Development of efficient gene prediction algorithms is one of the fundamental efforts in gene prediction study in the area of genomics. In genomic signal processing the basic step of the identification of protein codi...Development of efficient gene prediction algorithms is one of the fundamental efforts in gene prediction study in the area of genomics. In genomic signal processing the basic step of the identification of protein coding regions in DNA sequences is based on the period-3 property exhibited by nucleotides in exons. Several approaches based on signal processing tools and numerical representations have been applied to solve this problem, trying to achieve more accurate predictions. This paper presents a new indicator sequence based on amino acid sequence, called as aminoacid indicator sequence, derived from DNA string that uses the existing signal processing based time-domain and frequency domain methods to predict these regions within the billions long DNA sequence of eukaryotic cells which reduces the computational load by one-third. It is known that each triplet of bases, called as codon, instructs the cell machinery to synthesize an amino acid. The codon sequence therefore uniquely identifies an amino acid sequence which defines a protein. Thus the protein coding region is attributed by the codons in amino acid sequence. This property is used for detection of period-3 regions using amino acid sequence. Physico-chemical properties of amino acids are used for numerical representation. Various accuracy measures such as exonic peaks, discriminating factor, sensitivity, specificity, miss rate, wrong rate and approximate correlation are used to demonstrate the efficacy of the proposed predictor. The proposed method is validated on various organisms using the standard data-set HMR195, Burset and Guigo and KEGG. The simulation result shows that the proposed method is an effective approach for protein coding prediction.展开更多
Novel fault-tolerant architectures for bit-parallel polynomial basis multiplier over GF（2^m）, which can correct the erroneous outputs using linear code, are presented. A parity prediction circuit based on the code g...Novel fault-tolerant architectures for bit-parallel polynomial basis multiplier over GF（2^m）, which can correct the erroneous outputs using linear code, are presented. A parity prediction circuit based on the code generator polynomial that leads lower space overhead has been designed. For bit-parallel architectures, the Moreover, there is incorporation of space overhead only marginal time error-correction is about 11%. overhead due to capability that amounts to 3.5% in case of the bit-parallel multiplier. Unlike the existing concurrent error correction （CEC） multipliers or triple modular redundancy （TMR） techniques for single error correction, the proposed architectures have multiple error-correcting capabilities.展开更多
The hydrophobic effect is the major factor that drives a protein molecule towards folding and to a great degree the stability of protein structures. Therefore the knowledge of hydrophobic regions and its prediction is...The hydrophobic effect is the major factor that drives a protein molecule towards folding and to a great degree the stability of protein structures. Therefore the knowledge of hydrophobic regions and its prediction is of great help in understanding the structure and function of the protein. Hence determination of membrane buried region is a computationally intensive task in bioinformatics. Several prediction methods have been reported but there are some deficiencies in prediction accuracy and adaptability of these methods. Of these proteins that are found embedded in cellular membranes, called as membrane proteins, are of particular importance because they form targets for over 60% of drugs on the market. 20-30% of all the proteins in any organism are membrane proteins. Thus transmembrane protein plays important role in the life activity of the cells. Hence prediction of membrane buried segments in transmembrane proteins is of particular importance. In this paper we have proposed signal processing algorithms based on digital filter for prediction of hydrophobic regions in the transmembrane proteins and found improved prediction efficiency than the existing methods. Hydrophobic regions are extracted by assigning physico-chemical parameter such as hydrophobicity and hydration energy index to each amino acid residue and the resulting numerical representation of the protein is subjected to digital low pass filter. The proposed method is validated on transmembrane proteins using Orientation of Proteins in Membranes (OPM) dataset with various prediction measures and found better prediction accuracy than the existing methods.展开更多
Microarray data is inherently noisy due to the noise contaminated from various sources during the preparation of microarray slide and thus it greatly affects the accuracy of the gene expression. How to eliminate the e...Microarray data is inherently noisy due to the noise contaminated from various sources during the preparation of microarray slide and thus it greatly affects the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Efficient denoising is often a necessary and the first step to be taken before the image data is analyzed to compensate for data corruption and for effective utilization for these data. Hence preprocessing of microarray image is an essential to eliminate the background noise in order to enhance the image quality and effective quantification. Existing denoising techniques based on transformed domain have been utilized for microarray noise reduction with their own limitations. The objective of this paper is to introduce novel preprocessing techniques such as optimized spatial resolution (OSR) and spatial domain filtering (SDF) for reduction of noise from microarray data and reduction of error during quantification process for estimating the microarray spots accurately to determine expression level of genes. Besides combined optimized spatial resolution and spatial filtering is proposed and found improved denoising of microarray data with effective quantification of spots. The proposed method has been validated in microarray images of gene expression profiles of Myeloid Leukemia using Stanford Microarray Database with various quality measures such as signal to noise ratio, peak signal to noise ratio, image fidelity, structural content, absolute average difference and correlation quality. It was observed by quantitative analysis that the proposed technique is more efficient for denoising the microarray image which enables to make it suitable for effective quantification.展开更多
