Kuo-ching Liang

Gene Regulatory Network Reconstruction Using Conditional Mutual Information

The inference of gene regulatory network from expression data is an important area of research that provides insight to the inner workings of a biological system. The relevance-network-based approaches provide a simple and easily-scalable solution to the understanding of interaction between genes. Up until now, most works based on relevance network focus on the discovery of direct regulation using correlation coefficient or mutual information. However, some of the more complicated interactions such as interactive regulation and coregulation are not easily detected. In this work, we propose a relevance network model for gene regulatory network inference which employs both mutual information and conditional mutual information to determine the interactions between genes. For this purpose, we propose a conditional mutual information estimator based on adaptive partitioning which allows us to condition on both discrete and continuous random variables. We provide experimental results that demonstrate that the proposed regulatory network inference algorithm can provide better performance when the target network contains coregulated and interactively regulated genes.

Inference of Regulatory Gene Interactions from Expression Data Using Three-Way Mutual Information

This paper describes the technique designated best performer in the 2nd conference on Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Challenge 5 (unsigned genome‐scale network prediction from blinded microarray data). Existing algorithms use the pairwise correlations of the expression levels of genes, which provide valuable but insufficient information for the inference of regulatory interactions. Here we present a computational approach based on the recently developed context likelihood of related (CLR) algorithm, extracting additional complementary information using the information theoretic measure of synergy and assigning a score to each ordered pair of genes measuring the degree of confidence that the first gene regulates the second. When tested on a set of publicly available Escherichia coli gene‐expression data with known assumed ground truth, the synergy augmented CLR (SA‐CLR) algorithm had significantly improved prediction performance when compared to CLR. There is also enhanced potential for biological discovery as a result of the identification of the most likely synergistic partner genes involved in the interactions.

A profile-based deterministic sequential Monte Carlo algorithm for motif discovery

MOTIVATION Conserved motifs often represent biological significance, providing insight on biological aspects such as gene transcription regulation, biomolecular secondary structure, presence of non-coding RNAs and evolution history. With the increasing number of sequenced genomic data, faster and more accurate tools are needed to automate the process of motif discovery. RESULTS We propose a deterministic sequential Monte Carlo (DSMC) motif discovery technique based on the position weight matrix (PWM) model to locate conserved motifs in a given set of nucleotide sequences, and extend our model to search for instances of the motif with insertions/deletions. We show that the proposed method can be used to align the motif where there are insertions and deletions found in different instances of the motif, which cannot be satisfactorily done using other multiple alignment and motif discovery algorithms. AVAILABILITY MATLAB code is available at http://www.ee.columbia.edu/~kcliang

Minimum Error-Rate Linear Dispersion Codes for Cooperative Relays

Cooperative diversity systems have recently been proposed as a solution to provide spatial diversity for terminals where multiple antennas are not feasible to be implemented. As in multiple-input-multiple-output systems, space-time codes can be used to efficiently exploit the increase in capacity provided in cooperative diversity systems. In this paper, we propose a two-layer linear dispersion (LD) code for cooperative diversity systems and derive a simulation-based optimization algorithm to optimize the LD code and power allocation in terms of block error rate. The proposed code design paradigm can obtain optimal codes under arbitrary fading statistics. Performance comparisons are made to other cooperative diversity schemes. The effect that distances between source, relays, and destination terminals have on the energy allocation between the broadcast and cooperative intervals is also studied. Cooperative diversity, gradient estimation, linear dispersion (LD) codes, multiple-input-multiple-output (MIMO), stochastic approximation.

Robust discovery of periodically expressed genes using the laplace periodogram.

BackgroundTime-course gene expression analysis has become important in recent developments due to the increasingly available experimental data. The detection of genes that are periodically expressed is an important step which allows us to study the regulatory mechanisms associated with the cell cycle.ResultsIn this work, we present the Laplace periodogram which employs the least absolute deviation criterion to provide a more robust detection of periodic gene expression in the presence of outliers. The Laplace periodogram is shown to perform comparably to existing methods for the Sacharomyces cerevisiae and Arabidopsis time-course datasets, and to outperform existing methods when outliers are present.ConclusionTime-course gene expression data are often noisy due to the limitations of current technology, and may include outliers. These artifacts corrupt the available data and make the detection of periodicity difficult in many cases. The Laplace periodogram is shown to perform well for both data with and without the presence of outliers, and also for data that are non-uniformly sampled.

