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The detection of many forms of periodicities in DNA sequences has been an active area of research in recent years. Most of the signal processing based methods have used the simple Voss mapping to map the symbolic DNA sequence into binary indicator ones before computing some form of the so-called DNA spectrum to locate these repeats. A key research issue that remains however open is whether the success...
Finding the control policies for intervention for large PBNs is a serious computational challenge. We study the effects of reduction mappings on the policy design. Our results suggest that for a specific class of network models, one can use the control policy designed on the reduced net work to approximate the control policy for the original model. The approximation fails only for intervention policies...
An important preliminary goal in learning biological network models from experimental data is to study the plausibility of different types of regulatory mechanisms in living organisms. In addition to providing important biological insight, the knowledge of abundance of some specific regulatory rules in nature helps the computational problems by restricting the space of possible models to be learned...
The detection of many forms of periodicities in DNA sequences has been an active area of research in recent years. Most of the signal processing based methods have primarily focussed on using the short-time discrete Fourier transform (ST-DFT) as the key tool in identifying such repeat sequences. In this paper, we propose to use different fast discrete transforms such as the discrete cosine transform...
Copy number alterations (CNA) affecting small portions of chromosomes are difficult to identify. Advances in microarray technology now allow very high resolution scans of large cohorts of samples but at the price of severe noise degradation. Our proposed genome alteration detection algorithm (GADA) has been shown to be a highly accurate and efficient approach to analyze a single array sample. In this...
High-throughput distributed data analysis based on clustered computing is gaining increasing importance in the field of computational biology. This paper describes a parallel programming approach and its software implementation using Message Passing Interface (MPI) to parallelize a computationally intensive algorithm for identifying cellular contexts. We report successful implementation on a 1,024...
Sequence alignment is the positioning of primary biological sequences, such as DNA, RNA and protein sequences, to identify regions of similarity in large databases. Common signal processing techniques include cross-correlations in time or frequency. However, these techniques can result in many misalignments when capturing a grouping in local or repetitive portions of the sequence. We propose a time-frequency...
Genome-wide high-throughput mass spectrometry has emerged as an important new source of data on biological systems. This technology yields global information about the proteins expressed by an organism; consequently, biological processes can be studied without prior assumptions about the proteins that are involved. A profile of up- and down-regulated proteins is obtained which can be used to discover...
External control of a gene regulatory network is used for the purpose of avoiding undesirable states, such as those associated with disease. Certain types of cancer therapies are given in cycles: each treatment is followed by a recovery phase. In a recovery phase, the side effects tend to gradually degrade. Here, an intervention strategy that simulates cyclic therapies is proposed. It is shown how...
It is well-known that the Fourier spectrum of a DNA protein-coding region exhibits an f = 1/3 peak. This is due to an unbalanced nucleotide distribution and open reading frame (ORF) positional bias that introduces a 3-base periodicity into the sequence. Until now, the f = 1/3 property has mainly been used to detect protein-coding regions, but in our paper, we use the f = 1/3 spectral height to detect...
We introduce a new algebraic framework for detecting spot failures in DNA microarrays. The technique leverages the theory of superimposed coding with iterative detection methods, and has the advantage of being constructive and of small implementation complexity, as opposed to existing approaches to DNA microarray error-control coding.
The following topics are dealt with: signal processing and statistical approaches for functional genomics problems; statistical inference of biological networks from experimental data; pattern recognition methods for functional genomics; control theory and systems theory techniques for systems biology; models for cellular metabolism and intercellular signaling; modeling and simulation of biological...
Recurrent copy number variations across multiple samples are increasingly used to identify the genes and the genomic locations that are statistically and biologically significant and correlated with certain diseases. In this paper, we evaluate the predictive power of copy number variations for detecting autism. We consider both recurrent copy number variations at one location and correlated recurrent...
This paper presents an efficient low-complexity genome assembly algorithm with the ability to detect bit errors (SNPs). A hashing function is used to reduce the complexity of the assembly process. The algorithm is tested against genomic sequences of different lengths. Its performance in terms of completeness, accuracy, and efficiency (time and space) is evaluated against Phrap, a well-known sequence...
The most accurate methods for RNA secondary structure prediction simultaneously predict the common structure and alignment among multiple homologs. In addition to dynamic programming, practical algorithms utilize heuristics to restrict the search space and further reduce time and memory requirements. This work is directed toward improving these heuristics in order to reduce computation without a compromise...
This paper introduces a simple and robust method for the classification of significantly expressed genes in high throughput microarray measurements of a cellpsilas transcriptome. The technique has its origins in PCA-based fault detection and isolation (FDI) systems engineering. PCA-FDI is a data-driven procedure that can be used to isolate gene expression profiles associated with anomalous cell function...
Discrete classification is fundamental in GSP applications. In a previous publication, we provided analytical expressions for moments of the sampling distribution of the true error, as well as of resubstitution and leave-one-out error estimators, and their correlation with the true error, for the discrete histogram rule. When the number of samples or the total number of quantization levels is large,...
To elucidate biological functions from gene expression profiles, gene set enrichment analysis (GSEA) is widely applied against sets of predefined genes that may yield crucial clues to their functional themes or regulatory information. However, gene list derived from array based chromatin-immunoprecipitation (ChIP-chip) experiments, where all genes with one or more binding sites of a given protein,...
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