The document discusses the integration of machine learning and metabolomic network analysis for personalized medicine, emphasizing the need for generalized data analysis methods to efficiently integrate various omic domains. It highlights the importance of data visualization, statistical analysis, and the complexities of modeling within biochemical contexts. Additionally, it notes that effective integration of analytical methods and domain knowledge is crucial for advancing biomarker discovery and understanding biochemical relationships.

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Machine learning powered
metabolomic network
analysis
Dmitry Grapov, PhD
www.createdatasol.com
DAVe

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Predictive Modeling Within a Biochemical Context
Grapov et. al., Circ. Cardiovasc. Genet. 2014
Personalized Medicine
Complex Data Integration
Grapov et. al.,PLoS ONE (2014) doi:10.1371/journal.pone.0084260
J. Proteome Res., 2015, 14 (1), pp 557–566 DOI: 10.1021/pr500782g
Biomarker Discovery

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‘The impact of metabolomics on systems
biology is still in its infancy… few people have
a solid understanding across all the 'omics,
including lipidomics and epigenetics.’
Present and Future
‘There is a need for a generalized data
analysis methods capable of efficiently
integrating across[and within] -Omic domains’

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Maximizing Metabolomic Coverage
American Journal of Physiology - Endocrinology and Metabolism 2015 Vol. no. , DOI: 10.1152/ajpendo.00019.2015

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http://www.archaeology.org/issues/207-
1603/features/4157-arles-roman-wall-paintings
Materials of Connected Biological
Data Analysis and Visualization
Quality Assessment
• use replicated measurements
and/or internal standards to
estimate analytical variance
Statistical and Multivariate
• use the experimental design
to test hypotheses and/or
identify trends in analytes
Functional
• use statistical and multivariate
results to identify impacted
biochemical domains
Network and Predictive
• integrate statistical and
multivariate results with the
experimental design and
analyte metadata

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Data Analysis Visualization engine

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Statistical Data Analysis
fold-change
significance
Simplest representation: two-class comparison
↑ diabetics
↓ diabetics
↑ diabetics ↓ diabetics

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Multivariate Data Analysis
meta data
data
(row, column)
Clustering: distance + linkage

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dimensional
reduction
Multivariate Data Analysis
projection similarity

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Considerations for Selecting a Modeling Method
Is it appropriate for my data?
• Missing values
• Data types e.g. numeric/discrete
• Multicollinearity
• Fitting properties e.g. non-linear
Training and test performance?
• Optimization
• Parameter tuning
• Feature selection
• Feature weights/rank
https://en.wikipedia.org/wiki/Anscombe%27s_quartet
https://en.wikipedia.org/wiki/Overfitting
Very important to avoid
overfitting
non-linear
classificationoutlier
linear
Visualization is critical for model evaluation

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Machine Learning Based Predictive Modeling
Auto or manual
parameter tuning
Optimize based on a variety
of performance metrics
Control model cross-
validation
Share results with
global state
Choose from over
200 model types
Calculate classification or
regression models

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Pathway Analysis
Analyses:
• visualization
• mapping
• enrichment
• topology
Pathway: expert definition of a biochemical module

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Structural similarity
Complex Metabolic Systems Biology
Biochemistry

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Encoding Biochemical Reactions
http://www.genome.jp/dbget-bin/www_bget?rn:R00703

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Biochemical Reactions as Networks

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Network Analysis and Mapping
Biochemical
•reaction
•domain
Structural
•molecular fingerprints
• mass spectra
Empirical
•correlation
•partial correlation
BMC Bioinformatics 2012, 13:99 doi:10.1186/1471-2105-13-99

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MappingsNetwork Mapped Network
Grapov D.,American Society of Mass Spectrometry Conference (2013, 2014)
Network Mapping
+ DAVe

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Network Mapping

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‘Metabolomics can’t really
function on its own without
the genome sequence.’
Present and Future

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Integrated Omic Signal State
http://dx.doi.org/10.1016/j.tibtech.2015.12.013

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Omic’ data integration strategies
Biomarker Insights 2015:Suppl. 4 1-6 DOI: 10.4137/BMI.S29511
Empirical
correlation
Network
based
Biochemical
pathway

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Machine Learning Biology
Computer Vision
Ranking Deep
Learning

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Biochemical Deep Learning
How can we achieve the scale
and encoding required for
metabolomic deep learning?

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Biochemical Deep Learning
• Unexpected repurposing of
established methods hint at
potential of neural networks as
universal data encoders
• ‘Black-box’ algorithms are less
useful for understanding causal
biochemical relationships
• High degree of difficulty of
implementation limits routine
adoption among biologists

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Summary
• Metabolomic analysis involves
integration of analytical methods,
biochemical domain knowledge
and experimental data
• Network analysis and mapping are
useful methods for diverse data
integration, analysis and
visualization
• Combination of machine learning
and network analysis provides a
flexible framework for data
analysis and visualization

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data visualization network analysis machine learning
predictive modeling biochemistry software
createdatasol@gmail.com
www.createdatasol.com