With the completion of the Human Genome Project and the growing computational challenges presented by the large amount of genomic data available today, machine learning is becoming an integral part of biomedical research and plays a major role in the emerging fields of bioinformatics and computational biology. This situation offers unparalleled opportunities and unprecedented challenges to machine learning research in general and to Bayesian learning methods in particular. This paper outlines some of the opportunities and the challenges of this endeavor, it describes where the efforts of “cracking the code of life” can most benefit from a Bayesian approach, and it identifies some potential applications of Bayesian machine learning methods to the genomic analysis of squamous cell carcinomas of the head and neck.
Get full access to this article
View all access options for this article.
References
1.
Al Moustafa AE, Alaoui-Jamali MA, Batist G, Hernandez-Perez M, Serruya C, Alpert L, et al. (2002). Identification of genes associated with head and neck carcinogenesis by cDNA microarray comparison between matched primary normal epithelial and squamous carcinoma cells. Oncogene21:2634–2640.
2.
Alevizos I, Mahadevappa M, Zhang X, Ohyama H, Kohno Y, Posner M, et al. (2001). Oral cancer in vivo gene expression profiling assisted by laser capture microdissection and microarray analysis. Oncogene20:6196–6204.
3.
Baldi P, Long AD (2001). A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics17:509–519.
4.
Belbin TJ, Singh B, Barber I, Socci N, Wenig B, Smith R, et al. (2002). Molecular classification of head and neck squamous cell carcinoma using cDNA microarrays. Cancer Res62:1184–1190.
5.
Chen Y, Dougherty ER, Bittner ML (1997). Ratio-based decisions and the quantitative analysis of cDNA microarray images. Biomed Optics2:364–374.
6.
Eisen MB, Spellman PT, Brown PO, Botstein D (1998). Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA95:14863–14868.
7.
Friedman N, Linial M, Nachman I, Pe’er D (2000). Using Bayesian networks to analyze expression data. J Comput Biol7:601–620.
8.
Gonzales-Moles MA, Rodriguez-Archilla A, Ruiz-Avila I, Martinez AB, Morales-Garcia P, Gonzalez-Moles S (2002). p16 expression in squamous carcinomas of the tongue. Onkologie25:433–436.
9.
Ha PK, Benoit NE, Yochem R, Sciubba J, Zahurak M, Sidransky D, et al. (2003). A transcriptional progression model for head and neck cancer. Clin Cancer Res9:3058–3064.
10.
Hartemink AJ, Gifford DK, Jaakkola TS, Young RA (2002). Combining location and expression data for principled discovery of genetic regulatory network models. Pac Symp Biocomput 437–439.
11.
Iyer VR, Eisen MB, Ross DT, Schuler G, Moore T, Lee JC, et al. (1999). The transcriptional program in the response of human fibroblasts to serum. Science283:83–87.
12.
Kuo WP, Whipple ME, Jenssen TK, Todd R, Epstein JB, Ohno-Machado L, et al. (2003). Microarrays and clinical dentistry. J Am Dent Assoc134:456–462.
13.
Lander ES (1999). Array of hope. Nat Genet21(Suppl 1):3–4.
14.
Leethanakul C, Patel V, Gillespie J, Pallente M, Ensley JF, Koontongkaew S, et al. (2000a). Distinct pattern of expression of differentiation and growth-related genes in squamous cell carcinomas of the head and neck revealed by the use of laser capture microdissection and cDNA arrays. Oncogene19:3220–3224.
15.
Leethanakul C, Patel V, Gillespie J, Shillitoe E, Kellman RM, Ensley JF, et al. (2000b). Gene expression profiles in squamous cell carcinomas of the oral cavity: use of laser capture microdissection for the construction and analysis of stage-specific cDNA libraries. Oral Oncol36:474–483.
16.
Leethanakul C, Knezevic V, Patel V, Amornphimoltham P, Gillespie J, Shillitoe EJ, et al. (2003). Gene discovery in oral squamous cell carcinoma through the Head and Neck Cancer Genome Anatomy Project: confirmation by microarray analysis. Oral Oncol39:248–258.
17.
Lipshutz RJ, Fodor SP, Gingeras TR, Lockhart DJ (1999). High density synthetic oligonucleotide arrays. Nat Genet21(Suppl 1):20–24.
18.
Mendez E, Cheng C, Farwell DG, Ricks S, Agoff SN, Futran ND, et al. (2002). Transcriptional expression profiles of oral squamous cell carcinomas. Cancer95:1482–1494.
19.
Newton MA, Kendziorski CM, Richmond CS, Blattner FR, Tsui KW (2001). On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J Comput Biol8:37–52.
20.
Ohyama H, Zhang X, Kohno Y, Alevizos I, Posner M, Wong DT, et al. (2000). Laser capture microdissection-generated target sample for high-density oligonucleotide array hybridization. Biotechniques29:530–536.
21.
Pearl J (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. San Francisco, CA: Morgan Kaufmann.
22.
Pearl J (1995). Causal diagrams for empirical research. Biometrika82:669–710.
23.
Ramoni M, Sebastiani P (1999). Bayesian methods for intelligent data analysis. In: Intelligent data analysis, an introduction. Berthold M, Hand D, editors. New York, NY: Springer, pp. 128–166.
24.
Ramoni M, Sebastiani P, Cohen P (2002a). Bayesian clustering by dynamics. Mach Learn47:91–121.
25.
Ramoni MF, Sebastiani P, Kohane IS (2002b). Cluster analysis of gene expression dynamics. Proc Natl Acad Sci USA99:9121–9126.
26.
Schena M, Shalon D, Davis RW, Brown PO (1995). Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science270:467–470.
27.
Sebastiani P, Gussoni E, Kohane I, Ramoni M (2003). Statistical challenges in functional genomics (with discussion). Stat Sci18:33–70.
28.
Vieland VJ, Wang K, Huang J (2001). Power to detect linkage based on multiple sets of data in the presence of locus heterogeneity: comparative evaluation of model-based linkage methods for affected sib pair data. Hum Hered51:199–208.
29.
Vieland VJ, Sheffield V, Wassink T, Beck J, Goedken R, Childress D, et al. (2003). A new genome screen for autism based on the posterior probability of linkage (PPL) and incorporating language-based phenotypes finds evidence of linkage to several genomic locations, each supported by independent sources of information (abstract). Am J Hum Genet73(Suppl 5):196.
30.
Vigneswaran N, Wu J, Zacharias W (2003). Upregulation of cystatin M during the progression of oropharyngeal squamous cell carcinoma from primary tumor to metastasis. Oral Oncol39:559–568.
31.
Villaret DB, Wang T, Dillon D, Xu J, Sivam D, Cheever MA, et al. (2000). Identification of genes overexpressed in head and neck squamous cell carcinoma using a combination of complementary DNA subtraction and microarray analysis. Laryngoscope110(3 Pt 1):374–381.
32.
Wang K, Huang J, Vieland VJ (2000). The consistency of the posterior probability of linkage. Ann Hum Genet64(Pt 6):533–553.
33.
Wright S (1921). Correlation and causation. J Agr Res20:557–585.
