A Novel Technique of Feature Extraction Based on Local and Global

Similarity Measure for Protein Classiﬁcation

Neha Bharill and Aruna Tiwari

Department of Computer Science and Engineering, Indian Institute of Technology, PACL Campus, Indore, India

Keywords:

Bioinformatics, Probability-based Features, Position-speciﬁc Information, Binary Feed Forward Neural

Network, Protein Classiﬁcation.

Abstract:

The paper aims to propose a novel approach for extracting features from protein sequences. This approach ex-

tracts only 6 features for each protein sequence which are computed by globally considering the probabilities

of occurrences of the amino acids in different position of the sequences within the superfamily which locally

belongs to the six exchange groups. Then, these features are used as an input for Neural Network learning

algorithm named as Boolean-Like Training Algorithm (BLTA). The BLTA classiﬁer is used to classify the

protein sequences obtained from the Protein Information Resource (PIR). To investigate the efﬁcacy of pro-

posed feature extraction approach, the experimentation is performed on two superfamilies, namely Ras and

Globin. Across tenfold cross validation, the highest Classiﬁcation Accuracy achieved by proposed approach is

94.32±3.52 with Computational Time 6.54±0.10 (s) is remarkably better in comparison to the Classiﬁcation

Accuracies achieved by other approaches. The experimental results demonstrate that the proposed approach

extracts the minimum number of features for each protein sequence. Therefore, it results in considerably

potential improvement in Classiﬁcation Accuracy and takes less Computational Time for protein sequence

classiﬁcation in comparison with other well-known feature extraction approaches.

1 INTRODUCTION

In the recent years, Bioinformatics (Iqbal et al., 2014)

is emerged as a forefront research area. It is referred

as conceptualization of biology in terms of macro-

molecules. Due to dramatic evolution of technol-

ogy and continuous effort of Genome Project, a large

amount of protein, DNA and RNA sequences are gen-

erated on regular basis. In this regard, many tech-

niques have been proposed by the researcher to an-

alyze and interpret the DNA, RNA and protein se-

quences. Among these, protein sequence classiﬁca-

tion (Vipsita and Rath, 2013) is an important problem,

which determines the superfamily of an unknown

protein sequence. The major advantage of category

grouping is that molecular analysis is performed glob-

ally within a superfamily instead of local analysis.

A protein sequence contains the characters from 20

different amino acid alphabets that can occur in any

order. The problem of protein classiﬁcation are for-

mally stated in (Wang et al., 2001). Given a unlabeled

protein sequence S and a set of known superfami-

lies F={F

,...,F

}, the problem is to determine

with certain degree of accuracy whether the protein

sequence S belongs to one of superfamilies from set

, i = 1,..., f . Therefore, classiﬁcation of unknown

protein sequences into one of known superfamilies is

an important task. This will help in identifying the

structure and function of unknown protein sequences.

It also results in saving the large expenses incurred

in performing the experiments in laboratory. One of

the most important practical applications is in drug

discovery. For example, suppose a sequence S is ob-

tained from disease D and it is inferred by classiﬁ-

cation method that sequence S belongs to the super-

family F

. So, to treat the disease D one can use the

combination of existing drugs of superfamily F

In past, many feature extraction approaches (Ver-

gara and Est

evez, 2014) have been proposed by the re-

searchers to deal with the protein sequence classiﬁca-

tion problem. The n-gram encoding schemes (Wang

et al., 2001), (Solovyov and Lipkin, 2013) for extract-

ing features from protein sequence used the local and

global similarities by counting the occurrences of two

amino acids within a protein sequence. Further, the

extracted features are used as an input to Bayesian

Neural Network classiﬁer. Although, the n-gram en-

coding scheme works reasonably well. But its major

219

Bharill N. and Tiwari A..

A Novel Technique of Feature Extraction Based on Local and Global Similarity Measure for Protein Classiﬁcation.

DOI: 10.5220/0005283702190224

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2015), pages 219-224

ISBN: 978-989-758-070-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

drawback is that it fails to consider the positional sig-

niﬁcance of the residue pairs which is an important

consideration in superfamily classiﬁcation. In addi-

tion to it, the number of features extracted by this

approach is extremely large (≥ 62). This imposes a

major limitation on many classiﬁcation approaches.

