BIO INFORMATICS AND BIO SCIENCE Research 2018-2019

Indumathy R and Uma Maheswari S

ABSTRACT

Evolutionary search algorithms are becoming an essential advantage in the algorithmic toolbox for solving multi-dimensional optimization problems in a wide range of bioinformatics problems such as Genome fragment assembly which is a NP-hard problem. In computational biology, one of the most challenging problems is to reconstruct the original DNA sequence from a huge number of fragments, each one being numerous hundred base-pairs (bps) long. Recently it has become common for the
researchers to apply the heuristic search algorithms to solve these kinds of complex problems with the help of combinatorial optimization. A genetic algorithm, the most well-known and representative evolutionary search technique has been the subject of the major part of such applications. Despite the fact that there have been different methods available in the literature, researchers are still facing difficulties in choosing the best method that could solve these problems efficiently and effectively. This paper aims to analyses the existing algorithms, evaluate them, point out the faults of these works and specify the emerging trend.

KEYWORDS

Computational Biology, Human Genome Project, Optimization Problem, DNA Fragment
Assembly, Genetic Algorithms

For More Details: https://wireilla.com/papers/ijbb/V2N2/2212ijbb05.pdf

Shekofeh Yaraghi and Mohammad Davarpanah Jazi

ABSTRACT
Gene expression microarray experiments produce datasets with numerous missing expression value, which can significantly affect the performance of statistical and machine learning algorithms. In this paper, we proposed a novel method based on the fuzzy clustering and the shortest path algorithm for measuring the semantic similarity on GO to estimate missing value to microarray gene expression. In this proposed method, missing values are imputed with values generated from cluster centers. Genes similarity in clustering process resolute based on both the GO structure information and, s property. We have applied the proposed method on two datasets with different percentages of missing values. The experimental results indicate that proposed method provides a higher accuracy of missing value estimation because the semantic similarity obtained by sp algorithm better correlates with the expression similarity than other node-based methods.

KEYWORDS

Missing Value; Semantic Similarity; Fuzzy Clustering; Microarray

For More Details: https://wireilla.com/papers/ijbb/V2N2/2212ijbb04.pdf

Sundarapandian Vaidyanathan

ABSTRACT
This paper solves the output regulation problem of Sprott-I chaotic system, which is one of the classical chaotic systems discovered by J.C. Sprott (1994). Explicitly, for the constant tracking problem, new state feedback control laws have been derived for regulating the output of the Sprott-I chaotic system. Our controller design has been carried out using the regulator equations of C.I. Byrnes and A. Isidori (1990). The output regulation of the Sprott-I chaotic system has important applications in many areas of Science and Engineering. Numerical simulations are shown to illustrate the effectiveness of the control schemes proposed in this paper for the output regulation of the Sprott-I chaotic system.

KEYWORDS

Chaos; Feedback Control; Sprott-I System; Nonlinear Control Systems; Output Regulation.

For More details: https://wireilla.com/papers/ijbb/V2N2/2212ijbb03.pdf

Ashish Bhangale, Navneet Manjhi and Jyoti Bharti

ABSTRACT

The gait signature is extracted directly from the evidence gathering process. This is possible by using a Fourier series to describe the motion of the upper leg and apply temporal evidence gathering techniques to extract the moving model from a sequence of images. The improved classification capability of the phase-weighted magnitude information is verified using statistical analysis of the separation of clusters in the feature space. Furthermore, the technique is shown to be able to handle high levels of occlusion, which is of especial importance in gait as the human body is self-occluding. As such, a new technique has been developed to automatically extract and describe a moving articulated shape, the human leg, and shown its potential in gait as a biometric.

KEYWORDS

Fourier series, sequence of images, classification, phase-weighted magnitude, statistical analysis, occulation, articulated shape, biometric, VHT, HVHT, GAVHT, GA, SHT, CCD, FS & HHT

For More details: https://wireilla.com/papers/ijbb/V2N2/2212ijbb02.pdf

S. Qatawneh, A. Alneaimi, Th. Rawashdeh, M. Muhairat, R. Qahwaji and S. Ipson

ABSTRACT

DNA-binding proteins are a class of proteins which have a specific or general affinity to DNA and include three important components: transcription factors; nucleases, and histones. DNA-binding  proteins also perform important roles in many types of cellular activities. In this paper we describe machine learning systems for the prediction of DNA- binding proteins where a Support Vector Machine and a Cascade Correlation Neural Network are optimized and then compared to determine the learning algorithm that achieves the best prediction performance. The information used for classification is derived from characteristics that include overall charge, patch size and amino acids composition. In total 121 DNA- binding proteins and 238 non-binding proteins are used to build and evaluate the system. For SVM using the ANOVA Kernel with Jack-knife evaluation, an accuracy of 86.7% has been achieved with 91.1% for sensitivity and 85.3% for specificity. For CCNN optimized over the entire dataset with Jack knife evaluation we report an accuracy of 75.4%, while the values of specificity and sensitivity achieved
were 72.3% and 82.6%, respectively.

