LEITURA BÁSICA

 

 

 

·         Bioinformática:

 

o   Joel B. Hagen (2000). The origins of bioinformatics. Nature Reviews (Genetics), v. 1, pp. 231.

 

o   João C. Setúbal (2003). A origem e o sentido da Bioinformática. ComCiencia. Campinas, SP.

 

o   Russell Doolittle (2010). The roots of bioinformatics in protein evolution. PLoS Computational Biology, 6(7), pp. 1.

 

o   Paulien Hogeweg (2011). The roots of bioinformatics in theoretical biology. PLoS Computational Biology, 7(3), pp. 1.

 

o   R. B. Altman (1998). A Curriculum for Bioinformatics: the Time is Ripe. Bioinformatics, 14(7), pp. 549-550.

 

o   David B. Searls (2002). The language of genes. Nature, v. 420, pp. 211-218.

 

·         Banco de Dados Biológicos

 

o   Andreas D. Baxenavins (2003). The Molecular Biology Database Collection: 2003 update -- Nucleic Acids Research, 31(1):1-12.

 

o   L. Stein (2001). Genome Annotation: from sequence to biology, Nature Reviews: Genetics, v. 23, pp. 493-503.

 

o   J. B. L. Bard and S. Y. Rhee (2004). Ontologies in Biology: design, applications and future challenges, Nature Reviews: Genetics, v. 5, 213-222.

 

 

·         Predição de Genes

 

o   M. W. Craven and J. W. Shavlik (1994). Machine learning approaches to gene recognition, IEEE Expert, 9(2), pp. 2-10.

 

o   C. Mathé et al. (2002). Current methods of gene prediction, their strengths and weaknesses, Nucleic Acids Research, 30(19), pp. 4103-4117.

 

o   A. G. Pedersen et al. (1999).The biology of eukaryotic promoter prediction: a review.  Comput Chem. 23(3-4), pp. 191-207.

 

·         Agrupamento e Interpretação de Dados de Expressão Gênica

 

o   Jain, A. K., Murty, M. N., e Flynn, P. (1999). Data clustering: a review. ACM Computing Surveys, 3(31):264–323.

 

o   Molla, M. et al. (2003). Using machine learning to design and interpret gene expression microarrays. AI Magazine (Special Issue on Bioinformatics). A ser publicado.

 

 

o   Quackenbush, J. (2001). Computational analysis of cDNA microarray data. Nature Reviews, 6(2):418–428.

 

o   A. A. Alizadeh et al. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403:503–511.

 

 

o   Altman, R. B. e Raychaudhuri, S. (2001). Whole-genome expression analysis: challenges beyond clustering. Curr. Opin. Struct. Biol., 6(11):340–347.

 

o   Eisen, M. B. et al. (1998). Cluster analysis and display of genome-wide expression pattern. In Proc. of National Academy of Sciences USA, volume 95, pp. 14863–14868.

 

 

o   Tamayo, P. et al. (1999). Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. In Proc. Natl. Acad. Sci. USA, 96:2907–2912.

 

·         Classificação com Dados de Expressão Gênica

 

o   Golub, T. et al. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 5439(286):531–537.

 

o   Khan, J. et al. (2001). Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, 7:673–679.

 

 

·         Predição de Estrutura Secundária de Proteínas

 

o   B. Rost (2001). Review: protein secondary structure prediction continues to rise. J Struct Biol. May-Jun;134(2-3):204-18.

 

·         Computação com Literatura Biomédica

 

o   S. Raychaudhuri et al. (2003). The computational analysis of scientific literature to define and recognize gene expression clusters. Nucleic Acids Research, 31(15), 4553-4560.

 

o   M. D. Yandle and W. H. Majoros (2002). Genomics and natural language processing. Nature Reviews: Genetics, v. 3, pp. 601-610.

 

 

o   L. Hirschman et al. (2002). Accomplishments and challenges in literature data mining for biology. Bioinformatics, 18(12), pp. 1553-1561.