Also, these algorithms works on large number of fea-

tures therefore they have high-computational com-

plexity. In 2005, Bandyopadhyay proposed another

feature extraction approach that overcomes some of

the limitations of (Wang et al., 2001). This approach

limit the number of features and correspondingly it

extracts 20 features for each protein sequence. Once

the features are extracted, they are used as an input

to fuzzy genetic clustering strategy to evolve a set of

prototypes for each superfamily. Finally, it uses the

nearest neighbour (NN) rule to classify a set of un-

known sequences into a particular superfamily. But,

this approach only considers the global positional in-

formation corresponding to each amino acid. Thus, it

fails to consider the local positioning of each amino

acid in the respective sequence. Another approach

proposed by (Mansouri et al., 2008) which extract

only relevant features from the protein sequences by

counting the occurrence probability of six exchange

groups in each sequence. Then, it uses these extracted

features as an input for generating some of the in-

terpretable fuzzy rules which is used to assign pro-

tein sequences into appropriate superfamily. This ap-

proach suffers from a major drawback that the fea-

tures extracted by this approach only considers the lo-

cal positioning of each sequence within an exchange

group. It fails to consider the global probability of

occurrence of each amino acid in entire superfamily.

Hence, the above discussed classiﬁers do not capture

both the global and local similarity. Thus, the relevant

features are not extracted due to which it results in

degradation of classiﬁcation accuracy and have high-

computational time.

In this paper, the proposed new feature extrac-

tion approach overcomes the limitations of existing

feature extraction approaches. It capture both the

global and local similarity of each protein sequence

for extracting features and only 6 relevant features

corresponding to each protein sequence are extracted.

Firstly, it computes the global probability of each

amino acid present within a sequence by counting the

positional information of amino acid in all the se-

quences. Then, the local similarities are determined

based on the concept of weighting scheme (Karchin

and Hughey, 1998). Further, the computed weights of

each amino acid within a sequence is encoded to their

respective six exchange groups where the exchange

groups are effective equivalence classes of amino

acids derived from PAM (Dayhoff and Schwartz,

1978). Once, the features are extracted then these fea-

tures are fed as an input to the Boolean-Like Training

Algorithm (BLTA) (Gray and Michel, 1992) to per-

form the classiﬁcation of unknown sequences into the

superfamilies. To validate the efﬁcacy of proposed

feature extraction approach, the comparison is done

by implementing other feature extraction approaches

and evaluating their performance on BLTA classiﬁer.

The observation can be drawn from the experimental

results that the proposed approach limit the number of

features extracted corresponding to each protein se-

quence by capturing both local and global similarity

measure thus, leads to the lesser Computational Time

and higher Classiﬁcation Accuracy.

The rest of the paper is organized as follows. The

description of proposed model is illustrated in Sec-

tion 2. In Section 3, the experimental results are re-

ported. Finally, Section 4 concludes this paper.

2 PROPOSED FEATURE

EXTRACTION APPROACH

Protein sequence contains characters from

the amino acid that can be viewed as a text

strings which is formally represented by a set

A={A,C,D, E,F, G, H,I,K,L,M,N, P,Q,R,S, T,V,W

,Y }. The protein sequence can be of any length and

contains the combination of these amino acids in any

order. Therefore, the most important issue in applying

any algorithm for the protein sequence classiﬁcation

is encoding of these protein sequences in terms

of feature vectors and then applying these feature

vectors as an input to any learning algorithm for

classiﬁcation. For proper classiﬁcation of sequences

into superfamilies a relevant input representation

is needed. Thus, the success of learning algorithm

depends on the kind of input data available. The

proposed new feature extraction approach extracts

only 6 relevant features corresponding to each protein

sequence by capturing both the global and local

similarity of protein sequences. Next, section is

presented with the proposed method for computing

the global similarity corresponding to all the protein

sequences belongs to the superfamily. Section 2.2,

describe the proposed local similarity measure which

Figure 1: Primary Structure of Five Related Proteins.

BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms

220

incorporate the global similarity measure computed

for all the protein sequences. Finally, in Section 2.3,

the proposed encoding method is presented which

evaluate the feature vector once the global and local

similarity measures for all the protein sequence is

determined.

