A protein structural classes prediction method based on predicted secondary structure and PSI-BLAST profile

Predicting protein structural classes based on complex networks and recurrence analysis

“…The overall accuracy (OA) is computed for each dataset. Also, the following three standard performance measures have recently been widely used to estimate prediction accuracy: Sensitivity (Sens), Specificity (Spec) and Matthew's Correlation Coefficient (MCC) [11,28,31,73,77]. The MCC value ranges between -1 and 1, where 0 represents random correlation and larger positive (negative) values indicate a better (lower) prediction quality for a given class.…”

Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou's general PseAAC

“…We select the accuracy of each class and overall accuracy as evaluation indexes which are summarized in Table 6. The compared methods include the famous methods SCPRED [17] and MODAS [19], and the other competitive methods such as RKS-PPSC [18], IEA-PSSF [72], Zhang et al [45], PSSS-PSSM [20], LCC-PSSM [73], AADP-PSSM [21] and AAC-PSSM-AC [23]. Among these methods, the PSSS-PSSM method has the best overall accuracy on the three datasets.…”

Phogly–PseAAC: Prediction of lysine phosphoglycerylation in proteins incorporating with position-specific propensity

“…There exist a variety of feature extraction methods such as BLO-SUM62 matrix (Henikoff and Henikoff, 1992), PSSM (position specific scoring matrix) (Ding et al, 2014), PSDP (position-specific dipeptide propensity matrix) (Xu et al, 2014), CKSAAP (composition of k-space Amino Acid Pair) (Wang et al, 2009), GPS (group-based prediction system) (Xue et al, 2010) and so on, which were widely used and had shown their effective performance. In this study, we constructed the features from the sequence order information and position specific amino acid propensity was utilized to convert peptide fragments into mathematical expressions.…”