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PubMed1995

A convolution-adapted ratio-TAR algorithm for 3D photon beam treatment planning.

A convolution-adapted ratio of tissue-air ratios (CARTAR) method of dose calculation has been developed at the Mallinckrodt Institute of Radiology. This photon pencil-beam algorithm has been developed and implemented specifically for three-dimensional treatment planning. In a standard ratio of tissue-air ratios (RTAR) algorithm, doses to points in irregular field geometries are not adequately modeled. This is inconsistent with the advent of conformal therapy, the goal of which is to conform the dose distribution to the target volume while sparing neighboring sensitive normal critical structures. This motivated us to develop an algorithm that can model the beam penumbra near irregular field edges, while retaining much of the speed for the original RTAR algorithm. The dose calculation algorithm uses two-dimensional (2D) convolutions, computed by 2D fast Fourier transform, of pencil-beam kernels with a beam transmission array to calculate 2D off-axis profiles at a series of depths. These profiles are used to replace the product of the transmission function and measured square-field boundary factors used in the standard RTAR calculation. The 2D pencil-beam kernels were derived from measured data for each modality using commonly available dosimetry equipment. The CARTAR algorithm is capable of modeling the penumbra near block edges as well as the loss of primary and scattered beam in partially blocked regions. This paper describes the dose calculation algorithm, implementation, and verification.

Algorithms↗

PubMed2002

Comparison of two algorithms for determining beam weights and wedge filters.

This article compares two algorithms for determining beam weights and wedge filters for conformal treatment planning. One algorithm, which is based on dose-gradient analysis, provides analytic formulas for determining beam weights, wedge angles, and collimator angles (i.e., wedge orientations) so that the dose distribution is homogeneous in the target volume. The second algorithm is based on the concept of the super-omni wedge (i.e., the arrangement of two pairs of orthogonal nominal wedged beams), numerically optimize beam weights, wedge angles, and collimator angles so that the dose requirements to targets and organs at risk are satisfied to the best. Three clinical cases were tested. For the first case, both algorithms resulted in comparable homogeneous dose distributions in the target volume. For the second case, the second algorithm resulted in much lower doses to the eyes plus a better homogeneous dose distribution in the target volume. For the third case, only the second algorithm was applicable, and the treatment plan it developed met the prescribed requirements. The results show that the first algorithm is better in terms of feasibility, whereas the second is better in terms of applicability and the quality of treatment plans.

Algorithms↗

PubMed1990

Algorithms for separating the speech of interfering talkers: evaluations with voiced sentences, and normal-hearing and hearing-impaired listeners.

Two signal-processing algorithms, derived from those described by Stubbs and Summerfield [R.J. Stubbs and Q. Summerfield, J. Acoust. Soc. Am. 84, 1236-1249 (1988)], were used to separate the voiced speech of two talkers speaking simultaneously, at similar intensities, in a single channel. Both algorithms use fundamental frequency (FO) as the basis for segregation. One attenuates the interfering voice by filtering the cepstrum of the signal. The other is a hybrid algorithm that combines cepstral filtering with the technique of harmonic selection [T.W. Parsons, J. Acoust. Soc. Am. 60, 911-918 (1976)]. The algorithms were evaluated and compared in perceptual experiments involving listeners with normal hearing and listeners with cochlear hearing impairments. In experiment 1 the processing was used to separate voiced sentences spoken on a monotone. Both algorithms gave significant increases in intelligibility to both groups of listeners. The improvements were equivalent to an increase of 3-4 dB in the effective signal-to-noise ratio (SNR). In experiment 2 the processing was used to separate voiced sentences spoken with time-varying intonation. For normal-hearing listeners, cepstral filtering gave a significant increase in intelligibility, while the hybrid algorithm gave an increase that was on the margins of significance (p = 0.06). The improvements were equivalent to an increase of 2-3 dB in the effective SNR. For impaired listeners, no intelligibility improvements were demonstrated with intoned sentences. The decrease in performance for intoned material is attributed to limitations of the algorithms when FO is nonstationary.

Algorithms↗

PubMed2006

Use of a multiantigen detection algorithm for diagnosis of Kaposi's sarcoma-associated herpesvirus infection.

