1. Work in Progress Presented by Ian Wood to CASCI, 4/24/13 Advisor: Luis Rocha

2.  Motivation and Previous Work  Implementation  Preliminary Results  Future Directions

3. Image From: J. Carneiro, et al., “When three is not a crowd: a Crossregulation model of the dynamics and repertoire selection of regulatory CD4+ T cells.,” Immunological Reviews, vol. 216, pp. 48–68, 2007.

6. Image From: A. Abi-Haidar and L. M. Rocha, “Collective Classification of Textual Documents by Guided Self-Organization in T-Cell Cross- Regulation Dynamics,” Evolutionary Intelligence, p. In press, 2011.

7. Red: Balanced training with cell death Green: Positive- only training with cell death Blue: Balanced training with cell death Yellow: Positive- only training with cell death

8. Image From: A. Abi-Haidar and L. M. Rocha, “Collective Classification of Textual Documents by Guided Self-Organization in T-Cell Cross- Regulation Dynamics,” Evolutionary Intelligence, p. In press, 2011. Red: Training and testing documents ordered by time Green: reinforced bias Blue: Documents out of order

11.  Benefits  C/C++ executes fast  Individual objects can be tracked  Interface structure allows implementations of parts to be swapped  Can take advantage of hardware  Limitations  Development time is slow  System is not easily ported

13.  Good for performing the same process across many elements in parallel  Could allow for efficient binding of Tcells without exact matching Images From: http://docs.nvidia.com/cuda/cuda-c-programming- guide/index.html

15.  Specs: NVIDIA GeForce GTX 560 Ti running on Windows 7  384 CUDA cores  1 GB dedicated memory, 4 GB available  Average binding time per document of the inefficient sequential version: 128.5766 seconds  Average binding time per document of the CUDA parallel version: 1.650175 seconds  Average binding time per document of the mapped sequential version: 0.061443669  Speedup1 = sequential/parallel = 77.92  Speedup2 = sequential/mappedsequential= 2093

18.  Most of my work thus far is replication of Al’s work Image From: A. Abi-Haidar and L. M. Rocha, “Collective Classification of Textual Documents by Guided Self-Organization in T-Cell Cross- Regulation Dynamics,” Evolutionary Intelligence, p. In press, 2011.

19.  Mixed vs Separated  Mixed – Training and Testing set are randomly interleaved. The First 10% (12) documents are labeled  Separated – Testing set is presented after training  Ordered vs Shuffled  Ordered – Documents are ordered by month of publication (random within month)  Shuffled – Documents are randomly shuffled without regard to date of publication

20. Equations Source: Luis Rocha’s IARPA presentation

21. nsloteselfrselfenselfrnselfeunlabrunlabedraterdratecondcondiprecisionaccuracyrecallmccf1tpostnegfposfneg 1239826311210.740.80.930.620.822820102 1339825311210.950.780.60.610.731829112 39825322210.840.780.70.570.76212649 20812 88825 2210.570.130.270.24430026 2012241210121022120.580.6310.390.73308220 20812 88825 520.580.6310.390.73308220

22. Top Effector WordsTop Regulator WordsTop Weighted Effector WordsTop Weighted Regulator Words highsepharosehighsepharose proteinaffinitiindicatengaffiniti conditionshybridlowhybrid lowpcrconditionspcr indicatengchangevariouschange variousimmunoprecipitatedproteinimmunoprecipitated shownidentifimaniidentifi titlepullshownpull resultslibrarititleinteraction alsofragmentresultsinteracts keywordinteractshoweverlibrari onliinteractiononlifragment fighaalsocloned figureeclkeywordidentificateon cellidentificateonfigureha howeversuppressescelltranscriptional keywordsdeletionfigdeletion introductionlysatekeywordsbeads bodidemonstratedintroductionlysate abstractneitherseemember

25. nsloteselfrselfenselfrnselfeunselfrunselfedraterdratecondcondiprecisionaccuracyrecallmccf1tpostnegfposfneg 1239826311210.740.80.930.620.822820102

26. nsloteselfrselfenselfrnselfeunselfrunselfedraterdratecondcondiprecisionaccuracyrecallmccf1tpostnegfposfneg 1239826311210.740.80.930.620.822820102

27. nsloteselfrselfenselfrnselfeunselfrunselfedraterdratecondcondiprecisionaccuracyrecallmccf1tpostnegfposfneg 14383737125100.380-0.36023730 20466444116100.50000300

28. nsloteselfrselfenselfrnselfeunselfrunselfedraterdratecondcondiprecisionaccuracyrecallmccf1tpostnegfposfneg 1239826311210.740.80.930.620.822820102

29. nsloteselfrselfenselfrnselfeunselfrunselfedraterdratecondcondiprecisionaccuracyrecallmccf1tpostnegfposfneg 14383737125100.380-0.36023730 20466444116100.50000300

30. nsloteselfrselfenselfrnselfeunselfrunselfedraterdratecondcondiprecisionaccuracyrecallmccf1tpostnegfposfneg 10612656511 120.670.70.80.410.732418126 10612656511 210.5 100.67300 0

31.  Sooner  Exhaustive parameter search  Effect of multiple iterations on distributions  Characterizing sensitivity (analytically/artificial data)  Other datasets and comparisons to other classifiers

32.  Later  Proximity on APC based on proximity in text  Bi-gram features  Other binding function ▪ Substring ▪ Sequence comparison binding

33.  J. Carneiro, et al., “When three is not a crowd: a Crossregulation model of the dynamics and repertoire selection of regulatory CD4+ T cells.,” Immunological Reviews, vol. 216, pp. 48–68, 2007.  A. Abi-Haidar and L. M. Rocha, “Collective Classification of Textual Documents by Guided Self-Organization in T-Cell Cross-Regulation Dynamics,” Evolutionary Intelligence, p. In press, 2011.