frankild
Jianyi Lin
Pablo Minguez
Christian von Mering
Peer Bork
Localization and disease
Sune Pletscher-Frankild
Alberto Santos
Janos Binder
Kalliopi Tsafou
Christian Stolte
Albert Palleja
Heiko Horn
Evangelos Pafilis
Reinhardt Schneider
Sean O’ Donoghue

What's hot

The STRING database

Lars Juhl Jensen

Network biology - Large-scale integration of data and text

Lars Juhl Jensen

STRING - Large-scale integration of data and text

Lars Juhl Jensen

Large-scale integration of data and text

Lars Juhl Jensen

STRING: Large-scale data and text mining

Lars Juhl Jensen

The STRING database - Quality scores for heterogeneous interaction data

Lars Juhl Jensen

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

Lars Juhl Jensen

Making gene networks through data integrationLars Juhl Jensen

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

Lars Juhl Jensen

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

Networks of proteins and diseasesLars Juhl Jensen

Network Biology: Large-scale integration of data and text

Lars Juhl Jensen

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

Lars Juhl Jensen

STRING & STITCH: Network integration of heterogeneous data

Lars Juhl Jensen

Large-scale integration of data and text

Lars Juhl Jensen

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

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

Lars Juhl Jensen

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

Lars Juhl Jensen

One tagger, many uses - Illustrating the power of ontologies in named entity ...

Lars Juhl Jensen

STRING: Protein networks from data and text mining

Lars Juhl Jensen

What's hot (20)

The STRING database

Network biology - Large-scale integration of data and text

STRING - Large-scale integration of data and text

Large-scale integration of data and text

STRING: Large-scale data and text mining

The STRING database - Quality scores for heterogeneous interaction data

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

Making gene networks through data integration

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

Network biology: Large-scale data and text mining

Networks of proteins and diseases

Network Biology: Large-scale integration of data and text

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

STRING & STITCH: Network integration of heterogeneous data

Large-scale integration of data and text

Network biology: Large-scale data integration and text mining

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

One tagger, many uses - Illustrating the power of ontologies in named entity ...

STRING: Protein networks from data and text mining

Similar to Network biology: Large-scale data and text mining

Networks of proteins and diseasesLars Juhl Jensen

Large-scale data and text miningLars Juhl Jensen

Large-scale integration of data and textLars Juhl Jensen

Protein association networks: Large-scale integration of data and text

Lars Juhl Jensen

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

Systems biology: Bioinformatics on complete biological systemLars Juhl Jensen

Systems biology: Bioinformatics on complete biological systems

Lars Juhl Jensen

Network biologyLars Juhl Jensen

Integration of heterogeneous data

Lars Juhl Jensen

Large-scale integration of data and textLars Juhl Jensen

Systems biology - Bioinformatics on complete biological systemsLars Juhl Jensen

Data integration with STRINGLars Juhl Jensen

Network Biology: A crash course on STRING and Cytoscape

Lars Juhl Jensen

Advanced bioinformaticsof proteomics datasets

Lars Juhl Jensen

STRING - Modeling of biological systems through cross-species data integ...

Lars Juhl Jensen

In silico and Text-Based Analysis of Cellular Networks

Lars Juhl Jensen

Systems biology - Understanding biology at the systems levelLars Juhl Jensen

Cellular Network Biology

Lars Juhl Jensen

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

Lars Juhl Jensen

Cross-species data integration

Lars Juhl Jensen

Similar to Network biology: Large-scale data and text mining (20)

Networks of proteins and diseases

Large-scale data and text mining

Large-scale integration of data and text

Protein association networks: Large-scale integration of data and text

Network biology: Large-scale data integration and text mining

Systems biology: Bioinformatics on complete biological system

Systems biology: Bioinformatics on complete biological systems

Network biology

Integration of heterogeneous data

Large-scale integration of data and text

Systems biology - Bioinformatics on complete biological systems

Data integration with STRING

Network Biology: A crash course on STRING and Cytoscape

Advanced bioinformaticsof proteomics datasets

STRING - Modeling of biological systems through cross-species data integ...

