Jimali Software Engineering

Protein identification is a key and essential step in mass spectrometry (MS) based proteome research. To date, there are many protein identification strategies that employ either MS data or MS/MS data for database searching. While MS-based methods provide wider coverage than MS/MS-based methods, their identification accuracy is lower since MS data have less information than MS/MS data. Thus, it is desired to design more sophisticated algorithms that achieve higher identification accuracy using MS data. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from MS data for many years. In this paper, we extend this technology to protein mixture identification. First, we formulate the problem of protein mixture identification as a Partial Set Covering (PSC) problem. Then, we present several algorithms that can solve the PSC problem efficiently. Finally, we extend the partial set covering model to both MS/MS data and the combination of MS data and MS/MS data. The experimental results on simulated data and real data demonstrate the advantages of our method: (1) it outperforms previous MS-based approaches significantly; (2) it is useful in the MS/MS-based protein inference; and (3) it combines MS data and MS/MS data in a unified model such that the identification performance is further improved.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=c3608345a2a4d21dd36596e069c888a7p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=c3608345a2a4d21dd36596e069c888a7p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Travel Depth, introduced by Coleman and Sharp in 2006, is a physical interpretation of molecular depth, term frequently used to describe the shape of a molecular active site or binding site. Travel Depth can be seen as the physical distance a solvent molecule would have to travel from a point of the surface, i.e., the Solvent Excluded Surface (SES), to its convex hull. Existing algorithms providing an estimation of the Travel Depth are based on a regular sampling of the molecule volume and on the use of the Dijkstra’s shortest path algorithm. Since Travel Depth is only defined on the molecular surface, this volume-based approach is characterized by a large computational complexity due to the processing of unnecessary samples lying inside or outside the molecule. In this paper, we propose a surface-based approach that restricts the processing to data defined on the SES. This algorithm significantly reduces the complexity of Travel Depth estimation and makes possible the analysis of large macromolecule surface shape description with high resolution. Experimental results show that compared to existing methods, the proposed algorithm achieves accurate estimations with considerably reduced processing times.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=33b26cc8a59813d0ba68230b804ad07fp=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=33b26cc8a59813d0ba68230b804ad07fp=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

A fundamental problem arising in the evolutionary molecular biology is to discover the locations of gene duplications and multiple gene duplication episodes based on the phylogenetic information. The solutions to the Multiple Gene Duplication problems can provide useful clues to place the gene duplication events onto the locations of a species tree and to expose the multiple gene duplication episodes. In this paper, we study two variations of the Multiple Gene Duplication problems: the Episode-Clustering (EC) problem and the Minimum Episodes (ME) problem. For the EC problem, we improve the results of Burleigh et~al. with an optimal linear-time algorithm. For the ME problem, on the basis of the algorithm presented by Bansal and Eulenstein, we propose an optimal linear-time algorithm.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=d5b323521528115af9755138842c7885p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=d5b323521528115af9755138842c7885p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

We propose a general theoretical framework for analyzing differentially expressed genes and behavior patterns from two homogenous short time-course data. The framework generalizes the recently proposed Hilbert Schmidt Independence Criterion (HSIC) based framework adapting it to the time-series scenario by utilizing tensor analysis for data transformation. The proposed framework is effective in yielding criteria that can identify both the differentially expressed genes and time-course patterns of interest between two time series experiments without requiring to explicitly cluster the data. The results, obtained by applying the proposed framework with a linear kernel formulation, on various datasets, are found to be both biologically meaningful and consistent with published studies.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=15c8195d5f41cb1334cce27518400c2bp=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=15c8195d5f41cb1334cce27518400c2bp=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

