Applying data integration into reconstruction of gene networks from micro

Transcription

1 Applying data integration into reconstruction of gene networks from microarray data PhD Thesis Proposal Dipartimento di Informatica e Scienze dell Informazione Università degli Studi di Genova December 3, 2009

2 Contents Data-driven biomarker discovery Baseline knowledge (microarray data) Prior knowledge (biologically relevant data) Data-driven/knowledge-driven biomarker discovery Data fusion strategy Knowledge representation

3 Data-driven biomarker discovery

4 Baseline knowledge source: ArrayExpress (EBI)

5 Baseline knowledge In data-driven biomarker discovery model, statistical learning is performed on gene expression data matrix (numerical data) Post-processing involves sample annotations and gene annotations For example, it is possible to find a mapping between microarray probesets and functionality of the genes (using Genbank annotations)

6 Prior knowledge Gene Ontology (more insight into functionality) Online Mendelian Inheritance in Man (more insight into genetic disease research) Kyoto Encyclopedia of Genes and Genomes (more insight into biochemical data, among others) Genbank (more insight into putative functionality, reference sequence database)

7 Gene Ontology

8 OMIM <?xml version= " 1.0 "?> <!DOCTYPE Mim e n t r i e s PUBLIC " //NLM / / NCBI Mim / / EN" " h t t p : / /www. ncbi. nlm. nih. gov / dtd / NCBI_Mim. dtd " > <Mim e n t r i e s > <Mim e n t r y > <Mim entry_mimnumber>143100< / Mim entry_mimnumber> <Mim entry_mimtype value= " pound ">3< /Mim entry_mimtype> <Mim e n t r y _ t i t l e >HUNTINGTON DISEASE ; HD< / Mim e n t r y _ t i t l e > <Mim e n t r y _copyright>copyright ( c ) Johns Hopkins U n i v e r s i t y < /Mim e n t r y _copyright> <Mim entry_symbol>htt< / Mim entry_symbol> <Mim entry_locus>4p16.3< / Mim entry_locus> <Mim e n t r y _ a l i a s e s > <Mim entry_aliases_e>huntington CHOREA< / Mim entry_aliases_e> < / Mim e n t r y _ a l i a s e s > <Mim entry_seealso> <Mim c i t > <Mim cit_number>17< / Mim cit_number> <Mim c i t _ a u t h o r >Barinaga< / Mim c i t _ a u t h o r > <Mim c i t _ o t h e r s >< / Mim c i t _ o t h e r s > <Mim c i t _ y e a r >1996< / Mim c i t _ y e a r > < / Mim c i t > Beginning of record for Huntington Disease (XML, 1.4MB)

9 KEGG PC Substance : : = { s i d { i d , version 5 }, source db { name "KEGG", source i d s t r " D00287 " }, synonyms { " ", " Cyclophosphamide ", " Cyclophosphamide (USP) ", " Cyclophosphamide hydrate ", " Cyclophosphamide hydrate ( JP15 ) ", " Cytoxan ", " Cytoxan (TN) ", " D00287 ", " Neosar ", " Neosar (TN) " }, comment { " A n t i n e o p l a s t i c ", Beginning of record for cyclophosphamine (ASN.1, 46KB), listed as drug for neuroblastoma cancer, obtained from PubChem through KEGG links

10 Genbank >gi gb W W57613 zd24f07. s1 Soares_fetal_heart_NbHH19W Homo sapiens cdna clone IMAGE: , mrna sequence CAGTGTCCACAAGTAAAGCTTTATTGAAATACATTCACGTTCATTCATTTATATATTGTCAAGGGCTGCT TTTGCACTACATATGGTCAGAGTGGGTAGTTATGACATATTGAATGGCCCACAAGGCTAAACTATTTGCT ATCTGATCCCTTACAGTAAGTAAAGTTAGCCAACTTGTGATTTAGACAGTAGAGGAATCAAATGAAATAG CATCAAAAAGTTGTAGGATCATTGGGCCATTCTAACCCTAAGTCAGAGGAAGAAATGGAATGGGAAGTAG AGGGTGAACAGAAAGAACACAATACATCTGAGTGGGACGCACACATGTTGAGTAGTAAAAATCTCTGAGG ACTTCTTGTTAGGGGGATGCCAAAAAGTGGGGCCCTACCCAAGAAGATATAAAAAGGCTGAGATTAGGGC CGGGGTGCCGGTGGCTCCACACTTTGTAATCCCCAGCACTTTGGAGAAGGCTGAAGCCGGGTNGGATAAC CCTGGGATCCAGGAGNTTCCAGAACCAGNCCTTGGGCCCACATTNGGGGAACCCCCATTTTC FASTA record of transcript W57613, identified by l 1 -l 2 method as member of hypoxia gene signature, with no known functionality

11 Data-driven/knowledge-driven biomarker discovery

12 Data fusion strategy General strategy: Linear combination of elementary kernels κ = {K 1,...,K m }, producing superkernel K = m i=1 µ i K i where µ i 0 for i = 1,...,m, used further in statistical learning phase Primary question: How to design suitable elementary kernels for heterogeneous information from databanks? Secondary question 1: Is it possible to use linear combination property to construct kernels that can accumulate knowledge at a time? Secondary question 2: Is it possible to define other data fusion strategies?

13 Knowledge representation General goal: treating numerical matrix as baseline data type, define set of operations, involving information taken from other data sources, that will enhance results produced in statistical learning phase when using that matrix Primary question: Is it possible to achieve this without reformulating original SL regularization algorithms? Secondary question: Is it possible to adapt SL regularization algorithms for using with data types other than numerical matrices?