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

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Future work and possible project topic in gene regulatory network l.jpg

Future Work and Possible Project Topic in Gene Regulatory network

Learning from multiple data sources;

Learning causality in Motifs;

Learning GRN with feedback loops;


Learning from multiple data sources l.jpg
Learning from multiple data sources network

  • 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.


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Learning Causality in Motifs network

  • They be used to assemble a transcriptional regulatory network.

  • Network motifs are the simplest units of network architecture.




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Future work and Possible Project Topics in protein interaction

  • Learning from multiple data sources;

  • Disease related protein-protein interactions;

  • Learning from different species;


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Learning from Multiple data sources interaction

  • Gene Neighbor: identifies protein pair encoded in close proximity across multiple genomes.

  • Rosetta Stone

  • Phylogenetic Profile

  • Gene Clustering:

  • closely spaced genes, and assigns a probability P of observing a particular gap distance


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Disease related interaction protein-protein interactions;

Disease Related???

-- Query NCBI OMIM

Database


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Learning from interactiondifferent species



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Projects for BioQA interaction

  • 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?

  • 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?


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Projects for BioQA interaction

  • 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?

  • 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.


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Projects for BioQA interaction

  • Semantics of Words

    • Dealing with the semantics of words to improve the retrieval of answers

      • Example: semantic relation between “role” and “play”

  • 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

  • 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



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Other projects interaction


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Build an Ontology interaction

  • Build an ontology for a domain for which we do not have an ontology yet.

  • Verify its consistency.


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Various kinds of text extraction systems interaction

  • 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


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Drug classification in Pharmacogenetics interaction

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.


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