2.1 Global Similarity Measure for

Feature Extraction

Given, a set S consist of all the sequences of a pro-

tein superfamily F

, i = 1,..., f which is formally

represented as S ={s

,....,s

} where n represent

the number of sequences. The protein sequences

which belong to the same superfamily share the

structural similarities with each other as shown in

Fig. 1. All the sequences present in Fig. 1 are un-

aligned and taken from same superfamily and con-

sist of total 9 amino acids represented by a set

A={A,D,G,H,K,M, S,V,Y }. These unaligned se-

quences are aligned using BioEdit tool. The global

similarity measure is determined by calculating the

probability of occurrence of each amino acid in a par-

ticular position with respect to the total number of se-

quences present in the superfamily. It is mathemati-

cally represented by

(Probability)

i j

= (Occurence)

i j

/n (1)

where (Probability)

i j

represent the probability of

occurrence of i

amino acid at j

position,

(Occurence)

i j

denote the frequency of i

amino acid

at j

position and n represent the total number of se-

quences in a particular superfamily. For example, as

shown in Fig. 1, the amino acid G occurs in the third

position three times out of ﬁve sequences, therefore

probability of occurrence of G is (Probability)

i j

Thus in the same way, the global similarity measure

for all the sequences shown in Fig. 1 is computed and

presented in Table 1. Once the global similarity mea-

sure is evaluated, the position speciﬁc weight of each

amino acid is calculated. This is discussed in the sub-

sequent section.

Table 1: Global Similarity Measure of Protein Sequences

present in Fig. 1.

Amino acids Position1 Position2 Position3 Position4 Position5

A 0 0.2 0.2 0.2 0

D 0 0 0 0.2 0

G 0 0 0.6 0 0

H 0 0 0 0 0.6

K 0 0.8 0.2 0 0

M 1 0 0 0 0

S 0 0 0 0 0.2

V 0 0 0 0.6 0

Y 0 0 0 0 0.2

Table 2: Feature Vector of Each Sequence Present in Fig. 1.

Sequence e

1 1.4 0.2 0 0.6 1 0

2 0.8 0 0 0.2 1.6 0.2

3 1.4 0 0 0.6 1.6 0

4 0.2 0 0 0.6 1 0

5 1.4 0 0 0.6 1.6 0

2.2 Local Similarity Measure for

Feature Extraction

Given a protein sequence, the weight of each amino

is evaluated by adding all the position speciﬁc occur-

rences of amino acid at that place and the respective

probability of occurrence of amino acid in that place

from the entire super family. It is mathematically rep-

resented as:

Weight(i) = (PSO)

× (Probability)

i j

(2)

where Weight(i) denote the weight of i

amino acid,

(PSO)

represent the position speciﬁc occurrence of

amino acid and (Probability)

i j

represent the prob-

ability of occurrence of i

amino acid at j

position.

For example, in Fig. 1 corresponding to the sequence1

i.e. MKGDH, the weight of each amino acid is calcu-

lated as follows:

Weight(M)=1×1.0=1.0, Weight(K)=1×0.8=0.8

Weight(G)=1×0.6=0.6, Weight(D)=1×0.2=0.2

Weight(H)=1×0.6=0.6

The weights of all other amino acids present in Fig. 1

with respect to the sequence1 is zero. This is be-

cause these amino acids are not present in sequence1.

Hence, for all other remaining sequences the weight

of each amino acid present within the sequence are

calculated in the similar manner.

Therefore, for sequence2 i.e. MKAVY, the weight

of each amino acid is evaluated as follows.

Weight(M)=1×1.0=1.0, Weight(K)=1×0.8=0.8

Weight(A)=1×0.2=0.2, Weight(V)=1×0.6=0.6

Weight(Y)=1×0.2=0.2

Similarly, for sequence3 i.e. MKGVH, the weight

of each amino acid is evaluated as follows.

Weight(M)=1×1.0=1.0, Weight(K)=1×0.8=0.8

Weight(G)=1×0.6=0.6, Weight(V)=1×0.6=0.6

Weight(H)=1×0.6=0.6

For sequence4 i.e. MAKAS, the weight calcula-

tion of each amino acid is presented as follows.

