**10. References**


Altschul, S.F.; Madden, T.L.; Schaffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W. & Lipman, D.J.

Andreeva, A.; Howorth, D.; Chandonia, J.M.; Brenner, S.E.; Hubbard, T.J.P.; Chothia, C. &

Bateman, A.; Birney, E.; Cerruti, L.; Durbin, R.; Etwiller, *L.;* Eddy, S.R.; Griffiths-Jones, S.;

Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliand, G.; Bhat, T.N.; Weissing, H.; Ilya, N.S. &

Black, S.D. & Mould, D.R. (1991). Development of hydrophobicity parameters to analyze

Chen, J. & Chaudhari, N. (2007). Cascaded bidirectional recurrent neural networks for

Cheng, J.; Sweredoski, M.J. & Baldi, P. (2006). DOMpro: protein domain prediction using

Dong, L.; Yuan, Y. & Cai, T. (2006). Using bagging classifier to predict protein domain

Dumontier, M.; Yao, R.; Feldman, H.J. & Hogue, C.W. (2005). Armadillo: domain boundary

Elhefnawi, M.M; Youssif, A.A; Ghalwash, A.Z & El Behaidy, W.H. (1 January 2010). *An* 

Henikoff, S. & Henikoff, J. G. (1992). Amino acid substitution matrices from protein blocks, *PNAS*, Vol.89, No.22, (November 1992), pp.10915-10919, ISSN 0027-8424

No.Database Issue, (January 2004), pp.D138-D141, ISSN 0305-1048

Vol.193, No.1, (February 1991), pp.72-82, ISSN 0003-2697

(1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. *Nucleic Acids Research*, Vol.25, No.17, (July 1997), pp.3389-3402,

Murzin, A.G. (2008). Data growth and its impact on the SCOP database: new developments. *Nucleic Acids Research*, Vol.36, No.Database Issue, (November 2007),

Howe, K.L.; Marshall, M.; Sonnhammer, E.L.; David, J.; Studholme, C.Y. & Sean, R.E. (2004). The Pfam protein families database. *Nucleic Acids Research*, Vol.32,

Bourne, P.E. (2000). The protein data bank. *Nucleic Acids Research*, Vol.28, No.1,

proteins which bear post or contranslation modification. *Analytical Biochemistry*,

protein secondary structure prediction. *IEEE/ACM Transactions on Computational Biology and Bioinformatics*, Vol.4, No.4, (October 2007), pp.572-582, ISSN 1545-5963 Chen, P.; Liu, C.; Burge, L.; Li, J.; Mohammad, M.; Southerland, W.; Gloster, C. & Wang, B.

(2010). DomSVR: domain boundary prediction with support vector regression from sequence information alone. *Amino Acids*, Vol.39, No.3, (February 2010), pp.713-726,

profiles, secondary structure, relative solvent accessibility, and recursive neural networks. *Data Mining and Knowledge Discovery*, Vol.13, No.1, (July 2006) pp.1-10,

structural class. *Journal of Biomolecular Structure and Dynamics*, Vol.24, No.3,

prediction by amino acid composition. *Journal of Molecular Biology*, Vol.350, No.5,

*Integrated Methodology for Mining Promiscuous Proteins: A Case Study of an Integrative Bioinformatics Approach for Hepatitis C Virus Non-structural 5a Protein*, Springer, Retrieved from http://www.springerlink.com/content/ l067380601040028/ Gewehr, J. E.; Hintermair, V. & Zimmer, R. (2007). AutoSCOP: automated prediction of

SCOP classification using unique pattern-class mapping. *Bioinformatics*, Vol.23,

**10. References** 

ISSN 0305-1048

ISSN 0939-4451

ISSN 1384-5810

pp.D419-D425, ISSN 0305-1048

(January 2000), pp.235-242, ISSN 0305-1048

(December 2006), pp.239-242, ISSN 0739-110

(July 2005), pp.1061-1073, ISSN 0305-1048

No.10, (March 2007), pp.1203-1210, ISSN 1367-4803


**8** 

*Brazil* 

**Recurrent Self-Organizing Map for** 

*Federal University of Pará (UFPA)* 

**Severe Weather Patterns Recognition** 

José Alberto Sá, Brígida Rocha, Arthur Almeida and José Ricardo Souza

Weather patterns recognition is very important to improve forecasting skills regarding severe storm conditions over a given area of the Earth. Severe weather can damage electric and telecommunication systems, besides generating material losses and even losses of life (Cooray et al., 2007; Lo Piparo, 2010; Santos et al., 2011). In especial for the electrical sector, is strategic to recognize weather patterns that may help predict weather caused damages. Severe weather forecast is crucial to reduce permanent damage to the systems equipments

This study aimed to evaluate the temporal extensions applicability of Self-Organizing Map (Kohonen, 1990, 2001) for severe weather patterns recognition over the eastern Amazon region, which may be used in improving weather forecasting and mitigation of the risks and

A large part of this region is located at low latitudes, where severe weather is usually associated with the formation of peculiar meteorological systems that generate a large amount of local rainfall and a high number of lightning occurrences. These systems are

Convective indices pattern recognition has been studied by means of neural network to determine the best predictors among the sounding-based indices, for thunderstorm prediction and intensity classification (Manzato, 2007). The model was tested for the Northern Italy conditions (Manzato, 2008). Statistical regression methods have also been used for radiosonde and lightning observations data obtained over areas of Florida

These important contributions to this area of study have shown that the applications should be pursued to find out the best predicting statistical tools. Moreover, the achieved skills are largely dependent on the hour of the sounding and the local climatic conditions. So far few studies have been carried out for the extremely moist tropical conditions, prevailing over the Amazon region, where the data for the case studies analyzed in this chapter were obtained.

In this context, the convective patterns recognition for the Amazon region may be used in local weather forecast. These forecasts are subsidiary elements in decision-making regarding preventive actions to avoid further damage to the electrical system. These outages lead to

noted for their intense convective activity (Jayaratne, 2008; Williams, 2008).

**1. Introduction** 

damages associated.

and outages in transmission or distribution lines.

Peninsula in the U. S. A. (Shafer & Fuelberg, 2006).