We focus on the neuro-fuzzy prediction of biological activities of HIV-1 protease inhibitory compounds when inferring from small training sets. We propose two computational intelligence prediction techniques which are suitable for small training sets, at the expense of some computational overhead. Both techniques are based on the FAMR model. The FAMR is a Fuzzy ARTMAP (FAM) incremental learning system used for classification and probability estimation. During the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair. The two proposed algorithms in this paper are: 1. The GA-FAMR algorithm, which is new, uses a genetic algorithm to optimize the relevances assigned to the training data. 2. The Ordered FAMR is derived from a known algorithm. Instead of optimizing relevances, it optimizes the order of data presentation using the algorithm of Dagher et al. In our experiments, we compare these two algorithms with an algorithm not based on the FAM, the FS-GA-FNN. We conclude that when inferring from small training sets, both techniques are efficient, in terms of generalization capability and execution time. The computational overhead introduced is compensated by the better accuracy obtained. Finally, the proposed techniques are used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=2d791fcbd2169bb2c4e18e3954bd9a94p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=2d791fcbd2169bb2c4e18e3954bd9a94p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

The adaptation of the bacterium Escherichia coli to carbon starvation is controlled by a large network of biochemical reactions involving genes, mRNAs, proteins, and signalling molecules. The dynamics of these networks is difficult to analyze, notably due to a lack of quantitative information on parameter values. To overcome these limitations, model reduction approaches based on quasi-steady-state (QSS) and piecewise-linear (PL) approximations have been proposed, resulting in models that are easier to handle mathematically and computationally. The approximations are not supposed to affect the capability of the model to account for essential dynamical properties of the system, but the validity of this assumption has not been systematically tested. In this paper we carry out such a study by evaluating a large and complex PL model of the carbon starvation response in E. coli using an ensemble approach. The results show that, in comparison with conventional nonlinear models, the PL approximations generally preserve the dynamics of the carbon starvation response network, although with some deviations concerning notably the quantitative precision of the model predictions. This encourages the application of PL models to the qualitative analysis of bacterial regulatory networks, in situations where the reference time-scale is that of protein synthesis and degradation.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=535ac49c52fde0e7f3d87da69b95edd7p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=535ac49c52fde0e7f3d87da69b95edd7p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This information can be used to understand the genetic regulatory network that generates the data. Typically, Bayesian analysis approaches are applied which neglect the time series nature of the experimental data, have difficulty in determining the direction of causality, and do not perform well on networks with tight feedback. This paper presents a method to learn genetic network connectivity which exploits the time series nature of experimental data to achieve better causal predictions. This method breaks up the data into bins, and determines an initial set of potential influence vectors for each gene based upon the probability of the gene’s expression increasing in the next time step. These vectors are then combined to form new vectors with better scores and are competed against each other to determine the final influence vector for each gene. The result is a directed graph representation of the genetic network’s repression and activation connections. Results are reported for several synthetic networks with tight feedback showing significant improvements over another dynamic Bayesian approach. Promising results are reported for genes involved in the yeast cell cycle.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=242b7fb897a29dcf8088ca1213468476p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=242b7fb897a29dcf8088ca1213468476p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

One can generate trajectories to simulate a system of chemical reactions using either Gillespie's direct method or Gibson and Bruck's next reaction method. Because one usually needs many trajectories to understand the dynamics of a system, performance is important. In this paper we present new formulations of these methods that improve the computational complexity of the algorithms. We present optimized implementations, available from http://cain.sourceforge.net, that offer better performance than previous work. There is no single method that is best for all problems. Simple formulations often work best for systems with a small number of reactions, while some sophisticated methods offer the best performance for large problems and scale well asymptotically. We investigate the performance of each formulation on simple biological systems using a wide range of problem sizes. We also consider the numerical accuracy of the direct and the next reaction method. We have found that special precautions must be taken in order to ensure that randomness is not discarded during the course of a simulation.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=14b7eec17f212c44de1ffdd30ae033e5p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=14b7eec17f212c44de1ffdd30ae033e5p=1//a

Analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity, and towards prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks like classification, clustering and feature selection enable efficient mining of knowledge about gene interactions in the form of networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools like fuzzy sets, evolutionary strategies and neurocomputing have been found to help in providing low cost, acceptable solutions in the presence of various types of uncertainties. In this article we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=b0c6cf24aa461fd2581455282fc6f667p=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=b0c6cf24aa461fd2581455282fc6f667p=1//a

Probe defects are a major source of noise in gene expression studies. While existing approaches detect noisy probes based on external information such as genomic alignments, we introduce and validate a targeted probabilistic method for analyzing probe reliability directly from expression data and independently of the noise source. This provides insights into the various sources of probe-level noise and gives tools to guide probe design.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=d3969f08c9f429a4e9dbca7468ab1214p=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=d3969f08c9f429a4e9dbca7468ab1214p=1//a

