An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species

Table 8

Unsupervised algorithms and parameter values in the experiments.

Algorithm	Parameter values	Implementation
RBH	Soft filter and Smith Waterman alignment; -value = 1e − 06	BLASTp program^1; Matlab script
RSD	-value thresholds: 1e − 05, 1e − 10, and 1e − 20; Divergence thresholds α: 0.8, 0.5, and 0.2.	BLASTp program^1; Python script^2
OMA	Default parameter values	OMA stand-alone^3

Available in http://www.ncbi.nlm.nih.gov/BLAST/.
²Available in https://pypi.python.org/pypi/reciprocal_smallest_distance/1.1.4/.
³Available in http://omabrowser.org/standalone/OMA.0.99z.3.tgz.