Discriminating Outer Membrane Proteins with Fuzzy K-Nearest Neighbor Algorithms Based on the General Form of Chou’s PseAAC

Abstract

Outer membrane proteins (OMPs) play important roles in cell biology. In addition, OMPs are targeted by multiple drugs. The identification of OMPs from genomic sequences and successful prediction of their secondary and tertiary structures is a challenging task due to short membrane-spanning regions with high variation in properties. Therefore, an effective and accurate silico method for discrimination of OMPs from their primary sequences is needed. In this paper, we have analyzed the performance of various machine learning mechanisms for discriminating OMPs such as: Genetic Programming, K-nearest Neighbor, and Fuzzy K-nearest Neighbor (Fuzzy K-NN) in conjunction with discrete methods such as: Amino acid composition, Amphiphilic Pseudo amino acid composition, Split amino acid composition (SAAC), and hybrid versions of these methods. The performance of the classifiers is evaluated by two datasets using 5-fold crossvalidation. After the simulation, we have observed that Fuzzy K-NN using SAAC based-features makes it quite effective in discriminating OMPs. Fuzzy K-NN achieves the highest success rates of 99.00% accuracy for discriminating OMPs from non-OMPs and 98.77% and 98.28% accuracies from α-helix membrane and globular proteins, respectively on dataset1. While on dataset2, Fuzzy K-NN achieves 99.55%, 99.90%, and 99.81% accuracies for discriminating OMPs from non- OMPs, α-helix membrane, and globular proteins, respectively. It is observed that the classification performance of our proposed method is satisfactory and is better than the existing methods. Thus, it might be an effective tool for high throughput innovation of OMPs.

A novel coding scheme of nuclear receptor subfamilies prediction

“…The superiority of adding the PseAA components is that they can include some key characteristics of a protein sequence by means of a discrete mode as stated above [12]. In this way, a protein sequence can be represented by a vector with the help of the Chou's PseAA composition, i.e., Fuzzy K-nearest neighbor classification algorithm is a special forms of the K-nearest neighbor algorithm family [13,14]. Replace a voting of assigning roughly the label in the nearest neighbors with …”

Identify Secretory Protein of Malaria Parasite with Modified Quadratic Discriminant Algorithm and Amino Acid Composition

“…Among the three test methods, the jackknife test is deemed to be the least arbitrary that can always yield a unique result for a given benchmark dataset [29] and has been widely used to examine the performance of various predictors [24][25][26][30][31][32][33][34][35][36][37]. However, considering the longer time needed for the jackknife test and because the goal of our paper concentrated on introducing a new model for secretory protein prediction and comparing it with published results [4-6], we adopted the fivefold crossvalidation to evaluate the performance of our method.…”

Application of Data Mining based on Classifier in Class Label Prediction of Coal Mining Data

“…Nearest neighbor (NN) classifier [10] is also known as recently some query, it is a kind of mechanism which can identify the location data points the based on the known value's nearest neighbor. Nearest neighbor classifier have a wide range of applications in many areas, such as pattern recognition, mage database, Internet marketing, cluster analysis, etc.…”