iTIS-PseTNC: A sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition

TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition

“…As demonstrated by a series of recent publications (Chou 2011;Chen et al 2014;Ding et al 2014;Lin et al 2014;Xu et al 2014;Liu et al 2015) in response to the call (Chou 2011) to establish a really useful sequencebased statistical predictor for a biological system, we need to consider the following procedures: (a) construct or select a valid benchmark dataset to train and test the predictor; (b) formulate the biological sequence samples with an effective mathematical expression that can truly reflect their intrinsic correlation with the target to be predicted; (c) introduce or develop a powerful algorithm (or engine) to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a userfriendly web server for the predictor that is accessible to the public. Below, let us describe how to address these steps one by one.…”

“…28-30 in (Chou 2011). Accordingly, the jackknife test has been widely recognized and increasingly used by investigators to examine the quality of various predictors (Xu et al 2013;Chen et al 2014;Ding et al 2014;Lin et al 2014;Dehzangi et al For each feature and classifier, the average performance of each evaluation index is reported, followed by a standard deviation a The weight (w = 0.8) is optimized by varying its value from 0 to 1 with a step size of 0.1 on the training set over tenfold cross-validation For each feature and classifier, the average performance of each evaluation index is reported, followed by a standard deviation a The weight (w = 0.8) is optimized by varying its value from 0 to 1 with a step size of 0.1 on the training set over tenfold cross-validation X. He et al: TargetFreeze: Identifying Antifreeze Proteins… 2015; Khan et al 2015;Mandal et al 2015).…”

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences

“…"Distance Pair" method incorporates the amino acid distance pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) [108] vector, which is very useful for analysing DNA-binding proteins [15,170,189,275]. PDT is the abbreviation for "physicochemical distance transformation", which can incorporate considerable sequence-order information or important patterns of protein/peptide sequences into Pseudo components [28], which is very useful for conducting various proteome analyses [17, 23, 215-217, 224, 225, 231, 235, 276-289] and genome analysis as well [216,218,220,223,229,255,277,290].…”

Improving N6-methyladenosine site prediction with heuristic selection of nucleotide physical–chemical properties

“…[42]. Motivated by the wide and successful usage of pseudo amino acid composition or Chou's PseAAC in the areas of computational proteomics with protein/peptide sequences [43,44], recently, the concept of pseudo k-tuber nucleotide composition has been developed to deal with DNA/RNA sequences in computational genetics and genomics [45][46][47][48][49]. According to Ref.…”

“…Support vector machine (SVM), which was proposed by Cortes and Vapnik [51], has been widely used in the realm of bioinformatics [29,30,47,49,[52][53][54][55]. The basic idea of SVM is to transform the input vector into a high-dimension Hilbert space by kernel functions and then seek a separating hyper plane between classes with the maximal margin in this space.…”