In silico and Text-Based Analysis of Cellular Networks

Systems biology - Understanding biology at the systems level

Cellular Network Biology

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

Cross-species data integration

Recently uploaded

FAIR & AI Ready KGs for Explainable Predictions

Michel Dumontier

The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed. In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine. Bio Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.

Multi-source connectivity as the driver of solar wind variability in the heli...

Sérgio Sacani

The ambient solar wind that flls the heliosphere originates from multiple sources in the solar corona and is highly structured. It is often described as high-speed, relatively homogeneous, plasma streams from coronal holes and slow-speed, highly variable, streams whose source regions are under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify solar wind sources and understand what drives the complexity seen in the heliosphere. By combining magnetic feld modelling and spectroscopic techniques with high-resolution observations and measurements, we show that the solar wind variability detected in situ by Solar Orbiter in March 2022 is driven by spatio-temporal changes in the magnetic connectivity to multiple sources in the solar atmosphere. The magnetic feld footpoints connected to the spacecraft moved from the boundaries of a coronal hole to one active region (12961) and then across to another region (12957). This is refected in the in situ measurements, which show the transition from fast to highly Alfvénic then to slow solar wind that is disrupted by the arrival of a coronal mass ejection. Our results describe solar wind variability at 0.5 au but are applicable to near-Earth observatories.

platelets_clotting_biogenesis.clot retractionpptx

muralinath2

general properties of oerganologametal.ppt

IqrimaNabilatulhusni

Citrus Greening Disease and its Management

subedisuryaofficial

Structures and textures of metamorphic rocks

kumarmathi863

Unveiling the Energy Potential of Marshmallow Deposits.pdf

Erdal Coalmaker

Large scale production of streptomycin.pptx

Cherry

insect morphology and physiology of insect

anitaento25

ESR_factors_affect-clinic significance-Pathysiology.pptx

muralinath2

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

muralinath2

extra-chromosomal-inheritance[1].pptx.pdfpdf

DiyaBiswas10

Slide 1: Title Slide Extrachromosomal Inheritance Slide 2: Introduction to Extrachromosomal Inheritance Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus. Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids. Slide 3: Mitochondrial Inheritance Mitochondria: Organelles responsible for energy production. Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria. Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring. Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy. Slide 4: Chloroplast Inheritance Chloroplasts: Organelles responsible for photosynthesis in plants. Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts. Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species. Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA. Slide 5: Plasmid Inheritance Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes. Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation. Significance: Important in biotechnology for gene cloning and genetic engineering. Slide 6: Mechanisms of Extrachromosomal Inheritance Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance. Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells. Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression. Slide 7: Examples of Extrachromosomal Inheritance Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells. Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration. Slide 8: Importance of Extrachromosomal Inheritance Evolution: Provides insight into the evolution of eukaryotic cells. Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases. Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification. Slide 9: Recent Research and Advances Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA. Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases. Slide 10: Conclusion Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology. Future Directions: Continued research and technological advancements hold promise for new treatments and applications. Slide 11: Questions and Discussion Invite Audience: Open the floor for any questions or further discussion on the topic.

justice-and-fairness-ethics with example

azzyixes

Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...

Sérgio Sacani

Since volcanic activity was first discovered on Io from Voyager images in 1979, changes on Io’s surface have been monitored from both spacecraft and ground-based telescopes. Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images show that a plume deposit from a powerful eruption at Pillan Patera has covered part of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive optics at visible wavelengths.

plant biotechnology Lecture note ppt.pptx

yusufzako14

Hemostasis_importance& clinical significance.pptx

muralinath2

Nutraceutical market, scope and growth: Herbal drug technology

Lokesh Patil

As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.

