# Presentation: 016422

## Application integration with web-tools via caBIG API support

**Application integration with web-tools via caBIG API support**

- * Disease classification by mass spectrometry pattern recognition (Q5)
- * Anonymous in-silico screening (ACSN)
- * Image analysis (Vmicro)
- Remote microscope
- Mixture modeling

## Disease classification by mass spectrometry pattern recognition

- Proteome Analysis Expression analysis: proteins (mass spectrometry)

- probabilistic classification of healthy vs. disease whole serum samples using mass spectrometry

- Q5

## Disease classification by mass spectrometry pattern recognition

- Both SELDI protein chip type and sample preparation method contribute significantly to classification accuracy.

- Q5

- Achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset

## Disease classification by mass spectrometry pattern recognition

- These SWISSPROT and TrEMBL proteins are consistent with *m/z* peaks of the discriminant having significance for classification of Ovarian Cancer serum samples. Due to mass-aliasing, the database lookup does not prove these proteins present in the serum samples, but these proteins serve as candidates in the search for novel biomarkers.

- Q5

## Anonymous in-silico screening  service

- Encrypted query and encrypted target
- Outcome classification as information neutral
- 	+/- with qualification
- Negotiated co-disclosure with decryption
- 	key exchange
- Demonstration of concept
- 	- Potential-ligand protein target identification
- 		Anonymous Compound Screening and Negotiation *(ACSN)*
- 	- Others
- 	(will resemble double blind testing with followup aggreements)

## Anonymous in-silico screening  service

*(ACSN)*
- Additional support requirements already well underway
- Secure Content Exchange Negotiation System (SCENS)

- 1. Determination of available sources through 		information requests to the SCENS system
- 2. Standard, secure, and customizable negotiation 		independent of the type of entities involved
- 3. Feedback from data borrowers collected through the 	SCENS system provides added value and 		incentives for data providers to use the sharing 	process

## Image analysis 

- Remote Microscope for tissue-slide analysis
- Virtual Microscope (V.*micro*)
-   morphometric modeling PCA similarity
- 	training and mining interactions with images
- -  serial section with data missing
- 	C and C++, matlab, ImageJ
- -  datasource for these applications
- 	SCENS

## Proposed applications to develop further with full caBIG API interoperability

- Q5

- _Open access application ?_
- -source code available
- -use cases are reported (in press)
- -ported and wrapped with open
-   source technologies for
-   public access
- -support metadata development
-   with database curating activities
-   via caBIG APIs

*ACSN*

- V.*micro*

- +

- +

- +

- +

- +/-

- +

- +/-

## Dartmouth application references

- Q5

*SCENS*

- V.*micro*

- Lilien, R.H., Farid, H., Donald, B.R.  2003.  Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum.  J. Comput. Biol. 10 (in press)

- Ye, S., et al. SCENS: A system for the mediated sharing of sensitive data. In Third ACM/IEEE Joint Conference on Digital Libraries. 2003. Houston, TX.