The ability to readily and accurately diagnose Kaposi's sarcoma-associated herpesvirus (KSHV, or human herpesvirus 8) infection in individuals remains a demanding task. Among the available diagnostic methods, sensitivities and specificities range widely, and many are inadequate for large-scale screening studies. We examined a serological algorithm for detecting KSHV in human sera having high sensitivity and specificity. This method uses previously described open reading frame (ORF) K8.1 and ORF65 peptide-based enzyme-linked immunosorbent assays and a novel purified recombinant full-length LANA1 protein. We generated two multiantigen algorithms: one that maximized sensitivity and one that maximized specificity. These serological algorithms were then used to evaluate seroprevalence rates among populations of clinical and epidemiological importance. The serological algorithms yielded sensitivities of 96% and 93% and specificities of 94% and 98% for the more sensitive and specific algorithms, respectively. Among kidney donors, seroprevalence was low, 4.0% (2/50), and similar to that of blood donors (P = 0.46; odds ratio [OR], 1.4; confidence interval [CI], 0.14 to 7.9) using the highly specific algorithm. Using the sensitive algorithm, 8.0% (4/50) were infected compared to 6.4% (16/250) observed among blood donors (OR, 1.3; CI, 0.41 to 4.0; P = 0.43). Among subjects requiring bone marrow transplantation, seroprevalence rates were not elevated compared to those of blood donors (OR, 2.0; 95% CI, 0.10 to 122.9; P = 0.50). Because the need for high-quality KSHV detection methods are warranted and because questions remain about the optimal methods for assessing KSHV infection in individuals, we propose a systematic approach to standardize and optimize the assessment of KSHV infection rates using a combination of established and novel serological assays and methods.

Algorithms↗

PubMed2004

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

Algorithms↗

PubMed2006

Protein threading with profiles and distance constraints using clique based algorithms.

With the advent of experimental technologies like chemical cross-linking, it has become possible to obtain distances between specific residues of a newly sequenced protein. These types of experiments usually are less time consuming than X-ray crystallography or NMR. Consequently, it is highly desired to develop a method that incorporates this distance information to improve the performance of protein threading methods. However, protein threading with profiles in which constraints on distances between residues are given is known to be NP-hard. By using the notion of a maximum edge-weight clique finding algorithm, we introduce a more efficient method called FTHREAD for profile threading with distance constraints that is 18 times faster than its predecessor CLIQUETHREAD. Moreover, we also present a novel practical algorithm NTHREAD for profile threading with Non-strict constraints. The overall performance of FTHREAD on a data set shows that although our algorithm uses a simple threading function, our algorithm performs equally well as some of the existing methods. Particularly, when there are some unsatisfied constraints, NTHREAD (Non-strict constraints threading algorithm) performs better than threading with FTHREAD (Strict constraints threading algorithm). We have also analyzed the effects of using a number of distance constraints. This algorithm helps the enhancement of alignment quality between the query sequence and template structure, once the corresponding template structure is determined for the target sequence.

Algorithms↗

PubMed2002

CCCP algorithms to minimize the Bethe and Kikuchi free energies: convergent alternatives to belief propagation.

This article introduces a class of discrete iterative algorithms that are provably convergent alternatives to belief propagation (BP) and generalized belief propagation (GBP). Our work builds on recent results by Yedidia, Freeman, and Weiss (2000), who showed that the fixed points of BP and GBP algorithms correspond to extrema of the Bethe and Kikuchi free energies, respectively. We obtain two algorithms by applying CCCP to the Bethe and Kikuchi free energies, respectively (CCCP is a procedure, introduced here, for obtaining discrete iterative algorithms by decomposing a cost function into a concave and a convex part). We implement our CCCP algorithms on two- and three-dimensional spin glasses and compare their results to BP and GBP. Our simulations show that the CCCP algorithms are stable and converge very quickly (the speed of CCCP is similar to that of BP and GBP). Unlike CCCP, BP will often not converge for these problems (GBP usually, but not always, converges). The results found by CCCP applied to the Bethe or Kikuchi free energies are equivalent, or slightly better than, those found by BP or GBP, respectively (when BP and GBP converge). Note that for these, and other problems, BP and GBP give very accurate results (see Yedidia et al., 2000), and failure to converge is their major error mode. Finally, we point out that our algorithms have a large range of inference and learning applications.