Behavior of living organisms is strongly modulated by the day and night cycle giving rise to a cyclic pattern of activities. Such a pattern helps the organism to coordinate their activities and maintain a balance between what could be performed during the 'day' and what could be relegated to 'night'. This cyclic pattern, called the 'Circadian Rhythm', is a biological phenomenon observed in a large number of organisms. In this paper, our goal is to analyze transcriptome data from Cyanothece for the purpose of discovering genes whose expressions are rhythmic. We cluster these genes into groups that are close in terms of their phases and show that genes from a specific metabolic functional category are tightly clustered, indicating perhaps a 'preferred time of the day/night' when the organism performs this function. The proposed analysis is applied to two sets of micro array experiments performed under varying incident light patterns. Subsequently we propose a model with a network of three phase oscillators together with a central master clock and use it to approximate a set of 'circadian controlled genes' that can be approximated closely.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=b1d3a68d7a36910bed4511963c68fd03p=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=b1d3a68d7a36910bed4511963c68fd03p=1//a

As a well established feature selection algorithm, principal component analysis (PCA) is often combined with state-of-the-art classification algorithms to identify cancer molecular patterns in microarray data. However, its global feature selection mechanism prevents it from effectively capturing the latent data structures in the high dimensional data. In this study, we investigate the benefit of adding nonnegative constraints on PCA and develop a nonnegative principal component analysis algorithm (NPCA) to overcome the global nature of PCA. A novel classification algorithm NPCA-SVM is proposed for microarray data pattern discovery. We report strong classification results from the NPCA-SVM algorithm on five benchmark microarray datasets by direct comparison with other related algorithms. We have also proved mathematically and interpreted biologically that microarray data will inevitably encounter over-fitting for a SVM/PCA-SVM learning machine under a Gaussian kernel. In addition, we demonstrate nonnegative principal component analysis can be used to capture meaningful biomarkers effectively.br clear=both style=clear: both;/ br clear=both style=clear: both;/ hr / div style=font-size:xx-small;color:gray;padding-bottom:.5emPresented By:/div diva href=http://www.pheedo.com/feeds/ht.php?t=camp;i=0ac0ff807f35bc85d082a839f6be6dc2amp;p=1Inside Guantanamo: Sunday at 9P e/p/a/div table border=0 cellpadding=0 cellspacing=0 trtd valign=topembed src=http://c.brightcove.com/services/viewer/federated_f9/17831997001?isVid=1publisherID=1660622131 bgcolor=#FFFFFF flashVars=@videoPlayer=17854499001playerID=17831997001domain=embed base=http://admin.brightcove.com name=flashObj width=300 height=250 seamlesstabbing=false type=application/x-shockwave-flash allowFullScreen=true swLiveConnect=true allowScriptAccess=always pluginspage=http://www.macromedia.com/shockwave/download/index.cgi?P1_Prod_Version=ShockwaveFlash/embedbr /br /img src=http://images.pheedo.com/g/ngc_bluewhale/brand_logo_80x60.pngbr /font size=2 face=tahoma Guantanamo Bay is one of the world's controversial prisons. This may be its final chapter. With unprecedented access, National Geographic has the story you haven't heard. Both sides, told from the inside, before its doors close forever. Click to learn more and go Inside Guantanamo br //fonta href=http://www.pheedo.com/click.phdo?a=v3%3Aa271cee67dfff482f0d65fb1ab2dbeb4%3AMr%2Bh0MpnVRLPNJdcAt9CNC9V4bldEKN7LJct7xOR4Qasw2TqiPSywbekHkNSMJBXoLLTgxjqJ6GFDjQrWKxDTti%2BExxPSgB53ImQxT%2Fv%2F65baGhOO2fHMoDRL2wRGFtyEd9rjTRarteEV4MpZVASTMH%2BQlzbT04u%2FQ%3D%3Dtarget=_blankfont size=2 font color=007DC3 face=tahoma Unatgeotv.com/guantanamoU/font/a/td/tr trtdnbsp;/td/tr /table div style=font-size:xx-small; padding-top: 1em;span style=border-top: 1px solid br style=display:none/ a href=http://www.pheedo.com/Ads by Pheedo/a /spanimg alt= style=border: 0; height: 1px; width: 1px; border=0 height=1 width=1 src=http://www.pheedo.com/feeds/ht.php?t=vamp;i=0ac0ff807f35bc85d082a839f6be6dc2amp;p=1/ br/ /div