EY - Supply Chain Services 2018_template.pptx

AlguinaldoKong

Penicillin...........................pptx

Cherry

Lateral Ventricles.pdf very easy good diagrams comprehensive

silvermistyshot

Recently uploaded (20)

FAIR & AI Ready KGs for Explainable Predictions

Multi-source connectivity as the driver of solar wind variability in the heli...

platelets_clotting_biogenesis.clot retractionpptx

general properties of oerganologametal.ppt

Citrus Greening Disease and its Management

Structures and textures of metamorphic rocks

Unveiling the Energy Potential of Marshmallow Deposits.pdf

Large scale production of streptomycin.pptx

insect morphology and physiology of insect

ESR_factors_affect-clinic significance-Pathysiology.pptx

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

extra-chromosomal-inheritance[1].pptx.pdfpdf

justice-and-fairness-ethics with example

Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...

plant biotechnology Lecture note ppt.pptx

Hemostasis_importance& clinical significance.pptx

Nutraceutical market, scope and growth: Herbal drug technology

EY - Supply Chain Services 2018_template.pptx

Penicillin...........................pptx

Lateral Ventricles.pdf very easy good diagrams comprehensive

Network biology: Large-scale data and text mining

1. Network biology Large-scale data and text mining Lars Juhl Jensen

2. association networks

3. guilt by association

5. biological systems

6. protein networks

7. STRING

8. 1100+ genomes

9. computational predictions

10. gene fusion

11. Korbel et al., Nature Biotechnology, 2004

12. gene neighborhood

13. Korbel et al., Nature Biotechnology, 2004

14. phylogenetic profiles

15. Korbel et al., Nature Biotechnology, 2004

16. experimental data

17. gene coexpression

18.

19. protein interactions

20. Jensen & Bork, Science, 2008

21. curated knowledge

22. complexes

23. pathways

24. Letunic & Bork, Trends in Biochemical Sciences, 2008

25. many databases

26. different formats

27. different identifiers

28. variable quality

29. not comparable

30. not same species

31. hard work

32. (Ph.D. students)

33. common identifiers

34. quality scores

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

36. score calibration

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

38. homology-based transfer

39. Franceschini et al., Nucleic Acids Research, 2013

40. missing most of the data

41. text mining

42. >10 km

43. too much to read

44. computer

45. as smart as a dog

46. teach it specific tricks

47.

48.

49. named entity recognition

50. comprehensive lexicon

51. CDC2

52. cyclin dependent kinase 1

53. expansion rules

54. hCdc2

55. CDC2

56. flexible matching

57. cyclin-dependent kinase 1

58. cyclin dependent kinase 1

59. “black list”

60. SDS

61. co-mentioning

62. counting

63. within documents

64. within paragraphs

65. within sentences

66. natural language processing

67. Gene and protein names Cue words for entity recognition Verbs for relation extraction [nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1]

68. text corpus

69. ~2 million full-text articles

70. ~22 million abstracts

71. wait there’s more

72. general approach

73. curated knowledge

74. experimental data

75. text mining

76. computational predictions

77. common identifiers

78. quality scores

79. score calibration

80. visualization

81. protein networks

82. string-db.org

83. chemical networks

84. stitch-db.org

85. subcellular localization

86. compartments.jensenlab.org

87. tissue expression

88. tissues.jensenlab.org

89. temporal expression

90. de Lichtenberg, Jensen et al., Science, 2005

91. disease associations

92. medical informatics

93. electronic health records

94. drugs

95. side effects

96. biodiversity informatics

97. British Heritage Library

98. organisms

99. environments

100. Acknowledgments STRING and STITCH Michael Kuhn Damian Szklarczyk Andrea Franceschini Milan Simonovic Alexander Roth Sune Pletscher- Frankild Jianyi Lin Pablo Minguez Christian von Mering Peer Bork Localization and disease Sune Pletscher-Frankild Alberto Santos Janos Binder Kalliopi Tsafou Christian Stolte Albert Palleja Heiko Horn Evangelos Pafilis Reinhardt Schneider Sean O’ Donoghue

Network biology: Large-scale data and text mining

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Network biology: Large-scale data and text mining

Similar to Network biology: Large-scale data and text mining (20)

More from Lars Juhl Jensen

More from Lars Juhl Jensen (20)

Recently uploaded

Recently uploaded (20)

Network biology: Large-scale data and text mining