This document discusses knowledge management for integrative omics data analysis. It describes Biomax, a knowledge management platform that can flexibly interconnect isolated data silos in biomedical research. The platform addresses challenges like aggregating and analyzing multi-scale omics data from various sources and representing biological knowledge through semantic mapping and ontologies. Examples demonstrate how Biomax can integrate data from literature and databases, develop domain models, perform statistical analyses and network searches on integrated data, and support collaborative knowledge extraction.

1. Knowledge Management for
Integrative Omics Data Analysis
Barcelona 15.02.13
Dr. Hilmar Ilgenfritz
Biomax Informatics AG
www.biomax.com

2. Biomax – Unique disruptive technology
Biomax Profile Biomax Vision
Headquartered near Munich Master scientific complexity
Germany
Reduce cost and time
In business for more than 15 Ensure ease of use
years
Increase speed of development
World wide customer base
BioXM is a configurable
Enable centers of excellence for knowledge management platform
personalized medicine to flexibly interconnect isolated
Support for Systems Biology silos of information in biomedical
research

3. Generate scientific impact from knowledge
exploitation
Challenge
Actionable to bridge
knowledge for the gap
rational decision
making
Insight with
Aggregate scientific
impact
Collect

4. ... e.g. expression data in a pathway context

5. Why „Knowledge Management“?
Knowledge: “the realisation and
understanding of patterns and
their implications existing in
information”
Need to mine information for
patterns
A pattern often only emerges
when information from different
silos is combined
e.g. Expression with gene
function, SNPs with clinical
history of patients, ...
Need semantically integrated
information
e.g. Information about identical
or “equivalent” objects and
“meaning” requires
framework for integration
methods to find “equivalent”
“meaning”

6. Knowledge Management aspects
• Data integration
• Semantic mapping
• Knowledge representation
• Data analysis
• Knowledge extraction
• Collaboration and project management

7. Public knowledge integration

8. Knowledge aspects in Systems
Medicine
Ontologies/SOPs/Study Design
Clinical
Biobank Data
eCRF/ Experimental
EHR Data
Public
Literature KM Molecular Data
Data
Data integration

9. Multi-Scale Modelling
Organs
Cells
Individuals
Tissues
requires “language” for formal, structured description
>> semantic mapping

10. Semantic mapping
Mapping entities or descriptions e.g. genes, phenotypes
cancer - blastoma, model parameters etc. with “equivalent
meaning” from different sources using controlled, structured
vocabularies
drop dead
drd, Q8IR42,
AAD52607

11. Traditional semantic mapping
KEGG
PubMed Gene Ontology UniProt

12. Working with semantic networks
• Connected data, meta-
data and knowledge
• Query, view, report
• Integrate with analysis

13. Machine is configured to
deliver relevant actionable
BioXM knowledge through apps
Machine is configured to build
the connections to information
Knowledge and data, based on the
Model knowledge model
Any type of data
Any format of data
Any volume of data
Any location of data
Any size of data
Documents Spread sheets

14. Apps Common users
Knowledge
Model
Power user
Information and
Data Access
Administrator

15. Concept - Agile Solution Building
Step 1:
Specification
• Designing the
data model
Query the knowledge network, explore Define the domain-specific data
the graph and report query results model
Step 3: Use Step 2:
• Query building Implementation
and information • Importing Instantiate the
retrieval information knowledge network with
data and information
from external resources

16. Solution deployment
Step 4
Web Apps for
Information
Retrieval,
Reporting and
Annotation

17. Sketch a domain model describing
the semantic network

18. Setting up the data model - graphical support
for modeling semantic networks
Example:
sub-network defining
a study design

19. Import data into semantic structure

20. Knowledge Network Representation
Dynamic network representation in BioXM
Each node or edge of the network may serve
as entry point for further exploration!

21. Knowledge Network Expansion
Dynamic network representation in BioXM

22. On-the-fly queries retrieve
context-specific sub-network
Experimental Knowledge
data
Molecular
data
Q
u
e
r
y
e.g. Patient overview

23. Natural language query wizard

24. Explore network

25. Application areas
Clinical Research Biobanking
Pathway Analysis Literature Mining

26. Application areas
NextGenSequencing Comparative Genomics
Urban/rural ozone levels (annual average)
Systems Biology Environmental Sciences

27. Knowledge Management aspects
• Data integration
• Semantic mapping
• Knowledge representation
• Data analysis
• Knowledge extraction
• Collaboration and project management

28. Connecting different sources
Example:
Literature derived
tissue specific FAS
pathway + CTD

29. Connecting different sources
Example:
Literature derived
tissue specific FAS
pathway + CTD

30. Validation of interconnection CASP10 -
Tetrachlorethene, evidence from CTD
3472561

31. Another example

32. Knowledge Management aspects
• Data integration
• Semantic mapping
• Knowledge representation
• Data analysis
• Knowledge extraction
• Collaboration and project management

33. Multi-Scale Modelling

34. Structuring domain knowledge

35. Formalising domain knowledge

36. Extending domain knowledge

37. Navigate the network
(find associated pathways)

38. Enter neighboring knowledge domain
(Toll-like receptor signalling pathway)

39. Collect all information about IL6

40. Knowledge Management aspects
• Data integration
• Semantic mapping
• Knowledge representation
• Data analysis
 Multi-variate data analysis
• Knowledge extraction
• Collaboration and project management