Position weight matrices are an important method for modeling signals or motifs in biological sequences, both in DNA and protein contexts. In this paper we present fast algorithms for the problem of finding significant matches of such matrices. Our algorithms are of the on--line type, and they generalize classical multi-pattern matching, filtering, and super-alphabet techniques of combinatorial string matching to the problem of weight matrix matching. Several variants of the algorithms are developed, including multiple matrix extensions that perform the search for several matrices in one scan through the sequence database. Experimental performance evaluation is provided to compare the new techniques against each other as well as against some other on--line and index--based algorithms proposed in the literature. Compared to the brute-force $O(mn)$ approach, our solutions can be faster by a factor that is proportional to the matrix length $m$. Our multiple-matrix filtration algorithm had the best performance in the experiments. On a current PC, this algorithm finds significant matches ($p$ = 0.0001) of the 123 JASPAR matrices in the human genome in about 18 minutes.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=9f8e942600e246c1f3be64d8d3b2898fp=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=9f8e942600e246c1f3be64d8d3b2898fp=1//a

This paper presents the impact of twins and the measures for their removal from the population of genetic algorithm (GA) when applied to effective conformational searching. It is conclusively shown that a twin removal strategy for a GA provides considerably enhanced performance when investigating solutions to complex ab initio protein structure prediction (PSP) problems in low resolution model. Without twin removal, GA crossover and mutation operations can become ineffectual as generations lose their ability to produce significant differences which can lead to the solution stalling. The paper relaxes the definition of chromosomal twins in the removal strategy to not only encompass identical, but also highly-correlated chromosomes within the GA population, with empirical results consistently exhibiting significant improvements solving PSP problems.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=c193c885d56205517c93cb80aeec2d0cp=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=c193c885d56205517c93cb80aeec2d0cp=1//a

In this paper we are interested in the problem of approximating trees by trees with a particular self-nested structure. Self-nested trees are such that all their subtrees of a given height are isomorphic. We show that these trees present remarkable compression properties, with high compression rates. In order to measure how far a tree is from being a self-nested tree, we then study how to quantify the degree of self-nestedness of any tree. For this, we define a measure of the self-nestedness of a tree by constructing a self-nested tree that minimizes the distance of the original tree to the set of self-nested trees that embed the initial tree. We show that this measure can be computed in polynomial time and depict the corresponding algorithm. The distance to this nearest embedding self-nested tree (NEST) is then used to define compression coefficients that reflect the compressibility of a tree. To illustrate this approach, we then apply these notions to the analysis of plant branching structures. The approach is characterized on both a database of artificial plants with varying degrees of self-nestedness and on a real plant structure. We finally show that the NEST may reveal important aspects of the plant growth.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=b26f39549853d4d1e7ae8ea7b8fdcf70p=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=b26f39549853d4d1e7ae8ea7b8fdcf70p=1//a

Finding structural similarities in distant proteins can reveal functional relationships that can not be identified using sequence comparison. Given two proteins A and B and threshold ε Å, we develop an algorithm, TRiplet-based Iterative ALignment (TRIAL) for computing the transformation of B that maximizes the number of aligned residues such that the root mean square distance of the alignment is at most ε Å. Our algorithm is designed with the specific goal of effectively handling proteins with low similarity in primary structure, where existing algorithms perform particularly poorly. Experiments show that our method outperforms existing methods. TRIAL alignment brings the secondary structures of distant proteins to similar orientations. It also finds more number of secondary structure matches at lower RMSD (Root Mean Square Deviation) values and increased overall alignment lengths. Its classification accuracy is up to 63% better than other methods, including CE and DALI. TRIAL successfully aligns 83% of the residues from the smaller protein in reasonable time while other methods align only 29 to 65% of the residues for the same set of proteins.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=7b1ee0cd68ffc86bfda67f86fb5f210bp=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=7b1ee0cd68ffc86bfda67f86fb5f210bp=1//a

