Lun Huang

A Novel gene detection method based on period-3 property

Processing of biomolecular sequences using communication theory techniques provides powerful approaches for solving highly relevant problems in bioinformatics by properly mapping character strings into numerical sequences. We provide an optimized procedure for predicting protein-coding regions in DNA sequences based on the period-3 property of coding region. We present a digital correlating and filtering approach in the process of predicting these regions, and find out their locations by using the magnitude of the output sequence. These approaches result in improved computational techniques for the solution of useful problems in genomic information science and technology.

Mimo/-space time codes for Polynomial Phase modulation wireless communications

Multiple input multiple output (MIMO) space time codes are used to increase the diversity or the number of degrees of freedom for improving the capacity (data rate) and reliability (error rate) of wireless communication over fading channels. In this paper, we propose a new structure of space time code designed for non-stationary constant-amplitude modulation formats using Polynomial Phase Signals (PPS). In PPS modulation, the information is carried in the coefficients of polynomials that modulate the phase of a carrier. Here, Space Time Code (STC) structures differ from the conventional STC design as they are designed based on the coefficients of the phase polynomials instead of the Euclidean distance of the transmitted signals. These MIMO/STC/PPS based systems offers variable rates of transmission, better performance and easier implementation than conventional formats.

Identification of transcription factor binding sites based on the Chi-Square (x 2 ) distance of a probabilistic vector model

This paper describes a new approach for locating signals, such as promoter sequences, in nucleic acid sequences. Transcription Factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position weight matrix (PWM) [1], which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. In this paper, we present a Chi-Square ( x2 ) distance model [2], which is based on the distance between the profiles of component vectors. It is a novel probabilistic method for modeling TF-DNA interactions. Our approach uses x2 distances to represent TF binding specificities. Simulation results show that the proposed approach identifies TF binding sites significantly better than the PWM model method.

Effect of mutations on the detection of translational signals based on a communications theory approach

Gene and regulatory sequence identification is the first step in the functional annotation of any genome. Identification and annotation of such elements in the genome is a fundamental challenge in genomics and computational biology. Since regulatory elements are often short and variable, their identification and discovery using computational algorithms is difficult. However, significant advances have been made in the computational methods for modeling and detection of DNA regulatory elements. This paper proposes a novel use of techniques and principles from communications engineering and coding theory for modeling, identification and analysis of genomic regulatory elements and biological sequences. The last 13 bases sequence in the 16S rRNA molecule was used as a test sequence and was detected using the proposed models. Results show that the proposed models are not only able to identify this regulatory element (RE) in the mRNA sequence, but also can help identify coding from noncoding regions. The models described in this work where used to study the effect of mutations in the last 13 bases sequence of the 16S rRNA molecule. The obtained results showed total agreement with published investigations on mutations which further certify the biological relevance of the proposed models.

Identification of Transcriptional Promoter Sequence based on statistical filter bank model

This paper describes a new approach for locating transcription related signals, such as promoter sequence in nucleic acid sequences. Transcription Factor (TF) and corresponding polymerase binding to their DNA target site is a fundamental regulatory interaction. The most common model used to represent TF and polymerase binding specificities is a position weight matrix (PWM) [1], which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. In this paper, we present a statistical filter model based on Chi-Square (χ2) distance [2], which is a statistical distance metric between the profiles of component vectors. It is a novel statistical method for modeling TF-DNA and polymerase-DNA interactions. Our approach also uses a generalized correlation algorithm to evaluate the combination coefficients for the filter bank. Simulation results show that the proposed approach identifies promoter sequences better than the PWM model method and Chi-Square (χ2) distance model.