KEYWORDS

DNA-Binding Proteins, Machine Learning, SVM, CCNN, Jack-knife Technique

For more Details: https://wireilla.com/papers/ijbb/V2N2/2212ijbb01.pdf

S. M. Anzar and P. S. Sathidevi

ABSTRACT
The efficiency of a multibiometric system can be improved by weighting the scores obtained from the degraded modalities in an appropriate manner. In this paper, we propose an efficient PSO (Particle Swarm Optimization) based integration weight optimization scheme using d-prime statistics to determine the optimal weight factors for the complementary modalities, under different noise conditions. Instead of treating the weight estimation process from an algebraic point of view, an attempt is made to consider the same from the principles of linear programming techniques. The performance of the proposed method is analysed in the context of fingerprint and voice biometrics using sum rule of fusion. The d-prime statistics of fingerprint and voice modalities are measured and the integration weights are computed as the ratio of these two statistics. The computed d-prime ratio is then optimized against the recognition accuracy. The optimizing parameter is estimated in the training/validation phase using Leave-One-Out Cross Validation (LOOCV) technique. Experimental studies show that the proposed method improves recognition rate under normal operating conditions and reduces the FAR (False Acceptance Rate) considerably even at extreme low SNR (Signal to Noise Ratio) conditions. The proposed biometric solution can be easily integrated into any multibiometric system with score level fusion. Moreover, it finds extremely useful in applications where there are less number of available training samples.

KEYWORDS

Multibiometric System, Integration Weight, d-prime Statistics, Particle Swarm Optimization, Robustness

For More Details: https://wireilla.com/papers/ijbb/V2N3/2312ijbb04.pdf

Subhomoi Borkotoky

Abstract

HIV integrase is a 32 kDa protein produced from the C-terminal portion of the Pol gene product, and is an attractive target for new anti-HIV drugs. Integrase is an enzyme produced by a retrovirus (such as HIV) that enables its genetic material to be integrated into the DNA of the infected cell. Raltegravir and Elvitegravir are two important drugs against integrase.

Keywords

Integrase, Chemsketch, Virtual screening.

For more Details: https://wireilla.com/papers/ijbb/V2N3/2312ijbb03.pdf

Srinivas Koppu, V. Madhu Viswanatham and Kamalakannan

ABSTRACT

This paper identifies the different kinds of methods, which helps human to communicate with computer. Traditionally the user interacting with computer via command line or GUI or common hand-held devices such as mouse and keyboard which is not feasible for a handicapped or a blind or a mute people. In order to interact with computer, the good interface required, the interface is sub part of human computer Interaction (HCI). HCI not only software design, it also concentrates on design of hardware devices. Describe the interaction between Brain and Computer based on EEG patterns. Brain generates EEG signal Based upon that system has to act and it has to perform tasks like human in real world. Thus the research work that is proposed would be to build a human to computer interaction through EEG signal. The system that allow the user to interact directly brain to computer based EEG signal and no need to use any input device to interact with any type of systems such are computer , TV’s. This way the computer
system can reduce the man work needed to feed information into the system by a large extent.

KEYWORDS

EEG signal, Brain Computer Interface (BCI), Human Computer Interaction (HCI), Speech Recognizer, Neural Networks, Cognitive Science.

For More Details: https://wireilla.com/papers/ijbb/V2N3/2312ijbb02.pdf

Guillaume Koum¹ and Josiane Etang

Abstract

A geo-ontology can be built around a Geographic Information System (GIS) and enriched with a firstorder logic language under the closed-world assumption (CWA). This extended system provides knowledge representation formalisms that are aimed to describe general conceptual information. They can also be used in the construction of the knowledge base of a reasoning tool. In this paper an environment is defined where a geo-ontology offers possibilities with a system that allows users to represent and reason about some aspects of the real world. With respect to the added intelligence; knowledge is preserved and enhanced. Knowledge enhancement is performed under the CWA in a subset of the first-order logic language. A geo-reasoning task is undertaken so that a GIS and a geo-ontology are integrated in what can correspond to a natural language processing system. The framework is implemented to demonstrate geo-ontology’s ability to enhance knowledge in malaria control domain.

Keywords

geographic information system, geo-ontology; closed-world assumption, geo-reasoning, malaria control

For more details: https://wireilla.com/papers/ijbb/V2N3/2312ijbb01.pdf

Shashank Rana, Shrikant Sharma and Raghvendar Singh

Abstract
Lactoferrin is an iron binding globular protein with antimicrobial activity was firstly isolated in bovine milk. Lactoferrin (LF) is structurally similar to the transferrins. So it is also known as lactotransferrin (LTF) is a globular multifunctional protein. Our work was on motif discovery by using OOPS modal of MEME (Multiple EM for Motif Elicitation) tool. The aim of motif discovery is to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. We have taken fifteen LTF AA sequencesfrom different resources.By analysis of these sequences, three motifs were retrieved. It is to be noted that all fifteen sequences contains all three motifs but start points of those are different. Each motif has 15 sites and 50 widths. On the bases of motif analysis, it is distinct that LTF retrieved from any milk recourse, have common conserved patterns.

Keywords

MEME, LTF, OOPS, Conserved pattern, MAST

For More Details: https://wireilla.com/papers/ijbb/V2N4/2412ijbb03.pdf