Weight(M)=1×1.0=1.0, Weight(K)=1×0.2=0.2

Weight(S)=1×0.2=0.2,Weight(A)=1×0.2+1×0.2=0.4

For sequence5 i.e. MKGVH, the weight calcula-

tion of each amino acid is presented as follows.

Weight(M)=1×1.0=1.0, Weight(K)=1×0.8=0.8

Weight(G)=1×0.6=0.6, Weight(V)=1×0.6=0.6

Weight(H)=1×0.6=0.6

ANovelTechniqueofFeatureExtractionBasedonLocalandGlobalSimilarityMeasureforProteinClassification

221

Furthermore, these amino acids within the se-

quence share some structural similarity with each

other. Thus, encoding of these amino acids present

within the sequence is another important issue in or-

der to represent these amino acids as a feature vector.

Therefore, encoding method is presented in the sub-

sequent section.

2.3 Encoding of Protein Sequences

According to PAM (Dayhoff and Schwartz, 1978),

the amino acids belong to the six exchange groups.

This is because these amino acids within the

group exhibits high evolutionary similarity. The

Six-letter exchange groups are formally repre-

sented as: e

={H,R,K}, e

={D,E, N, Q}, e

={C},

={S,T,P,A,G}, e

={M,I,L,V } and e

={F,Y,W }.

For a given protein sequence1 MKGDH present in

Fig. 1, the amino acids M∈ e

, K∈ e

, G∈ e

, D∈ e

H∈ e

. The encoding of these amino acids is done by

ﬁnding the belongingness of each amino acid to the

speciﬁc group and assign the addition of weight val-

ues of amino acids to the speciﬁc group. The weight

value of amino acid M i.e. 1 is assign to the exchange

group e

, the amino acid K and H both belongs to the

exchange group e

, so the addition of their weight

values i.e. Weight(K) +Weight(H) = 0.8 + 0.6 = 1.4

is assign as an overall weight to the exchange group

. Similarly, the amino acids G and D belongs to

the exchange group e

and e

, so the weight values

of G i.e. 0.6 and D i.e. 0.2 is assign to the exchange

groups e

and e

. Thus, one can observe that none

of the amino acid from sequence1 belongs to the

exchange e

and e

so the weight values assign to

the exchange groups e

and e

is 0. Hence for se-

quence1 MKGDH, the feature vectors is obtained as

{(e

,1.4),(e

,0.2),(e

,0),(e

,0.6),(e

,1),(e

,0)}.

The feature vectors for remaining sequences shown

in Fig. 1 are determined in the similar manner and

presented in Table 2. The feature vectors generated

using the proposed method consider both the local

and global similarity and thus extract only 6 relevant

features corresponding to each protein sequence.

Therefore, it works effectively with any classiﬁcation

algorithm when applied with protein sequence data.

3 EXPERIMENTAL RESULTS

AND DISCUSSION

In this section, the experimentation is carried out to

investigate the performance of the proposed approach

on BLTA classiﬁer (Gray and Michel, 1992). All

Table 3: Data used in the experiments.

Name of Number of Minimum length Maximum length

superfamilies sequences of sequence of sequence

RAS 500 171 296

Globin 500 128 339

codes are written in the MATLAB computing envi-

ronment and tested on Intel(R) Xeon(R) E5 − 1607

Workstation PC. The data used for the experimen-

tal purpose are obtained from the International Pro-

tein Sequence Database (Barker et al., 2004), release

2012, in the Protein Information Resource (PIR). Ta-

ble 3, illustrate the information of the two superfam-

ilies used in the experimentation. In all the experi-

ments, the 10-fold cross validation test is performed

and corresponding results are reported subsequently.

3.1 Parameter Speciﬁcation

The proposed feature extraction approach is com-

pared with (Mansouri et al., 2008), (Bandyopadhyay,

2005), (Wang et al., 2001) using four parameters

i.e. Mean (M), Standard Deviation (SD), Classiﬁca-

tion Accuracy (CA) and Total Computational Time

(TCT). These four parameters are computed by each

approach only after the features are extracted corre-

sponding to all protein sequences and classiﬁcation is

performed with BLTA classiﬁer.

