1. Class Projects

2. Future Work and Possible Project Topic in Gene Regulatory network Learning from multiple data sources; Learning causality in Motifs; Learning GRN with feedback loops;

3. Learning from multiple data sources  We have gene expression data and topological ordering information;  Incorporating some other data sources as prior knowledge for the learning; Transcription factor binding location data; … Example: Partial regulatory network recovered using expression data and location data.

4. Learning Causality in Motifs They be used to assemble a transcriptional regulatory network. Network motifs are the simplest units of network architecture.

5. Learning GRN with feedback loops

6. Learning GRN with feedback loops (Con’dProtein-Protein Interactions

7. Future work and Possible Project Topics in protein interaction  Learning from multiple data sources;  Disease related protein-protein interactions;  Learning from different species;

8. Learning from Multiple data sources (a)Gene Neighbor: identifies protein pair encoded in close proximity across multiple genomes. (b)Rosetta Stone (c)Phylogenetic Profile (d)Gene Clustering: closely spaced genes, and assigns a probability P of observing a particular gap distance

9. Disease related protein-protein interactions; Disease Related??? -- Query NCBI OMIM Database

10. Learning from different species

11. BioQA related projects

12. Projects for BioQA 1.Learning Given a set of relevant abstracts, what kind of features can we obtain to enhance our queries? Given a set of questions from users, how can we identify keywords from the questions to form queries? 2.Answer Presentation Given a relevant abstract/article, how can we retrieve the relevant passage with respect to the user’s question? how to extract answers?

13. Projects for BioQA 3.Automatic Extraction Extract relations of gene-disease, gene-biological process (also their corresponding organisms) Uniquely identify the genes A gene symbol can be associated with multiple gene identifiers. Which gene identifier is the right one? Can these extraction processes be generalized? 4.Sortal Resolution Given an abstract and query, perform sortal resolution (but not on pronouns) Example: Given the following abstract:  “In this report, we show that virus infection of cells results in a dramatic hyperacetylation of histones H3 and H4 that is localized to the IFN-beta promoter. … Thus, coactivator-mediated localized hyperacetylation of histones may play a crucial role in inducible gene expression. [PMID: 10024886] and the query about histones, perform resolution on histones Results: histones refer to H3, H4.

14. Projects for BioQA 5.Semantics of Words Dealing with the semantics of words to improve the retrieval of answers Example: semantic relation between “role” and “play” 6.Gene symbol variants, disambiguate gene symbols, entity recognition Generate gene symbol synonyms and variants given a gene symbol in a query Example: variants of “CDC28” can be written as “Cdc28”, “Cdc28p”, “cdc-28” “GSS” is a synonym of “PRNP”, but “GSS” itself is also a gene which is unrelated to “PRNP”. Improve on recognition of diseases, biological processes 7.Extension of Ontology To capture biological processes and their possible relations to diseases Examples: learning and/or memory can influence Alzheimer’s disease Degradation of ubiquitin cycle can cause extra long/short half-life of genes Extra long/short half-life of genes can cause cancer

15. CBioC Class Projects Extraction of organism info for each entity in a relationship High-priority. Use existing software for extraction, but need to use biological databases and algorithms for deducing info (not explicit), and allow users to correct this info. Example, PMID 16107876.Example KALPESH Image extension - extracts images & information about images and allows collaborative curation. Take PDFs & other structured documents, and extract images with their captions & references within the text, then let users polish. Related.Related Use ontologies and some automated tools to ensure consistency and cross-link info 2 people. Information entered by users needs to be validated against existing DB & ontologies. Also, need to tag our data for cross-reference. ExampleExample

16. Other projects

17. Build an Ontology  Build an ontology for a domain for which we do not have an ontology yet.  Verify its consistency.

18. Various kinds of text extraction systems  TREC suggested ones Which method/protocol is used in which experiment/procedure Gene – disease – role Gene – biological process – role Gene – mutation type – biological impact Gene – interaction – gene – function – organ Gene – interaction – gene – disease – organ  Protein Lounge inspired Kinase-phosphatase transcription factor peptide antigen

19. Drug classification in Pharmacogenetics Experimental Data available Drug response on cell lines; gene expression data; gene copy data; mutation analysis data; RNAi data Data from literature Mutation data (Sanger lab); NCI-60 drug response data; Mutation analysis data; Pathway data (e.g. BIND); Gene Ontology Proprietary data Where does the drug physically interact? (600 Kinase – IC 50) Gene expression data of patients after treatments Goal: Given a patient, what kinds of data do we need in order to determine if a drug should be applicable to that patient or not? How do we develop a classifier using these kinds of data? Find gene and protein interaction network (or components) using these data.