“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH RECOGNITION

Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays an important role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.

Effect of Blending Techniques on the Curing of Elastomer Blends

Compounding of styrene-butadiene copolymer/polybutadiene , natural rubber/ ethylene-propylene-diene terpolymer and natural rubber/butadiene-acrylonitrile copolymer blends was done in three different ways and their curing behaviour and the tensile properties of the es are compared.

Generation of Variability for Salt Tolerance in Rice Using Tissue Culture Techniques

The study deals with the generation of variability for salt tolerance in rice using tissue culture techniques. Rice is the staple food of more than half of the world’s population. The management of drought, salinity and acidity in soils are all energy intensive agricultural practices. The Genetic variability is the basis of crop improvement. Somaclonal and androclonal variation can be effectively used for this purpose. In the present study, eight isozymes were studied and esterase and isocitric dehydrogenase was found to have varietal specific, developmental stage specific and stress specific banding pattern in rice. Under salt stress thickness of bands and enzyme activity showed changes. Pokkali, a moderately salt tolerant variety, had a specific band 7, which was present only in this variety and showed slight changes under stress. This band was faint in tillering and flowering stage .Based on the results obtained in the present study it is suggested that esterase could possibly be used as an isozyme marker for salt tolerance in rice. Varietal differences and stage specific variations could be detected using esterase and isocitric dehydrogenase . Moreover somaclonal and androclonal variation could be effectively detected using isozyme markers.

Comparison Study of Pattern Synthesis Techniques Using Neural Networks

In this paper, a comparison study among three neuralnetwork algorithms for the synthesis of array patterns is presented. The neural networks are used to estimate the array elements' excitations for an arbitrary pattern. The architecture of the neural networks is discussed and simulation results are presented. Two new neural networks, based on radial basis functions (RBFs) and wavelet neural networks (WNNs), are introduced. The proposed networks offer a more efficient synthesis procedure, as compared to other available techniques

Studies on Soft Bycatch Reduction Devices for Selective Trawling

Trawling, though an efficient method of fishing, is known to be one of the most non-selective methods of fish capture. The bulk of the wild caught penaeid shrimps landed in India are caught by trawling.In addition to shrimps, the trawler fleet also catches considerable amount of non-shrimp resources. The term bycatch means that portion of the catch other than target species caught while fishing, which are either retained or discarded. Bycatch discards is a serious problem leading to the depletion of the resources and negative impacts on biodiversity. In order to minimize this problem, trawling has to be made more selective by incorporating Bycatch Reduction Devices (BRDs). There are several advantages in using BRDs in shrimp trawling. BRDs reduce the negative impacts of shrimp trawling on marine community. Fishers could benefit economically from higher catch value due to improved catch quality, shorter sorting time, lower fuel costs, and longer tow duration. Adoption of BRDs by fishers would forestall criticism by conservation groups against trawling.

Design Techniques for Sigmadelta based ADC for Wireless Applications

Analog-to digital Converters (ADC) have an important impact on the overall performance of signal processing system. This research is to explore efficient techniques for the design of sigma-delta ADC,specially for multi-standard wireless tranceivers. In particular, the aim is to develop novel models and algorithms to address this problem and to implement software tools which are avle to assist the designer's decisions in the system-level exploration phase. To this end, this thesis presents a framework of techniques to design sigma-delta analog to digital converters.A2-2-2 reconfigurable sigma-delta modulator is proposed which can meet the design specifications of the three wireless communication standards namely GSM,WCDMA and WLAN. A sigma-delta modulator design tool is developed using the Graphical User Interface Development Environment (GUIDE) In MATLAB.Genetic Algorithm(GA) based search method is introduced to find the optimum value of the scaling coefficients and to maximize the dynamic range in a sigma-delta modulator.

Development of active microwave imaging techniques for applications in mammography

Breast cancer is the most common non - skin malignancy in women and a leading cause of female morality. A potentially important strategy for reducing this menace is the detection at an early stage . The invention of non-invasive and non-ionizing microwave technique, to reveal the internal structure of biological objects was a break through in the field of medical diagnostics. Electrical properties of biological tissues and their interaction with electromagmetic waves have direct impact on human life. This thesis focuses on theoretical and experimental investigations of active microwave imaging techniques for breast cancer detection.

Optimization of Preparation Techniques and Dielectric Study of Polyanilines in the Microwave & High Frequency Field

The thesis deals with the preparation and dielectric characterization of Poly aniline and its analogues in ISM band frequency of 2-4 GHz that includes part of the microwave region (300 MHz to 300 GHz) of the electromagnetic spectrum and an initial dielectric study in the high frequency [O.05MHz-13 MHz]. PolyaniIine has been synthesized by an in situ doping reaction under different temperature and in the presence of inorganic dopants such as HCl H2S04, HN03, HCl04 and organic dopants such as camphorsulphonic acid [CSA], toluenesulphonic acid {TSA) and naphthalenesulphonic acid [NSA]. The variation in dielectric properties with change in reaction temperature, dopants and frequency has been studied. The effect of codopants and microemulsions on the dielectric properties has also been studied in the ISM band. The ISM band of frequencies (2-4 GHz) is of great utility in Industrial, Scientific and Medical (ISM) applications. Microwave heating is a very efficient method of heating dielectric materials and is extensively used in industrial as well as household heating applications.