Proper application of machine learning to accurately evaluate the accuracy of biomarkers is challenging and error-prone for those without expertise in machine learning or programming. We offer an easy to use tool which implements the best practices and produces a comprehensive yet clinically-relevant report when comparing several biomarkers or different methods/studies. It is called neuropredict, which is open source and applicable to any domain whose biomarkers can be represented by numbers.
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neuropredict: a proposal and a tool towards standardized and easy assessment of biomarkers
1. neuropredict: a proposal and a
tool towards standardized and
easy assessment of biomarkers
github.com/raamana
Pradeep Reddy Raamana, PhD
crossinvalidation.com
2. What are biomarkers?
2
[1]. Strimbu, K., & Tavel, J. A. (2010). What are Biomarkers? Current Opinion in HIV and AIDS, 5(6), 463–466.
3. What are biomarkers?
• “The term “biomarker”, a portmanteau of
“biological marker”, refers to a broad
subcategory of medical signs – that is,
objective indications of medical state
observed from outside the patient – which can
be measured accurately and reproducibly. ”1
2
[1]. Strimbu, K., & Tavel, J. A. (2010). What are Biomarkers? Current Opinion in HIV and AIDS, 5(6), 463–466.
4. What are biomarkers?
• “The term “biomarker”, a portmanteau of
“biological marker”, refers to a broad
subcategory of medical signs – that is,
objective indications of medical state
observed from outside the patient – which can
be measured accurately and reproducibly. ”1
• simplified: “set of numbers predicting label(s)”
2
[1]. Strimbu, K., & Tavel, J. A. (2010). What are Biomarkers? Current Opinion in HIV and AIDS, 5(6), 463–466.
5. What are biomarkers?
• “The term “biomarker”, a portmanteau of
“biological marker”, refers to a broad
subcategory of medical signs – that is,
objective indications of medical state
observed from outside the patient – which can
be measured accurately and reproducibly. ”1
• simplified: “set of numbers predicting label(s)”
• biomarkers are essential for computer-aided
diagnosis: 1) detection of disease and staging
their severity, and 2) monitoring response to
treatment.
2
[1]. Strimbu, K., & Tavel, J. A. (2010). What are Biomarkers? Current Opinion in HIV and AIDS, 5(6), 463–466.
6. Measuring biomarkers accuracy
is hard and error-prone!
3
• As proper application of ML requires
• training in linear algebra and statistics
• training in programming and
engineering
• It only gets harder in biomarker domain:
• blind application is not enough
• interpretability/limitations are important
• Too many black-boxes and knobs -->
7. Measuring biomarkers accuracy
is hard and error-prone!
3
• As proper application of ML requires
• training in linear algebra and statistics
• training in programming and
engineering
• It only gets harder in biomarker domain:
• blind application is not enough
• interpretability/limitations are important
• Too many black-boxes and knobs -->
8. Typical ML/biomarker workflow
4
Raw data Preproce
ssing
Feature
extraction
Cross-
validation
(CV)
Analysis
of CV
results
Visualize
and
compare
9. Typical ML/biomarker workflow
4
Raw data Preproce
ssing
Feature
extraction
Cross-
validation
(CV)
Analysis
of CV
results
Visualize
and
compare
Tools exist to do many of the small tasks individually,
but not as a whole!
10. Typical ML/biomarker workflow
4
Raw data Preproce
ssing
Feature
extraction
Cross-
validation
(CV)
Analysis
of CV
results
Visualize
and
compare
Tools exist to do many of the small tasks individually,
but not as a whole!
To those without machine learning or
programming experience, this is incredibly hard.
11. Typical ML/biomarker workflow
4
Raw data Preproce
ssing
Feature
extraction
Cross-
validation
(CV)
Analysis
of CV
results
Visualize
and
compare
neuropredict covers
these parts
Tools exist to do many of the small tasks individually,
but not as a whole!
To those without machine learning or
programming experience, this is incredibly hard.
17. Billions of dollars and decades of research,
but not much insight into biomarkers!
6Woo, CW., et al.. (2017). Nature Neuroscience, 20(3), 365-377.
18. Standardized measurement
and reports are necessary!
• Research studies do not report all the
information necessary
• to assess biomarker performance
well, and
• to engage in statistical comparison
with previous studies/biomarkers
• Standardization of performance
measurement and reports is needed!
7
19. neuropredict is an attempt to
standardize and learn from each other!
8
This is NOT specific to neuroscience.
Ideas and tools are generic!
20. I have a plan
9
Consensus on
standards of
analysis
Consensus on
significance
tests!
Standardize
report format
Open
validation of
neuro-predict
Cloud repo
and web
portals
Release, test,
improve and
iterate!
but I need your support!
21. Come, join me!
let’s improve biomarker science.
one commit at a time!
10
github.com/raamana