Enhancement of a Novel Method for Mutational
Disease Prediction using Bioinformatics
Techniques and Backpropagation Algorithm
Ayad Ghany Ismaeel, Anar Auda Ablahad
Abstract— The noval method for mutational disease prediction using bioinformatics tools and datasets for diagnosis the malignant
mutations with powerful Artificial Neural Network (Backpropagation Network) for classifying these malignant mutations are related to
gene(s) (e.g. BRCA1 and BRCA2) cause a disease (breast cancer). This noval method didn’t take in consideration just like adopted for
dealing, analyzing and treat the gene sequences for extracting useful information from the sequence, also exceeded the environment
factors which play important roles in deciding and calculating some of genes features in order to view its functional parts and relations to
diseases. This paper is proposed an enhancement of a novel method as a first way for diagnosis and prediction the disease by mutations
considering and introducing multi other features show the alternations, changes in the environment as well as genes, comparing
sequences to gain information about the structure/function of a query sequence, also proposing optimal and more accurate system for
classification and dealing with specific disorder using backpropagation with mean square rate 0.000000001.
Index Terms— Homology sequence, GC% content & AT% content, Bioinformatics, Backpropagation Network, BLAST, DNA Sequence,
Protein Sequence

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1 INTRODUCTION

C

ANCER is a dynamic disease, it has been estimated that
a new genetic alteration occurs in one out of 10000 tumor
cells at each cell division. This leads to an increasing
number of genetic aberrations in cancer cells, some of which
result in altered gene expression. These changes in gene expression are at least partially responsible for the characteristic
of a tumor or tumor-type. Genome-wide gene expression profiles provide detailed “tumor signatures” and can potentially
be used for molecular diagnosis and classification of tumors
[1]. Cancer is a complex family of diseases, from the view of
molecular biology; cancer is a genetic disease resulting from
abnormal gene expression. This alternation of gene expression
could be resulting from DNA instability, such as translocation,
amplification, deletion or point mutations [2]. Breast cancer
remains the most frequently diagnosed and the second
most lethal cancer for women in the world[3]
The diagnosis and prediction of the cancer are important
measures to avoid this disease specially for those which they
have in their family [4], and that represented in novel method
for mutational disease prediction as shown in Fig. 1, this noval
method based on two approaches the first for diagnosis nucleotide and its protein mutations through bioinformatics tools.
————————————————

 Ayad Ghany Ismaeel is currently pursuing Professor Assistant, PhD computer science in department of Information System Engineering, Erbil
Technical College- Hawler Polytechnic University (previous FTE- Erbil),
Iraq. PH-009647703580299. E-mail: dr_a_gh_i@yahoo.com Alternative
dr.ayad.ghany.ismaeel@gmail.com
 Anar Auda Ablahad is currently MSc student at research stage in computer science at Zakho University, its member in electrical and computer eng.
Duhok-IRAQ. E-mail: anar_alkasyonan@yahoo.com

And the second classify gene mutations (that have been diagnosed) are related to breast cancer or other cancer tissues using backpropagation neural network [9].

Fig. 1. Flowchart for main tasks of a novel method [9]
.

Important of Genome/Gene sequence in obtaining a blueprint–DNA directs all the instructions needed for cell development and function, DNA underlies almost every aspect of
human health, in function and disfunction, to study gene expression in a specific tissue, organ or tumor, to study human
variation, studying how humans relate to other organisms,
finding correlations how genome information relates to development of cancer, susceptibility to certain diseases and drug
metabolism is great challenge with providing tools for viewing, analyzing, discovering features and genes properties [5].

GC content is different in genes compared to the whole genome. In genes, the GC content is centered a round to 45-50%,
and it is more uniformly distributed than in the genome. The
genome is mostly 38% GC with its distribution skewed to the
left. As a consequence, regions of high GC content (62-68%)
have higher relative gene density than regions of lower GC
content [6]. And also Mutator genes, especially those with
dominant effects on the mutation spectra, are biased towards
either GC or AT richness [7].

The motivation overcome the drawbacks to reach an enhancement of a novel method, considering and introducing
multi other features show the alternations, changes in the environment as well as genes, comparing sequences to gain information about the structure/function of a query sequence,
also proposing optimal and more accurate system for classification and dealing with specific disorder.

Homology search through The BLAST program works on
the principle that regions of similarity are likely to contain
strongly conserved, indel-resistant segments. These areas of
strong conservation show up as non-gapped blocks in an
alignment. Recent versions are also capable of producing
gapped alignments, but they still build these gapped alignments up from non-gapped segments. This means that BLAST
is most likely to fail to detect a homologous sequence where
indels are scattered liberally and regularly throughout: in other words, highly divergent sequences may be missed. In a
pairwise search, a query sequence is compared to a database
sequence, yielding a score that indicates the likelihood of homology. This comparison is repeated for every sequence in the
database and high-scoring hits are reported [8].

The collection datasets relate to gene caused a disease is very
important stage in the proposed of enhancement a noval
method, because the noval method didn't care about it, however as show it can play an important role for mutational disease predication, as referring to section 1.