41. Complexity of chronic diseases
Socio- Lifestyle-environment
economic Risk and protective factors
determinants Tobacco smoking, pollutants, allergens,
nutrition, infections, physical exercise,
others
Gender Genes, Cells, Tissues, Organs
Biological expression of chronic diseases
Transcripts, proteins, metabolites, Target organ
local inflammation, Systemic inflammation Age
Cell and tissue remodeling
Clinical expression of chronic diseases
Co-morbidities, Severity of co-morbidities,
Persistence remission, Long-term morbidity,
Responsiveness - side effects to treatment

42. The unmet need – transform
data into insight
Insight in aggregated clinical data
for patient stratification
in chronic disease
• Up to 6,000 parameters per patient
• 5 years of patient history
Disperse clinical records

43. Oversight and insight over
the clinical processes

44. Patient map – parameters
for outcome prediction
 Specific outcome group accumulates in certain areas of the map

45. Patient group differentiating
outcome profile
 Out of almost XXX differentiating attributes at high confidence X
attributes constitute a robust predictor

46. Reliable outcome prediction
validation
 Patients with outcome risk can be predicted with high reliability.

47. Knowledge Management aspects
• Data integration
• Semantic mapping
• Knowledge representation
• Data analysis
 Integrative analysis
• Knowledge extraction
• Collaboration and project management

48. COPD ROS hypothesis
Muscle wasting in COPD patients is effect of systemic inflammation
resulting in nitroso-redox imbalance by mitochondria respiratory chain
uncoupling in COPD patients with low body mass index

49. Metabolism/ROS-production ODE model linked with
clinical data (Selivanov, Cascante, Barcelona)
Biophysical J. 92, 3492-3500
Glycolysis J. Theor. Biol. 252, 402-410
Bioinformatics 22, 2806-2812 Clinical Data connection
NAD Glc BMC Neuroscience, 7,(Suppl 1):S7
Mitochondria
O2 uptake
Exhalates
TCA cycle Cit from Biobridge
NADH Pyr AcCoA
NAD
OAA NADH Succ
ADP
NAD Lac Omics in
O Blood
RESPIRATION 2 transportc
La ROS,
ATP
glutathione
etc…
CrP antioxidant system
Clinical Data connection
ROS
cell damage
”OMICS” in muscle biopsies from Biobridge
(nitroso-redox balance, proteomics, genomics, signalling
Inflamation markers …)

50. Probabilistic network connecting inflammation and
metabolism baseds on omics data
(Turan, Falciani, Birmingham)
PLoS Comput Biol. 7 e1002129

51. Extending the deterministic model
Glycolysis
NAD Glc
Clinical ADP Resulting connecting network
data mechanic Myofibrils Glycolysis
work
ATP TCA cycle Cit NAD Glc
NADH Pyr AcCoA
NAD ADP
OAA NADH Succ mechanic
work
ADP ATP
O2 NAD Lac NADH Pyr
transport Electron chain CrP
ATP
diffusion
CrP ROS NAD Lac
Deterministic models
COPD knowledge base ATP
Data clinical/ CrP
experimental
Selection of hubs
Oxidative
phosphorylation
TCA COPD KB
Cycle
network
Glycolysis search
Probabilistic network Physiological
measurments

52. Integrative prediction models
ROS model
gas exchange
O IO O lung heterogeneities I
I
O IO O I
I
I I
O O IO
KM KM
I I KM
Simulation environment clinical data
BioBridge
PAC-COPD
ECLIPSE

53. Cross-study parameter matching

54. Semantic description of parameters

55. Mapping of model parameters
Context:Parameter Description:Instance_B
Ontology:A:54645 Element:Parameter:Instance_B
Ontology:A:5461
Ontology:B:987723
Ontology:C:21365
Ontology:A:54632
Element:Compound:Oxygen
Element:Model Parameter:Instance_A
Context:Model Parameter Description:Instance_A

56. Development of new probabilistic
Glycolysis
NAD Glc
network from COPD KB
hetero ADP
genic mechanic
Myofibrils
work
ATP TCA cycle Cit
NADH Pyr AcCoA Probabilistic
NAD (predictive)
OAA NADH Succ network
ADP
NAD Lac
CrP Electron chain 0.1 0.3
ATP 0.4 0.6
O2
diffusion
transport 0.8
CrP ROS 0.5 0.7
Deterministic models COPD knowledge base 0.2
0.4 0.1
Data clinical/
experimental
Selection of hubs
Oxidative
phosphorylation
TCA
Cycle Resulting connecting network
Glycolysis
COPD KB
Physiological
Correlation network measurments
network
search

57. Hidden variable prediction
ROS model
gas exchange
O I O O lung heterogeneities I
I
O I O O I
I
I I
O O I O
KM KM KM
I I
Simulation environment clinical data
BioBridge
PAC-COPD
ECLIPSE
Probabilistic model
0.1 0.3
0.4 0.6
0.8
0.5 0.7
0.2
0.4 0.1

58. Knowledge Management aspects
• Data integration
• Semantic mapping
• Knowledge representation
• Data analysis
 Statistics examples
• Knowledge extraction
• Collaboration and project management

59. Principal Component Analysis of Functional Modules
(activity of tissue remodelling pathways is altered in COPD patients)

60. Overrepresentation Analysis

61. Differential expression analysis

62. Cluster analysis
n = 92 n = 200

63. Knowledge Management aspects
• Data integration
• Semantic mapping
• Knowledge representation
• Data analysis
 Network search
• Knowledge extraction
• Collaboration and project management

64. Network analysis configuration

65. Network Search Results
Resulting connectingonnecting network
between two sources objects

66. Knowledge Management aspects
• Data integration
• Semantic mapping
• Knowledge representation
• Data analysis
• Knowledge extraction
• Collaboration and project management

67. Systematic literature review

68. Review results: Web Input form

69. Searches supporting the review flow

70. Results Stage 3: "include"

71. Thank you !