The Mean (M) is deﬁned as follows

M =

CCS

(3)

where CCS is the number of correctly classiﬁed se-

quences, n is the total number of sequences. The

mean determines the number of protein sequences are

correctly classiﬁed by each approach from the total

number of sequences.

The Standard Deviation (SD) is deﬁned as

SD =

n − 1

∑

i=1

− M)

(4)

where P

denote the i

protein sequence. The SD is

computed corresponding to each approach, it evalu-

ates the overall variation occur in the mean across ten

fold validation.

The total Classiﬁcation Accuracy (CA) is deﬁned as

CA = (M ±SD) × 100 (5)

The CA is computed for each approach which deter-

mines the total classiﬁcation accuracy by considering

the overall variation in mean and standard deviation

across ten fold cross validation.

The Total Computational Time (TCT) is deﬁned as

TCT = FET +CT (6)

BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms

222

Table 4: The Number of Features Extracted and the Neurons Required by all the Approaches in Each Layer.

Approaches Consideration Number of Number of Inputs Number of neurons

of Methods features extracted in Input Layer

Hidden Inhibition Output

Layer Layer Layer

Proposed approach Global and Local Similarity 6 18 500 500 2

Mansoori Local Similarity 6 18 500 500 2

Bandyopadhyay Global Similarity 20 60 500 500 2

Wang Global and Local Similarity 400 1200 500 500 2

where FET is the total feature extraction time, CT de-

notes the classiﬁcation time. The TCT is a sum of

time required in extracting the feature by a particu-

lar approach including the time required in classifying

protein sequences to the superfamilies.

3.2 Performance Comparison with

Other Approaches

The number of features extracted and the neurons

required by all the approaches while evaluating the

performance on BLTA classiﬁer are summarized in

Table 4. It can be observed from the table that,

the number of features extracted by the proposed

approach is similar to the number of features ex-

tracted by (Mansouri et al., 2008). But, the proposed

approach consider both the local and global simi-

larity measure whereas the (Mansouri et al., 2008)

only considers the local similarity measure to com-

pute the features corresponding to each sequence be-

longs to their respective superfamily. On the contrary,

the other approaches developed by (Bandyopadhyay,

2005), (Wang et al., 2001) extract 20 and 400 fea-

tures corresponding to each sequence and thus, it re-

sults in extraction of many irrelevant features for the

classiﬁcation of unknown protein sequence.To judge

the effectiveness of proposed approach, exhaustive re-

sults across ten fold cross validation along with the

performance comparison with three different exist-

ing feature extraction approaches (Mansouri et al.,

2008), (Bandyopadhyay, 2005), (Wang et al., 2001)

on BLTA classiﬁer by varying m-circle values is re-

ported in Table 5. Furthermore, of proposed ap-

proach . The four parameters i.e. Mean, Standard

Deviation, Classiﬁcation Accuracy and the Compu-

tational Time (seconds) corresponding to all the ap-

proaches is calculated by varying m-circle values of

BLTA classiﬁer. It is found that on protein data

Table 5: Comparison of Results in terms of Mean, Standard Deviations, Classiﬁcation Accuracy and Computational Time

with other Feature Extraction Approaches by Varying m-circle values of BLTA Classiﬁer is Reported.

Number of Proposed feature extraction approach (Mansouri et al., 2008) proposed by Mansoori

m-circle

Mean Standard Classiﬁcation Computational Mean Standard Classiﬁcation Computational

Deviation Accuracy Time (seconds) Deviation Accuracy Time (seconds)