So there is multi features the noval method for mutational
disease prediction must be taken in consideration like adopted
for dealing, analyzing and treat the gene sequences for extracting useful information from the sequence, also the environment factor play important roles in deciding and calculating
some of genes features in order to view its functional part and
relations to diseases. By using bioinformatics tools and datasets with powerful Artificial Neural Network ANN (BackPorpagation Network BPN) structure can make improvement/enhancement of a novel method.

2 RELATED WORK
Ayad Ghany Ismaeel, Anar A. Ablahad [9] developed a novel
method for disease mutation prediction and classification
based on two approaches which are first: diagnosis nucleotide
and its protein mutations through bioinformatics tools (fasta,
BioEdit and ClustalW). And second: classifying (BRCA1 and
BRCA2) gene mutations whether these mutations (malignant)
related to breast cancer or other cancer tissues using
backpropagation neural network. The result of the paper
showed reliable and accurate results with actual results of mutations diagnosis and classification.
Novel method reached to predict completely and obtained
best results but there are some points not taken into consideration although are important for this noval method. Like the
dataset adopted in the method and the normal gene used for
comparison, also gene sequence analysis and gene features
which play important roles in dealing and dis-covering gene
actions and abilities.

3 PROPOSED AN ENHANCEMENT OF A NOVEL METHOD

This proposal of enhancement a noval method focus on the
important feature in Bioinformatics tools which is BLAST to
reach to best Homology sequence related to the environment
which is different from region to another.
The algorithm of main tasks for enhancement a novel
method of maturational disease prediction, which based on
overcome the drawbacks at novel method [9], this enhancement will refer to it in red as follow:
Algorithm of main tasks for Enhancement a Novel method
Input: DNA sequence (normal and person’s gene sequence)
Output: Classification of tumor gene mutations (for the patient)

BEGIN
Step 1: collect datasets for the project
Step 2: search normal Homology Sequences at BLAST
Step 3: calculate GC% and AT%
Step 4: Find the percentage of normal gene homology e.g. at NCBI
Step 5: if GC% satisfies 38% then
Depend it
Else find another normal Homology seguence form another
Database like EBI, Ensemble, etc and return to step 2
Step 6: Get normal gene sequences
Step 7: CREATE fasta file;
SET normal and Person’s gene sequence to fasta file
Step8: SET fasta file to clustalW for alignment; Display alignment result
Step9: Simulate ANN (BPN) of optimal (new) structure.
Step10: CREATE training dataset file of Breast cancer mutations
Input DNA gene sequence (of the patient);
Display BPN results;
If matching found then
Display”highly risk of breast cancer”
Else Disply no risk "Normal"
END

4 EXPERIMENTAL RESULTS
4.1 Bioinformatics Techniques
Implement the proposed enhancement of noval method will
apply to diagnosis the mutations at patient gene and its protein is malignant or not.
In order to know how to access and get helpful bioinformatics tools this modified approach to reach an enhancement
of a novel method consider the collection dataset related to
gene caused the disease is very important stage in the proposed of enhancement a noval method, because the noval
method which not care about it for mutational disease predication, so in this stage will consider additional parts as follow:
1. The data set adapted to depend on the environment,
as it plays roles in alternations and differences in
genes shapes also genes mutations.
2. The researchers or biologists adopt a normal gene for
analysis and comparison should be compatible with
the environment of the study. The improvement of
this point can be shown in Fig. 2. The normal gene
can be obtained from NCBI or EBI, Ensemble, COSMIC. and each of these databases provide different
form of normal gene adopted in the local database
but when search for homology sequences at NCBI,
the 100% match found between the two normal gene
sequences. When normal gene of COSMIC is entered
to NCBI at blast it shows the RefSeq (normal) as the
first one in homology list with max identities=100.

Fig. 3. Gene bases positions.

Fig. 4. Gene ratios of A, G ,T and C.

Fig. 5. Calculating GC% and AT%
.

Fig. 2. Adopt a normal gene for analysis and comparison.

3. Calculating GC%content and AT% content for gene
stability and ability of doing amplification can be
done by using BioEdit package to determine ratios
and some amino acid and protein features. Needed to
calculate GC% and AT% of normal BRCA1 gene to
determine is the genome around 38% GC to depend
it otherwise search about another normal BRCA1
gene form another database, shown in Fig. 3, Fig. 4
and Fig. 5 respectively.

When reaching to this stage will need to complete another
important bioinformatics tool which is ClustalW, that tool can
work with fasta file for diagnosis there are malignant mutations or not, as referring to it in noval method [9].

4.2 Backpropagation Algorithm
To predict these malignant mutations which obtained in subsection 4.1 (using bioinformatics tools) are related to BRCA1
and BRCA2 (which caused breast cancer) based on noval
method need training the BPN by all malignant mutations
(which are 5 related to BRCA1 and 4 related to BRCA2) then
used to classify these malignant mutations which caused
breast cancer [9].

