Ishanu Chattopadhyay PRO
ML | Data Science Biomedical Informatics | Social Science | Assistant Professor
Large Science Models:
A General Framework for Generative Modeling of Microbiome-Metabolome systems
Ishanu Chattopadhyay, PhD
Assistant Professor of Biomedical Informatics & Computer Science
University of Kentucky
What is a healthy Microbiome/ Metabolome?
Can gut microbiota pre-empt developmental deficits?
High Abundance Classes
Complex Interdependecies Exist over time and taxonomic strata
quantized output levels
Can microbial assay from gut actionably
pre-empt developmental deficit?
Study Set-up
Predicting neurodevelopmental deficits
Forecasting ecosystem trajectories
Build classifiers
Ability to "fill in" missing data is equivalent to making trajectory forecasts
Forecast ecosystem fluctuations
Forecasting of class abundance variations both
in-sample (\( R^2 \ge 95\%\))
and out-of sample (\(R^2\ge 72\%\))
Forecasting of class abundance variations both
in-sample (\( R^2 \ge 95\%\))
and out-of sample (\(R^2\ge 72\%\))
Building classifier based on LSM metric
moving the top drivers impact risk of deficit maximally
Understanding Key Drivers via LSM perturbations
No transplantation is guaranteed to work reliably
Predicted to reduce
risk reliably
Predicted to reduce
risk reliably
IBD Metabolomics: Preliminary Results
Dataset from Metabolomics Workbench
| Study ID | ST000923 |
|---|---|
| Study Title | Longitudinal Metabolomics of the Human Microbiome in Inflammatory Bowel Disease |
| Institute | Broad Institute of MIT and Harvard |
|---|---|
| Last Name | Avila-Pacheco |
| First Name | Julian |
| Submit Date | 2017-11-14 |
| Num Groups | 3 |
| Total Subjects | 546 |
| Num Males | 276 |
| Num Females | 270 |
| Analysis Type Detail | LC-MS |
State-of-art microbiome based Classification (~10 species) *
| IBD vs UC | 0.82 |
| IBD vs CD | 0.76 |
*Zheng, J., et al. (2024). Noninvasive, microbiome-based diagnosis of inflammatory bowel disease. Nature Medicine, 30(12), 3555–3567. https://doi.org/10.1038/s41591-024-03280-4
| IBD vs non IBD | 0.85 |
Gut-Metabolome based Classification (~36 metabolites) *
Application 2
Metabolomics
LSM model
- No. parameters: 70 million
- Out of sample n=150
- Uses all names and unnamed metabolites (>81K features)
| AUC (out of sample) | |
|---|---|
| Healthy vs IBD | 96.1% |
| Healthy vs UC | 92% |
| UC vs CD | 99% |
| Healthy vs CD | 99% |
Metabolomics
LSM model
- No. parameters: 70 million
- Out of sample n=150
- Uses all names and unnamed metabolites (>81K features)
| AUC (out of sample) | |
|---|---|
| Healthy vs IBD | 96.1% |
| Healthy vs UC | 92% |
| UC vs CD | 99% |
| Healthy vs CD | 99% |
Metabolomics
Insight: the discriminating hypersurface is 2d (almost 1d)
| Number of metabolites | 5,280 | 85% untargeted metabolites |
|---|---|---|
| Number of parameters | 4,002,306 | |
| Average Tree Depth | 38.62 | |
| Number of constraints inferred | 108,884 | ~83% involve untargeted metabolites |
| Number of samples used | 180 | (no clinical phenotype information used) |
-
Using all samples of metabolite profiles
-
Not using clinical phenotypes and replicates
Application 3
ASD Metabolics: Preliminary Results
R_{\mathcal A}(y)
:=
\|L(y)\|_2
=
\left(
\sum_{k=1}^K
\bigl[-\log \Pr(a_k\to y)\bigr]^2
\right)^{1/2}
ASD Metabolics: Preliminary Results
ASD sample profile
ASD samples from same patient
sample profile of new patient \(y\)
Pr(a \rightarrow y)
Need one patient!
Predictive Performance
| AUC | Sensitivity at 95% spec | |
|---|---|---|
| LSM | 92.7% | 74% |
| MCHAT/F | 67% | 39% |
| ADOS-2 | 90-97% | 85% |
getting close to the gold standard
1 false positive
1 false negative
10% flag in TBD (expected 8.3% positives)
Predictive Performance
Top risk drivers mapped to known pathways
P_x(m_j) = \frac{\partial R(x)}{\partial m_j}
Patient specific driver profile
\displaystyle P(m_j) = \frac{1}{N} \sum_x P_x(m_j)
Average driver profile
Top 30 risk drivers (targeted met.)
A New Paradigm of AI-driven Discovery in Metabolome Biology
Copy of LSM-metabolomics
By Ishanu Chattopadhyay
Copy of LSM-metabolomics
DARPA-EA-25-02-05-MAGICS-PA-025
