Genome informatics

An J, Totrov M, Abagyan R. Comprehensive identification of druggable protein ligand binding sides. Genome Informatics 2004 15 31-41. 

From Dunker et al. (2000). Genome Informatics 11, 161-171, with permission from Japanese Society for Bioinformatics. 

Fujiwara, Y., Asogawa, M., and Nakai, K. (1997). Prediction of mitochondrial targeting signals using hidden Markov models. In Genome Informatics 53-60. Miyano, S., and Takagi, T. (eds.) Genome Informatics 1997 Universal Academy Press, Inc. Tokyo, Japan. 

Kanehisa, M. (2000). Post-genome Informatics, Oxford University Press, Oxford. 

Certain strains of mice may be less inclined to explore the test environment, such as mice with anxiety- or depressionlike phenotypes (see Mouse Phenome and Mouse Genome Informatics Databases for details). Allow a longer acclimation and/or test time (e.g., 10 or 15 min) to reduce this... 

Masaki Hoshida, Challenge to Genome Informatics, Kyoritsu Shuppan, Tokyo, 1994. 

Satoru Miyano and Toshihisa Takagi, Proceedings of the Eighth Workshop on Genome Informatics (GIW 97), Proceedings of a conference held 12-13 December 1997, at Yebisu Garden Place, Tokyo, in Genome Inf. Ser., Vol. 8, Universal Academy Press, Tokyo, 1997. 

There are many excellent introductory books and journal articles on the subject of neural networks. Just a few of them are listed below in the references. Additionally, there are tutorials online at various web sites. However, the applications of neural network techniques to problems in molecular biology and genome informatics are largely to be found in scientific journals and symposium proceedings. 

Tables 9.1, 10.1 and 11.1 in this book have references to many examples of applications of multilayer perceptrons to problems in genome informatics, with the particular architecture cited for each example.

Despite the fact that the neural network literature increasingly contains examples of radial basis function network applications, their use in genome informatics has rarely been -reported—not because the potential for applications is not there, but more likely due to a lag time between development of the technology and applications to a given field. Casidio et al. (1995) used a radial basis function network to optimally predict the free energy contributions due to hydrogen bonds, hydrophobic interactions and the unfolded state, with simple input measures. 

Artificial neural networks are now widely used to solve various problems in genome informatics and molecular sequence analysis. Part III provides an in-depth discussion of special system designs and considerations for building neural networks for genome informatics applications (chapters 6-8), and broad reviews of state-of-the-art methods and their evaluations (chapters 9-11). 

Figure 6.1 Design issues of neural network applications for genome informatics.

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