A Deterministic Sequential Monte Carlo Method for Haplotype Inference

Sets of single nucleotide polymorphisms (SNPs), or haplotypes, are widely used in the analysis of relationship between genetics and diseases. Due to the cost of obtaining exact haplotype pairs, genotypes which contain the unphased information corresponding to the haplotype pairs in the test subjects are used. Various haplotype inference algorithms have been proposed to resolve the unphased information. However, most existing algorithms are limited in different ways. For statistical algorithms, the limiting factors are often in terms of the number of SNPs allowed in the genotypes, or the number of subjects in the dataset. In this paper, we propose a deterministic sequential Monte Carlo-based haplotype inference algorithm which allows for larger datasets in terms of number of SNPs and number of subjects, while providing similar or better performance for datasets under various conditions.

Protein Secondary Structure Prediction using Deterministic Sequential Sampling

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. While many of the existing algorithms utilize the similarity and homology to proteins with known secondary structures in the Protein Data Bank, other proteins with low similarity measures require a single sequence approach to the discovery of their secondary structure. In this paper we propose an algorithm based on the deterministic sequential sampling method and hidden Markov model for the single-sequence protein secondary structure prediction. The predictions are made based on windowed observations and by the weighted average over possible conformations within the observation window. The proposed algorithm is shown to achieve better performance on real dataset compared to the existing single-sequence algorithm.

A Sequential Monte Carlo Method for Motif Discovery

We propose a sequential Monte Carlo (SMC)-based motif discovery algorithm that can efficiently detect motifs in datasets containing a large number of sequences. The statistical distribution of the motifs is modeled by an underlying position weight matrix (PWM), and both the PWM and the positions of the motifs within the sequences are estimated by the SMC algorithm. The proposed SMC motif discovery technique can locate motifs under a number of scenarios, including the single-block model, two-block model with unknown gap length, motifs of unknown lengths, motifs with unknown abundance, and sequences with multiple unique motifs. The accuracy of the SMC motif discovery algorithm is shown to be superior to that of the existing methods based on MCMC or EM algorithms. Furthermore, it is shown that the proposed method can be used to improve the results of existing motif discovery algorithms by using their results as the priors for the SMC algorithm.

Minimum Error Rate Linear Dispersion Codes for Cooperative Relays

Cooperative diversity systems have been recently proposed as a solution to provide spatial diversity for terminals where multiple antennas are not feasible to be implemented. As in MIMO systems, space-time codes can be used to efficiently exploit the increase in capacity provided in cooperative diversity systems. In this paper we propose a two-layer linear dispersion (LD) code for cooperative diversity systems and derive a simulation-based optimization algorithm to optimize the LD code and power allocation in terms of block error rate. The proposed code design paradigm can obtain optimal codes under arbitrary fading statistics. The effect that distances between source, relays, and destination terminals have on the energy allocation between the broadcast and cooperative intervals is also studied.

Bayesian Basecalling for DNA Sequence Analysis using Hidden Markov Models

It has been shown that electropherograms of DNA sequences can be modelled with hidden Markov models. Base-calling, the procedure that determines the sequence of bases from the given eletropherogram, can then be performed using the Viterbi algorithm. A training step is required prior to basecalling in order to estimate the HMM parameters. In this paper, we propose a Bayesian approach which employs the Markov chain Monte Carlo (MCMC) method to perform basecalling. Such an approach not only allows one to naturally encode the prior biological knowledge into the basecalling algorithm, it also exploits both the training data and the basecalling data in estimating the HMM parameters, leading to more accurate estimates. Using the recently sequenced genome of the organism Legionella pneumophila we show that similar performance as the state-of-the-art basecalling algorithm in terms of total errors can be achieved even when a simple Gaussian model is assumed for the emission densities.