The new modification in the BPN, i.e. get a new structure
which can be summarized as the optimum and accurate result
with best performance comparing to the previous novel method, the BPN is most commonly used in medical research.
BPNs where the signals travel in one direction from input
neuron to an output neuron without returning to its source,
neural network is a sorted topology which include node and
weighted connections. Each layer inside neural network will
be connected by their weight connector; the new BPN consists
of (input layer, at least one hidden layer and output layer) as
shown in Fig. 6 [6].

Fig. 8 reveals plot of the important element of training new
structure of feed-forward BPN, which is the plot of performance.

Fig. 6 New structre of BPN

That means the enhancement of a novel method will be
used a feed forward back-propagation network as popular
model in neural network, the new structure of BPN consist of:
1. One input layer with 10 nodes at the input layer
2. One hidden layer with 4 nodes at hidden layer
3. One output layer with one node at the output layer.
Using MATLAB R2010a on PC type Core i3 for neural
network toolbox because it contains various functions the classification of malignant mutations for breast cancer was developed successfully using new structure of feed-forward BP
neural network to obtain an optimal classifier for prediction a
breast cancer or any other disease with mean square error
0.000000001, as shown in Fig. 7.

Fig. 8. Plot performance of trainng a new structure of BPN
.

4.3 Discussion the Results
Table 1 reveals comparing the results of implementing an enhancement of a novel method with the noval method (the
origin) for mutational disease prediction [9].
TABLE 1
REVEALS COMPARISON AN ENHANCEMENT OF A NOVEL
METHOD WITH NOVEL METHOD FOR MUTATIONAL DISEASE
PREDICTION
The Feature
Datasets adopted is depended of the environment, as it play roles in
alternations and differences
in genes shapes and genes
mutations.
Normal gene can be obtained
different
form
adopted to the local database (based on their environment)
Considered the GC% content and AT% content for
gene
Mean square rate.

Fig. 7. Training a new structure of
.

feed-forward BPN

Optimize the classifying
BPN.

Enancement
of a Novel
Method
Yes

No

Yes

No

Yes

No

Mean square
rate
0.000000001
Yes

Mean
square
rate 0.0000001

Novel Method
(the origin )[9]

No

5 CONCLUSTIONS

REFERENCES

The important conclusions which obtained from implementing the proposed enhancement of novel method for mutational disease prediction are:

[1] Momiao Xiong, Li Jin, Wuju Li, and Eric Boerwinkle, “Computational
Methods for Gene Expression-Based Tumor Classification”, Quebecor, Vol.
29, No. 6 (2000).
[2] Jeffrey J.P. Tsai, J.G. Chang, S.H. Shih, “A Framework for Cancer-related
Genes Mining over the Internet”, BIBE’03 IEEE (2003).
[3] R. El Hamdi, M. Njah, M. Chtourou, “An Evolutionary Neuro-Fuzzy Approach to Breast Cancer Diagnosis”, 978-1-4244-6588-0, IEEE (2010).
[4] Marco Wiltgen,” Structural Bioinformatics: From the Sequence to Structure
and Function”, Bentham Science Publishers Ltd, © 2009.
[5] Jutta Marzillier, “Human Genome Sequencing and Its Impact on Science and
Medicine”, November 16th, 2011.
[6] Libby MacKinnon, “Characteristics of the Human Genome”, CSE, 2007.
[7] Hao Wu, Zhang Zhang, Songnian Hu, “On the molecular mechanism of GC
content variation among eubacteria genomes”, BioMed Central, Wuet al. Biology Direct 2012.
[8] Ian Holmes, “Sequence homology search tools on the world wide web”,
Berkeley Drosophila Genome Project, Berkeley, 2000.
[9] Ayad Ghany Ismaeel, Anar Auda Ablahad, “Novel Method for Mutational
Disease Prediction using Bioinformatics Techniques and Backpropopagation
Algorithm”, IRACST- Engineering Science and Technology: An International Journal Vol. 3, No. 1, (ESTIJ) 2013, pages 150-156.

1.

The enhancement of a novel method will base on collection datasets which related to environment, so that
means the results of diagnosis using bioinformatics
tools and then the classifying of BPN, i.e. the prediction is more expressive for the region and will be better as this as have seen from the above impact on the
results and how it could affect the environment.

2.

This enhancement of a novel method based on calculating the GC% content and AT% content for selecting
the a normal gene caused the disease and make
GC%= 38% as measure to depend the normal Homology sequence which is playing important role to select
the appropriate normal Homology sequence related
to environment.

3.

Offers an automatic and friendly diagnosis system for
detecting malignant mutation and pre-predict of
breast cancer as shown in Table 1, i.e. can use by any
researcher or patient who needed to test malignant
mutations at gene(s) which caused any cancer known
its malignant mutations.

4.

This Enhancement model of classification malignant
mutations for breast cancer was developed successfully using new structure of feed-forward backpropagation neural network, i.e. obtained a best classifier for prediction a breast cancer or any other disease disease known its malignant mutations with
mean square error 0.000000001.