文摘Development of efficient gene prediction algorithms is one of the fundamental efforts in gene prediction study in the area of genomics. In genomic signal processing the basic step of the identification of protein coding regions in DNA sequences is based on the period-3 property exhibited by nucleotides in exons. Several approaches based on signal processing tools and numerical representations have been applied to solve this problem, trying to achieve more accurate predictions. This paper presents a new indicator sequence based on amino acid sequence, called as aminoacid indicator sequence, derived from DNA string that uses the existing signal processing based time-domain and frequency domain methods to predict these regions within the billions long DNA sequence of eukaryotic cells which reduces the computational load by one-third. It is known that each triplet of bases, called as codon, instructs the cell machinery to synthesize an amino acid. The codon sequence therefore uniquely identifies an amino acid sequence which defines a protein. Thus the protein coding region is attributed by the codons in amino acid sequence. This property is used for detection of period-3 regions using amino acid sequence. Physico-chemical properties of amino acids are used for numerical representation. Various accuracy measures such as exonic peaks, discriminating factor, sensitivity, specificity, miss rate, wrong rate and approximate correlation are used to demonstrate the efficacy of the proposed predictor. The proposed method is validated on various organisms using the standard data-set HMR195, Burset and Guigo and KEGG. The simulation result shows that the proposed method is an effective approach for protein coding prediction.
基金supported by the National Science Council of the Republic of China,Taiwan,under Grant No.NSC 98-2221-E-262-007
文摘Novel fault-tolerant architectures for bit-parallel polynomial basis multiplier over GF（2^m）, which can correct the erroneous outputs using linear code, are presented. A parity prediction circuit based on the code generator polynomial that leads lower space overhead has been designed. For bit-parallel architectures, the Moreover, there is incorporation of space overhead only marginal time error-correction is about 11%. overhead due to capability that amounts to 3.5% in case of the bit-parallel multiplier. Unlike the existing concurrent error correction （CEC） multipliers or triple modular redundancy （TMR） techniques for single error correction, the proposed architectures have multiple error-correcting capabilities.
文摘The hydrophobic effect is the major factor that drives a protein molecule towards folding and to a great degree the stability of protein structures. Therefore the knowledge of hydrophobic regions and its prediction is of great help in understanding the structure and function of the protein. Hence determination of membrane buried region is a computationally intensive task in bioinformatics. Several prediction methods have been reported but there are some deficiencies in prediction accuracy and adaptability of these methods. Of these proteins that are found embedded in cellular membranes, called as membrane proteins, are of particular importance because they form targets for over 60% of drugs on the market. 20-30% of all the proteins in any organism are membrane proteins. Thus transmembrane protein plays important role in the life activity of the cells. Hence prediction of membrane buried segments in transmembrane proteins is of particular importance. In this paper we have proposed signal processing algorithms based on digital filter for prediction of hydrophobic regions in the transmembrane proteins and found improved prediction efficiency than the existing methods. Hydrophobic regions are extracted by assigning physico-chemical parameter such as hydrophobicity and hydration energy index to each amino acid residue and the resulting numerical representation of the protein is subjected to digital low pass filter. The proposed method is validated on transmembrane proteins using Orientation of Proteins in Membranes (OPM) dataset with various prediction measures and found better prediction accuracy than the existing methods.
文摘Microarray data is inherently noisy due to the noise contaminated from various sources during the preparation of microarray slide and thus it greatly affects the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Efficient denoising is often a necessary and the first step to be taken before the image data is analyzed to compensate for data corruption and for effective utilization for these data. Hence preprocessing of microarray image is an essential to eliminate the background noise in order to enhance the image quality and effective quantification. Existing denoising techniques based on transformed domain have been utilized for microarray noise reduction with their own limitations. The objective of this paper is to introduce novel preprocessing techniques such as optimized spatial resolution (OSR) and spatial domain filtering (SDF) for reduction of noise from microarray data and reduction of error during quantification process for estimating the microarray spots accurately to determine expression level of genes. Besides combined optimized spatial resolution and spatial filtering is proposed and found improved denoising of microarray data with effective quantification of spots. The proposed method has been validated in microarray images of gene expression profiles of Myeloid Leukemia using Stanford Microarray Database with various quality measures such as signal to noise ratio, peak signal to noise ratio, image fidelity, structural content, absolute average difference and correlation quality. It was observed by quantitative analysis that the proposed technique is more efficient for denoising the microarray image which enables to make it suitable for effective quantification.