2 0.9225 0.0450 92.26±4.50 6.87±0.11 0.8469 0.0100 84.70±1.00 7.63±0.17

4 0.9290 0.0424 92.90±4.25 6.55±0.24 0.8477 0.0108 84.78±1.08 7.21±0.06

8 0.9431 0.0352 94.32±3.52 6.54±0.10 0.8493 0.0106 84.94±1.06 7.23±0.14

16 0.9379 0.0385 93.79±3.86 6.48±0.09 0.8510 0.0084 85.10±0.85 7.15±0.07

32 0.9379 0.0385 93.79± 3.86 6.44±0.11 0.8510 0.0084 85.10±0.85 7.15±0.06

0.9379 0.0385 93.79±3.86 6.57±0.23 0.8542 0.0054 85.42±0.55 7.11±0.07

128 0.9379 0.0385 93.79±3.86 6.50±0.30 0.8542 0.0054 85.42±0.55 7.11±0.06

256 0.9379 0.0385 93.79±3.86 6.58±0.21 0.8542 0.0054 85.42±0.55 7.14±0.09

512 0.9379 0.0385 93.79±3.86 6.50±0.14 0.8542 0.0054 85.42±0.55 7.15±0.1

1024 0.9379 0.0385 93.79±3.86 6.41±0.08 0.8542 0.0054 85.42±0.55 7.13±0.14

Number of (Bandyopadhyay, 2005) proposed by Bandyopadhyay (Wang et al., 2001) proposed by Wang

m-circle

Mean Standard Classiﬁcation Computational Mean Standard Classiﬁcation Computational

Deviation Accuracy Time (seconds) Deviation Accuracy Time (seconds)

2 0.6734 0.0829 67.34±8.30 10.60±0.18 0.5141 0.0026 51.41±0.27 63.98±1.26

4 0.6744 0.0835 67.45±8.36 10.22±0.11 0.5141 0.0026 51.41±0.27 63.44±1.24

8 0.6748 0.0838 67.49±8.38 10.18±0.14 0.5141 0.0026 51.41±0.27 63.41±1.31

16 0.6750 0.0838 67.51±8.38 10.20±0.13 0.5141 0.0026 51.41±0.27 63.40±1.26

32 0.6750 0.0838 67.51±8.38 10.13±0.14 0.5141 0.0026 51.41±0.27 63.40±1.28

64 0.6750 0.0838 67.51±8.38 10.13±0.09 0.5141 0.0026 51.41±0.27 63.42±1.20

128 0.6750 0.0838 67.51±8.38 10.14±0.14 0.5141 0.0026 51.41±0.27 63.54±1.26

256 0.6750 0.0838 67.51±8.38 10.13±0.09 0.5141 0.0026 51.41±0.27 63.49±1.31

512 0.6750 0.0838 67.51±8.38 10.14±0.17 0.5141 0.0026 51.41±0.27 63.39±1.20

1024 0.6750 0.0838 67.51±8.38 10.16±0.09 0.5141 0.0026 51.41±0.27 63.40±1.22

ANovelTechniqueofFeatureExtractionBasedonLocalandGlobalSimilarityMeasureforProteinClassification

223

set, highest Classiﬁcation Accuracy achieved by pro-

posed technique is 94.32 ± 3.52 on m-circle value 8

with Computational Time 6.54±0.10 (s). Instead,

it attains 92.26 ± 4.50 as the minimum Classiﬁca-

tion Accuracy with Computational Time 6.87±0.11

(s) on m-circle value 2. On the other hand, method

given by (Mansouri et al., 2008) attains 85.42 ±

0.55 as maximum Classiﬁcation Accuracy for m-

circle values {64,...,1024} with Computational Time

varies from {7.11±0.06,..,7.15±0.1} (s) whereas it

gives the minimum Classiﬁcation Accuracy 84.70 ±

1.00 on m-circle value 2 with Computational Time

7.63±0.17 (s). On the contrary, the method pro-

posed by (Bandyopadhyay, 2005) achieves the best

Classiﬁcation Accuracy rate 67.51 ± 8.38 on m-

circle values {16,...,1024} with Computational Time

from {10.13±0.09,..,10.20±0.13} (s). Although, it

gives the worst Classiﬁcation Accuracy rate 67.34 ±

8.30 for m-circle value 2 with Computational Time

10.60±0.18 (s). The other method developed by

(Wang et al., 2001), exhibits 51.41 ± 0.27 as mini-

mum and maximum Classiﬁcation Accuracy rate for

all the values of m-circle with Computational Time

varies from {63.39±1.20,..,63.98±1.26}(s). More-

over, exhaustive results reported in Table 5, jus-

tify the signiﬁcance of proposed approach due to

the improvements in Classiﬁcation Accuracy rate as

well as in Computational Time when compared with

the methods proposed by (Mansouri et al., 2008),

(Bandyopadhyay, 2005), (Wang et al., 2001).