Partly due to ecombination, genealogical history of a set of DNA sequences in a population usually can not be represented by a single tree. Instead, genealogy is better represented by a genealogical network, which is a compact representation of a set of correlated local genealogical trees, each for a short region of genome and possibly with different topology. Inference of genealogical history for a set of DNA sequences under recombination has many potential applications, including association mapping of complex diseases. In this paper, we present two new methods for reconstructing local tree topologies with the presence of recombination, which extend and improve the previous work. We first show that the "tree scan" method can be converted to a probabilistic inference method based a hidden Markov model. We then focus on developing a novel local tree inference method called RENT that is both accurate and scalable to larger data. Through simulation, we demonstrate the usefulness of our methods by showing that the hidden Markov model-based method is comparable with the original method in terms of accuracy. We also show that RENT is competitive with other methods in terms of inference accuracy, and its inference error rate is often lower and can handle large data.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=b1e067d11072eed44a65630fe46ab730p=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=b1e067d11072eed44a65630fe46ab730p=1//a img src=http://www.pheedo.com/feeds/tracker.php?i=b1e067d11072eed44a65630fe46ab730 style=display: none; border=0 height=1 width=1 alt=/

Markov chain Monte Carlo has been the standard technique for inferring the posterior distribution of genome rearrangement scenarios under a Bayesian approach. We present here a negative result on the rate of convergence of the generally used Markov chains. We prove that the relaxation time of the Markov chains walking on the optimal reversal sorting scenarios might grow exponentially with the size of the signed permutations, namely, with the number of syntheny blocks.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=907e3d881f65e5c9eae95575feacd359p=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=907e3d881f65e5c9eae95575feacd359p=1//a img src=http://www.pheedo.com/feeds/tracker.php?i=907e3d881f65e5c9eae95575feacd359 style=display: none; border=0 height=1 width=1 alt=/

Finding Transcription Factor Binding Sites, i.e., motif discovery, is crucial for understanding the gene regulatory relationship. Motifs are weakly conserved and motif discovery is a NP-hard problem. We propose a new approach called Cluster Refinement Algorithm for Motif Discovery (CRMD). CRMD employs a flexible statistical motif model allowing a variable number of motifs and motif instances. CRMD first uses a novel entropy-based clustering to find complete and good starting candidate motifs from the DNA sequences. CRMD then uses an effective greedy refinement to search for optimal motifs from the candidate motifs. The refinement is fast, and it changes the number of motif instances based on the adaptive thresholds. The performance of CRMD is further enhanced if the problem has one occurrence of motif instance per sequence. Using an appropriate similarity test of motifs, CRMD is also able to find multiple motifs. CRMD has been tested extensively on synthetic and real datasets. The experimental results verify that CRMD usually outperforms four other state-of-the-art algorithms in terms of the qualities of the solutions with competitive computing time. It finds a good balance between finding true motif instances and screening false motif instances, and is robust on problems of various levels of difficulty.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=28f5fb3bfec40228d8c5d246580f262ap=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=28f5fb3bfec40228d8c5d246580f262ap=1//a img src=http://www.pheedo.com/feeds/tracker.php?i=28f5fb3bfec40228d8c5d246580f262a style=display: none; border=0 height=1 width=1 alt=/

The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution, phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most well studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both genomic sequence information and prior knowledge of a set of highly expressed genes. This novel approach provides more accurate results from sequence information alone by resisting the confounding effects of other biases. We validate this increase in accuracy in isolating translational efficiency bias on ten microbial genomes, five of which have proven particularly difficult for existing approaches due to the presence of strong confounding biases.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://www.pheedo.com/click.phdo?s=b73832bb5cd897347535b40e25be3cf0p=1img alt= style=border: 0; border=0 src=http://www.pheedo.com/img.phdo?s=b73832bb5cd897347535b40e25be3cf0p=1//a img src=http://www.pheedo.com/feeds/tracker.php?i=b73832bb5cd897347535b40e25be3cf0 style=display: none; border=0 height=1 width=1 alt=/