39. Hall M, Frank E, Holmes G, et al. The WEKA data mining software: an update. SIGKDD Explor Newsl 2009; 11(1): 10-8. 40. EL-Manzalawy Y, Honavar V. Integrating LibSVM into Weka Environment. 2005; Available from: http://www.cs.iastate.edu/~yasser/wlsvm. 41. C, Lin C. {LIBSVM}: a library for support vector machines. 2001; Available from: http://www.csie.ntu.edu.tw/~cjlin/libsvm. 42. Silverman BW. Density estimation for statistics and data analysis. London ; New York: Chapman and Hall; 1986. 43. Verleysen M, François D. The Curse of Dimensionality in Data Mining and Time Series Prediction. In: Cabestany J, Prieto A, Sandoval F, editors. Computational Intelligence and Bioinspired Systems: Springer Berlin / Heidelberg; 2005. p. 85-125. Fig. (1). Overall workflow of the protocol to the Protocol section under Methods for a detailed explanation of the workflow. Table 1. General properties of datasets max # of sequences µ length length µPID (%) σ % identity Mesophilic 3075 339 225 40 Training Set -- Thermophilic 1609 326 225 42 Mesophilic 325 358 209 47 Test Set 8.40 Thermophilic 382 349 204 50 Table 2. Reduced Amino Acid Alphabets Alphabet Size Reference Native 20 [24] Ab 10-19 Dssp 10-14 [31] Eb 11, 13 [25] [31] Gbmr 10-14 Hsdm 10,12,14-17 [30] Lr 10 [26] Lwi 10-19 [27] [27] Lwni 10,11,14 Lzbl 10-16 [28] Lzmj 10-16 [28] [29] Ml 10,15 Sdm 10-14 [30] Random 10 This study
39. Hall M, Frank E, Holmes G, et al. The WEKA data mining software: an update. SIGKDD Explor Newsl 2009; 11(1): 10-8. 40. EL-Manzalawy Y, Honavar V. Integrating LibSVM into Weka Environment. 2005; Available from: http://www.cs.iastate.edu/~yasser/wlsvm. 41. C, Lin C. {LIBSVM}: a library for support vector machines. 2001; Available from: http://www.csie.ntu.edu.tw/~cjlin/libsvm. 42. Silverman BW. Density estimation for statistics and data analysis. London ; New York: Chapman and Hall; 1986. 43. Verleysen M, François D. The Curse of Dimensionality in Data Mining and Time Series Prediction. In: Cabestany J, Prieto A, Sandoval F, editors. Computational Intelligence and Bioinspired Systems: Springer Berlin / Heidelberg; 2005. p. 85-125. Fig. (1). Overall workflow of the protocol to the Protocol section under Methods for a detailed explanation of the workflow. Table 1. General properties of datasets # of sequences µ length σ length max % identity µPID (%) Training Set Mesophilic 3075 339 225 40 -- Thermophilic 1609 326 225 42 Test Set Mesophilic 325 358 209 47 8.40 Thermophilic 382 349 204 50 Table 2. Reduced Amino Acid Alphabets Alphabet Size Reference Native 20 Ab 10-19 [24] Dssp 10-14 [31] Eb 11, 13 [25] Gbmr 10-14 [31] Hsdm 10,12,14-17 [30] Lr 10 [26] Lwi 10-19 [27] Lwni 10,11,14 [27] Lzbl 10-16 [28] Lzmj 10-16 [28] Ml 10,15 [29] Sdm 10-14 [30] Random 10 This study
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