Neil Jacklin

Arizona State University

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Zhi Ding

University of California, Davis

David Counsil

Sandia National Laboratories California

Ratish J. Punnoose

Sandia National Laboratories California

Chong‐Yung Chi

National Tsing Hua University

Kunyu Wang

Zhejiang International Studies University

Anna Scaglione

Zhejiang University

Yong Li

Northwestern Polytechnical University

Sissi Xiaoxiao Wu

Shenzhen University

D. Young

Sandia National Laboratories California

Chunqi Chang

Shenzhen University

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Arizona State University

Sandia National Laboratories California

University of Hong Kong

National Tsing Hua University

Huazhong University of Science and Technology

University of Science and Technology of China

Shenzhen University

Peng Cheng Laboratory

Northrop Grumman (United States)

Chinese University of Hong Kong

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Noniterative Convex Optimization Methods for Network Component Analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics (2012)

Neil Jacklin Zhi Ding Wei Chen Chunqi Chang

This work studies the reconstruction of gene regulatory networks by the means of network component analysis (NCA). We will expound a family of convex optimization-based methods for estimating the transcription factor control strengths and the transcription factor activities (TFAs). The approach taken in this work is to decompose the problem into a network connectivity strength estimation phase and a transcription factor activity estimation phase. In the control strength estimation phase, we formulate a new subspace-based method incorporating a choice of multiple error metrics. For the source estimation phase we propose a total least squares (TLS) formulation that generalizes many existing methods. Both estimation procedures are noniterative and yield the optimal estimates according to various proposed error metrics. We test the performance of the proposed algorithms on simulated data and experimental gene expression data for the yeast Saccharomyces cerevisiae and demonstrate that the proposed algorithms have superior effectiveness in comparison with both Bayesian Decomposition (BD) and our previous FastNCA approach, while the computational complexity is still orders of magnitude less than BD.

10.1109/tcbb.2012.81

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Modern modulation schemes : a linear programming based tone injection algorithm for PAPR reduction of OFDM and linearly precoded systems.

Neil Jacklin Zhi Ding

investigation into the performances of a variable length adjacent partitioning (VL-AP) and fixed length adjacent partitioning in comparison with other partitioning schemes such as pseudorandom partitioning. Simulation results with different modulation and partitioning scenarios showed that fixed length adjacent partition had better performance compared to variable length adjacent partitioning. As expected, simulation results showed a slightly better performance of pseudorandom partitioning technique compared to fixed and variable adjacent partitioning schemes. However, as the pseudorandom technique incurs high computational complexities, adjacent partitioning schemes were still seen as favorable candidates for PAPR reduction.

Modulation (music)

10.2172/1055613

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