Investigators
Chuck Alan Dorval, Mingchen Gao, Kathleen Lamkin-Kennard, Daniel DiLorenzo, Guillaume de Lartigue, Jennifer Blain Christen
Contact info (email)
mgao8@buffalo.edu
1. Define context(s)
identify/explore new therapies
reveal new biological insights
Current Conformance Level / Target Conformance Level
Extensive
Primary goal of the model/tool/database
The primary goal of the model is to use machine learning to classify the subjects into subcategories of the IBD conditions based on their multi-omics data. The machine learning model will incorporate multi-layer, convolutional, and recurrent neural networks for the classification, and also will use feature selection to discover underlying features that could be used as biomarkers. The selected biomarkers will be used in the following task of mechanistic modeling.
Biological domain of the model
Colon
Structure(s) of interest in the model
Gut microbial scosystem
Spatial scales included in the model
N/A
Time scales included in the model
N/A
2. Data for building and validating the model
| Data for building the model | Published? | Private? | How is credibility checked? | Current Conformance Level / Target Conformance Level |
|---|---|---|---|---|
| in vitro (primary cells cell, lines, etc.) | ||||
| ex vivo (excised tissues) | ||||
| in vivo pre-clinical (lower-level organism or small animal) | ||||
| in vivo pre-clinical (large animal) | ||||
| Human subjects/clinical | Yes | peer-reviewed publication | Adequate | |
| Other: ________________________ |
| Data for validating the model | Published? | Private? | How is credibility checked? | Current Conformance Level / Target Conformance Level |
|---|---|---|---|---|
| in vitro (primary cells cell, lines, etc.) | ||||
| ex vivo (excised tissues) | ||||
| in vivo pre-clinical (lower-level organism or small animal) | ||||
| in vivo pre-clinical (large animal) | ||||
| Human subjects/clinical | Yes | The validation data was part of the taining data | Adequate | |
| Other: ________________________ |
3. Validate within context(s)
| Who does it? | When does it happen? | How is it done? | Current Conformance Level / Target Conformance Level | |
|---|---|---|---|---|
| Verification | Software developers | throughout development of the software with an independent review prior to release | Software engineering testing for each model and global testing to ensure code correctness | Extensive |
| Validation | Developers and experiment users | throughout development and experimental validation | through experimentation, testing hypothesis on three independent sites | Extensive |
| Uncertainty quantification | The classification model will have uncertainty quantification | throughout development and experimental validation | Classification model has build-in uncertainty prediction | Adequate |
| Sensitivity analysis | Post-processing needed to analyze sensitivity | after model implementation and validation | Input perturbation will be added to analyze the input sensitivity | Adequate |
| Other:__________ | ||||
| Additional Comments |
4. Limitations
| Disclaimer statement (explain key limitations) | Who needs to know about this disclaimer? | How is this disclaimer shared with that audience? | Current Conformance Level / Target Conformance Level |
|---|---|---|---|
| The model's performance will deteriorate with less training data. | All users | o2s2parc platform, all software distribution | Extensive |
5. Version control
| Current Conformance Level / Target Conformance Level |
|---|
| Extensive |
| Naming Conventions? | Repository? | Code Review? | |
|---|---|---|---|
| individual modeler | Yes | o2s2parc platform, github | Yes |
| within the lab | Yes | o2s2parc platform, github | Yes |
| collaborators | Yes | o2s2parc platform, github | Yes |
6. Documentation
| Current Conformance Level / Target Conformance Level | |
|---|---|
| Code commented? | Extensive |
| Scope and intended use described? | Adequate, in both o2s2parc documentation and supporting material in publications |
| User’s guide? | Adequate |
| Developer’s guide? | Adequate |
7. Dissemination
| Current Conformance Level / Target Conformance Level |
|---|
| Adequate |
| Target Audience(s): | “Inner circle” | Scientific community | Public |
|---|---|---|---|
| Simulations | Extensive | Extensive | |
| Models | Extensive | Extensive | |
| Software | Extensive | Extensive | |
| Results | Extensive | Extensive | |
| Implications of results | Extensive | Extensive | Extensive |
8. Independent reviews
| Current Conformance Level / Target Conformance Level |
|---|
| Adequate |
| Reviewer(s) name & affiliation: | |
|---|---|
| When was review performed? | |
| How was review performed and outcomes of the review? | Presentation of concepts, live demo of software |
9. Test competing implementations
| Current Conformance Level / Target Conformance Level |
|---|
| Adequate |
| Yes or No (briefly summarize) | |
|---|---|
| Were competing implementations tested? | We plan to explore a few machine learning models |
| Did this lead to model refinement or improvement? | Performance on all models will be tested and compared |
10. Conform to standards
| Current Conformance Level / Target Conformance Level |
|---|
| Extensive |
| Yes or No (briefly summarize) | |
|---|---|
| Are there operating procedures, guidelines, or standards for this type of multiscale modeling? | Yes, we will meet the guidelines and standard on the o2s2parc platform |
| How do your modeling efforts conform? | We meet all documentation, programming and credibility standards from independent reviews by users |