Understanding Mouse Lactogenesis by Transcriptomics and Literature Analysis.

Citation: Ling, MHT. 2009. Understanding Mouse Lactogenesis by Transcriptomics and Literature Analysis. Doctor of Philosophy. Department of Zoology, The University of Melbourne, Australia.

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This thesis is advised by Professor Kevin R. Nicholas (currently in Deakin University, Australia) and co-advised Associate Professors Christophe Lefevre (currently in Deakin University, Australia) and Feng Lin (currently in Nanyang Technological University, Singapore). This thesis refuted previous assumption that generic computational linguistics processor is unable to process biomedical text due to domain-specificity and attributed it to complementary parts-of-speech tag use in the shallow parsing (breaking down sentences into phrases) process. This thesis confirmed that subject-verb-object structure is a suitable intermediate for extracting protein-protein interactions from text and demonstrated the flexibility of this technique in information extraction. This thesis demonstrated that information extraction by computational linguistics can supplement information extraction by statistical co-occurrence. Using computational and statistical information extraction, a filter representing the current state of biological knowledge was built to be used with microarray analysis for identifying potential novel hypotheses for further research. This thesis examined the relevance of mouse hormone-treated mammary tissue culture in studying mouse lactogenesis by comparing the transcriptomes of cultured tissues with in vivo mammary tissues across the lactation cycle using Affymetrix microarrays. It concluded that the tissue culture is useful in the study of primary hormonal responses but is unlikely to be useful in studying sustained responses and the tissue culture is a useful tool to “re-construct” the set of hormonal stimuli required to simulate mouse mammary tissues into lactogenesis.