4 CONCLUSIONS

In this paper, a novel feature extraction approach is

proposed for classifying the protein sequences into

the superfamilies. The proposed approach compute

both the local and global similarity measures for ex-

tracting relevant features corresponding to each pro-

tein sequence. The global similarity measure is cal-

culated by considering probability of occurrence of

the positional variance of each amino acid among all

the sequences within the superfamily. However, the

local similarity measure is produced by evaluating a

weighting scheme (Karchin and Hughey, 1998) of the

global probability and then assigns the weighted prob-

ability of each amino acid to the six exchange groups

(Dayhoff and Schwartz, 1978). Finally, the 6 features

are extracted corresponding to each protein sequence

which is classiﬁed using Boolean-Like Training Al-

gorithm (BLTA) (Gray and Michel, 1992).

The experimental work is carried out on two su-

perfamilies Ras and Globin to probe the efﬁcacy of

the proposed approach on BLTA classiﬁer in compar-

ison with other approaches (Mansouri et al., 2008),

(Bandyopadhyay, 2005), (Wang et al., 2001). More-

over, the results are analyzed and reported in terms of

four parameters-Mean, Standard Deviation, Classiﬁ-

cation Accuracy and Computational Time with vari-

ation in m-circle values of BLTA classiﬁer. The ob-

servation can be drawn from the experimental re-

sults, that the proposed approach extract very limited

number of features in comparison with other meth-

ods. Therefore, it outperforms on the BLTA classi-

ﬁer and thus, achieves best Classiﬁcation Accuracy

94.32 ± 3.52 with Computational Time 6.54±0.10 (s)

on m-circle value 8. Hence, its performance is much

higher in comparison to other methods (Mansouri

et al., 2008), (Bandyopadhyay, 2005), (Wang et al.,

2001) in terms of Classiﬁcation Accuracy and Com-

putational Time.

REFERENCES

Bandyopadhyay, S. (2005). An efﬁcient technique for su-

perfamily classiﬁcation of amino acid sequences: fea-

ture extraction, fuzzy clustering and prototype selec-

tion. Fuzzy Sets and Systems, 152(1):5–16.

Barker, W., Garavelli, J., Huang, H., McGarvey, P., Orcutt,

B., G.Y.Srinivasarao, Xiao, C., Yeh, L., Ledley, R.,

Janda, J., F.Pfeiffer, H.W.Mewes, A. T., and Wu, C.

(2004). The protein information resource (pir). Nu-

cleic Acids Research, 28(1):41–44.

Dayhoff, M. and Schwartz, R. (1978). A model of evo-

lutionary change in proteins. In In Atlas of protein

sequence and structure. Citeseer.

Gray, D. and Michel, A. (1992). A training algorithm for bi-

nary feedforward neural networks. Neural Networks,

IEEE Transactions on, 3(2):176–194.

Iqbal, M. J., Faye, I., Samir, B. B., and Said, A. M. (2014).

Efﬁcient feature selection and classiﬁcation of protein

sequence data in bioinformatics. The Scientiﬁc World

Journal, 2014.

Karchin, R. and Hughey, R. (1998). Weighting hidden

markov models for maximum discrimination. Bioin-

formatics, 14(9):772–782.

Mansouri, E., A.M. Zou, S. Katebi, H. M. R. B., and Sadr,

A. (2008). Generating fuzzy rules for protein classiﬁ-

cation. Iranian Journal of Fuzzy Systems.

Solovyov, A. and Lipkin, W. I. (2013). Centroid based clus-

tering of high throughput sequencing reads based on

n-mer counts. BMC bioinformatics, 14(1):268.

Vergara, J. R. and Est

evez, P. A. (2014). A review of fea-

ture selection methods based on mutual information.

Neural Computing and Applications, 24(1):175–186.

Vipsita, S. and Rath, S. K. (2013). Two-stage approach for

protein superfamily classiﬁcation. Computational Bi-

ology Journal, 2013.

Wang, J., Ma, Q., Shasha, D., and Wu, C. (2001). New tech-

niques for extracting features from protein sequences.

IBM Systems Journal, 40(2):426–441.

BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms

224