Dissecting the Biological Motherboard (Systems Biology and Beyond)

Genome-scale molecular networks, including gene pathways, gene regulatory networks and protein interactions, are central to the investigation of the nascent disciplines of systems biology and bio-complexity. Dissecting these genome-scale molecular networks in its all-possible manifestations is paramount in our quest for a genotype-input phenotype-output application which will also take environment-genome interactions into account.

Machine learning approaches are now increasingly being used for reverse engineering such networks. Our work stresses the importance of a system approach in biological research and how artificial neural networks are at the forefront of Artificial Intelligence techniques that are increasingly being used to construct as well as dissect molecular networks, the building blocks of the living system.

Our paper will show the application of artificial neural networks to reverse engineer a temporal gene pathway 
In this paper we will also explore the pruning of nodes of these artificial neural networks to simulate gene silencing and thus generate novel biological insight into these molecular networks (The Biological Motherboard).

The research described is novel, in that this may be the first time that the application of neural networks to temporal gene expression data is described. It will be shown that a trained artificial neural network, with pruning, can also be described as a gene network with minimal re-interpretation, where the weights on links between nodes reflect the probability of one gene affecting another gene in time

Multilevel Coding Schemes for Compute-and-Forward with Flexible Decoding

We consider the design of coding schemes for the wireless two-way relaying channel when there is no channel state information at the transmitter. In the spirit of the compute and forward paradigm, we present a multilevel coding scheme that permits computation (or, decoding) of a class of functions at the relay. The function to be computed (or, decoded) is then chosen depending on the channel realization. We define such a class of functions which can be decoded at the relay using the proposed coding scheme and derive rates that are universally achievable over a set of channel gains when this class of functions is used at the relay. We develop our framework with general modulation formats in mind, but numerical results are presented for the case where each node transmits using the QPSK constellation. Numerical results with QPSK show that the flexibility afforded by our proposed scheme results in substantially higher rates than those achievable by always using a fixed function or by adapting the function at the relay but coding over GF(4).Comment: This paper was submitted to IEEE Transactions on Information Theory in July 2011. A shorter version also appeared in the proceedings of the International Symposium on Information Theory in August 2011 without the proof of the main theore

Joint Compute and Forward for the Two Way Relay Channel with Spatially Coupled LDPC Codes

We consider the design and analysis of coding schemes for the binary input two way relay channel with erasure noise. We are particularly interested in reliable physical layer network coding in which the relay performs perfect error correction prior to forwarding messages. The best known achievable rates for this problem can be achieved through either decode and forward or compute and forward relaying. We consider a decoding paradigm called joint compute and forward which we numerically show can achieve the best of these rates with a single encoder and decoder. This is accomplished by deriving the exact performance of a message passing decoder based on joint compute and forward for spatially coupled LDPC ensembles.Comment: This paper was submitted to IEEE Global Communications Conference 201

Iterative Soft Input Soft Output Decoding of Reed-Solomon Codes by Adapting the Parity Check Matrix

An iterative algorithm is presented for soft-input-soft-output (SISO) decoding of Reed-Solomon (RS) codes. The proposed iterative algorithm uses the sum product algorithm (SPA) in conjunction with a binary parity check matrix of the RS code. The novelty is in reducing a submatrix of the binary parity check matrix that corresponds to less reliable bits to a sparse nature before the SPA is applied at each iteration. The proposed algorithm can be geometrically interpreted as a two-stage gradient descent with an adaptive potential function. This adaptive procedure is crucial to the convergence behavior of the gradient descent algorithm and, therefore, significantly improves the performance. Simulation results show that the proposed decoding algorithm and its variations provide significant gain over hard decision decoding (HDD) and compare favorably with other popular soft decision decoding methods.Comment: 10 pages, 10 figures, final version accepted by IEEE Trans. on Information Theor

A Decision Feedback Based Scheme for Slepian-Wolf Coding of sources with Hidden Markov Correlation

We consider the problem of compression of two memoryless binary sources, the correlation between which is defined by a Hidden Markov Model (HMM). We propose a Decision Feedback (DF) based scheme which when used with low density parity check codes results in compression close to the Slepian Wolf limits.Comment: Submitted to IEEE Comm. Letter