D. Anastassiou

Bio: D. Anastassiou is an academic researcher. The author has contributed to research in topics: Character (computing). The author has an hindex of 1, co-authored 1 publications receiving 436 citations.

Genomic signal processing

Abstract: Genomics is a highly cross-disciplinary field that creates paradigm shifts in such diverse areas as medicine and agriculture. It is believed that many significant scientific and technological endeavors in the 21st century will be related to the processing and interpretation of the vast information that is currently revealed from sequencing the genomes of many living organisms, including humans. Genomic information is digital in a very real sense; it is represented in the form of sequences of which each element can be one out of a finite number of entities. Such sequences, like DNA and proteins, have been mathematically represented by character strings, in which each character is a letter of an alphabet. In the case of DNA, the alphabet is size 4 and consists of the letters A, T, C and G; in the case of proteins, the size of the corresponding alphabet is 20. As the list of references shows, biomolecular sequence analysis has already been a major research topic among computer scientists, physicists, and mathematicians. The main reason that the field of signal processing does not yet have significant impact in the field is because it deals with numerical sequences rather than character strings. However, if we properly map a character string into, one or more numerical sequences, then digital signal processing (DSP) provides a set of novel and useful tools for solving highly relevant problems. For example, in the form of local texture, color spectrograms visually provide significant information about biomolecular sequences which facilitates understanding of local nature, structure, and function. Furthermore, both the magnitude and the phase of properly defined Fourier transforms can be used to predict important features like the location and certain properties of protein coding regions in DNA. Even the process of mapping DNA into proteins and the interdependence of the two kinds of sequences can be analyzed using simulations based on digital filtering. These and other DSP-based approaches result in alternative mathematical formulations and may provide improved computational techniques for the solution of useful problems in genomic information science and technology.

The role of signal-processing concepts in genomics and proteomics

Abstract: With the enormous amount of genomic and proteomic data that is available to us in the public domain, it is becoming increasingly important to be able to process this information in ways that are useful to humankind. Signal processing methods have played an important role in this context, some of which are reviewed in this paper. First we review the role of digital filtering techniques in gene identification. We then discuss the topic of long-range correlation between base pairs in DNA sequences. This correlation corresponds to a 1/f type of power spectrum. We also describe some of the recent applications of Fourier methods in the study of proteins. Finally we mention the role of Karhunen-Loeve like transforms in the interpretation of DNA microarray data for gene expression.

Signal Processing in Sequence Analysis: Advances in Eukaryotic Gene Prediction

Abstract: Genomic sequence processing has been an active area of research for the past two decades and has increasingly attracted the attention of digital signal processing researchers in recent years. A challenging open problem in deoxyribonucleic acid (DNA) sequence analysis is maximizing the prediction accuracy of eukaryotic gene locations and thereby protein coding regions. In this paper, DNA symbolic-to-numeric representations are presented and compared with existing techniques in terms of relative accuracy for the gene and exon prediction problem. Novel signal processing-based gene and exon prediction methods are then evaluated together with existing approaches at a nucleotide level using the Burset/Guigo1996, HMR195, and GENSCAN standard genomic datasets. A new technique for the recognition of acceptor splice sites is then proposed, which combines signal processing-based gene and exon prediction methods with an existing data-driven statistical method. By comparison with the acceptor splice site detection method used in the gene-finding program GENSCAN, the proposed DSP-statistical hybrid technique reveals a consistent reduction in false positives at different levels of sensitivity, averaging a 43% reduction when evaluated on the GENSCAN test set.

Manipulation, analysis and retrieval systems for audio signals

Abstract: Digital audio and especially music collections are becoming a major part of the average computer user experience. Large digital audio collections of sound effects are also used by the movie and animation industry. Research areas that utilize large audio collections include: Auditory Display, Bioacoustics, Computer Music, Forensics, and Music Cognition. In order to develop more sophisticated tools for interacting with large digital audio collections, research in Computer Audition algorithms and user interfaces is required. In this work a series of systems for manipulating, retrieving from, and analysing large collections of audio signals will be described. The foundation of these systems is the design of new and the application of existing algorithms for automatic audio content analysis. The results of the analysis are used to build novel 2D and 3D graphical user interfaces for browsing and interacting with audio signals and collections. The proposed systems are based on techniques from the fields of Signal Processing, Pattern Recognition, Information Retrieval, Visualization and Human Computer Interaction. All the proposed algorithms and interfaces are integrated under MARSYAS, a free software framework designed for rapid prototyping of computer audition research. In most cases the proposed algorithms have been evaluated and informed by conducting user studies. Contributions of this work to the area of Computer Audition include: a general multi-feature audio texture segmentation methodology, feature extraction from mp3 compressed data, automatic beat detection and analysis based on the Discrete Wavelet Transform and musical genre classification combining timbral, rhythmic and harmonic features. Novel graphical user interfaces developed in this work are various tools for browsing and visualizing large audio collections such as the Timbregram, TimbreSpace, GenreGram, and Enhanced Sound Editor.

Understanding Long-range Correlations in DNA Sequences

Abstract: In this paper, we review the literature on statistical long-range correlation in DNA sequences. We examine the current evidence for these correlations, and conclude that a mixture of many length scales (including some relatively long ones) in DNA sequences is responsible for the observed 1/f-like spectral component. We note the complexity of the correlation structure in DNA sequences. The observed complexity often makes it hard, or impossible, to decompose the sequence into a few statistically stationary regions. We suggest that, based on the complexity of DNA sequences, a fruitful approach to understand long-range correlation is to model duplication, and other rearrangement processes, in DNA sequences. One model, called ``expansion-modification system", contains only point duplication and point mutation. Though simplistic, this model is able to generate sequences with 1/f spectra. We emphasize the importance of DNA duplication in its contribution to the observed long-range correlation in DNA sequences.

Autoregressive modeling and feature analysis of DNA sequences

Abstract: A parametric signal processing approach for DNA sequence analysis based on autoregressive (AR) modeling is presented. AR model residual errors and AR model parameters are used as features. The AR residual error analysis indicates a high specificity of coding DNA sequences, while AR feature-based analysis helps distinguish between coding and noncoding DNA sequences. An AR model-based string searching algorithm is also proposed. The effect of several types of numerical mapping rules in the proposed method is demonstrated.