Algorithms↗

PubMedDate not supplied

Use of a glucose algorithm to direct diabetes therapy improves A1C outcomes and defines an approach to assess provider behavior.

PURPOSE: The purpose of this study was to determine whether an algorithm that recommended individualized changes in therapy would help providers to change therapy appropriately and improve glycemic control in their patients. METHODS: The algorithm recommended specific doses of oral agents and insulin based on a patient's medications and glucose or A1C levels at the time of the visit. The prospective observational study analyzed the effect of the algorithm on treatment decisions and A1C levels in patients with type 2 diabetes. RESULTS: The study included 1250 patients seen in pairs of initial and follow-up visits during a 7-month baseline and/or a subsequent 7-month algorithm period. The patients had a mean age of 62 years, body mass index of 33 kg/m(2), duration of diabetes of 10 years, were 94% African American and 71% female, and had average initial A1C level of 7.7%. When the algorithm was available, providers were 45% more likely to intensify therapy when indicated (P = .005) and increased therapy by a 20% greater amount (P < .001). A1C level at follow-up was 90% more likelyto be <7% in the algorithm group, even after adjusting for differences in age, sex, body mass index, race, duration of diabetes and therapy, glucose, and A1C level at the initial visit (P < .001). CONCLUSIONS: Use of an algorithm that recommends patient-specific changes in diabetes medications improves both provider behavior and patient A1C levels and should allow quantitative evaluation of provider actions for that provider's patients.

Aged↗

PubMed1997

Algorithms used in adverse drug event reports: a comparative study.

OBJECTIVE: To determine if and how the Kramer and Karch algorithms differ in assigning a probability that a published case was actually an adverse drug event (ADE), and to determine if these algorithms could be used to assess published ADEs. DESIGN: Open, single-rater comparison of Karch and Kramer algorithms in 200 published ADE reports. MAIN RESULTS: The algorithms were not significantly different regarding the proportion of cases deemed definite (p = 0.5204) or probable (p = 0.2972) ADEs. The Kramer instrument was more likely to assign a possible risk of ADE (p = 0.0001), while the Karch instrument was more likely to assign a risk of unlikely (p = 0.0001). The algorithms agreed in 41% of the cases and could be used to assess published ADEs. CONCLUSIONS: The Karch and Kramer algorithms may disagree in how they assign a probability of risk to a potential ADE. This may be due to how algorithms are applied, as well as to structural differences.

Adverse Drug Reaction Reporting Systems↗

PubMed2004

Incremental genetic K-means algorithm and its application in gene expression data analysis.

BACKGROUND: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. RESULTS: In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm. CONCLUSIONS: Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.

Algorithms↗

PubMed2004

A fast algorithm for determining the best combination of local alignments to a query sequence.

BACKGROUND: Existing sequence alignment algorithms assume that similarities between DNA or amino acid sequences are linearly ordered. That is, stretches of similar nucleotides or amino acids are in the same order in both sequences. Recombination perturbs this order. An algorithm that can reconstruct sequence similarity despite rearrangement would be helpful for reconstructing the evolutionary history of recombined sequences. RESULTS: We propose a graph-based algorithm for combining multiple local alignments to a query sequence into the single combination of alignments that either covers the maximal portion of the query or results in the single highest alignment score to the query. This algorithm can help study the process of genome rearrangement, improve functional gene annotation, and reconstruct the evolutionary history of recombined proteins. The algorithm takes O(n2) time, where n is the number of local alignments considered. CONCLUSIONS: We discuss two example applications of the algorithm. The algorithm is able to provide useful reconstructions of the metazoan mitochondrial genome. It is also able to increase the percentage of a query sequence's amino acid residues for which similar stretches of amino acids can be found in sequence databases.

Algorithms↗

PubMed2005

Genomic multiple sequence alignments: refinement using a genetic algorithm.

BACKGROUND: Genomic sequence data cannot be fully appreciated in isolation. Comparative genomics--the practice of comparing genomic sequences from different species--plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation) score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program. RESULTS: We tested the GenAlignRefine algorithm by running it on a Linux cluster to refine sequences from a simulation, as well as refine a multiple alignment of 15 Orthopoxvirus genomic sequences approximately 260,000 nucleotides in length that initially had been aligned by Multi-LAGAN. It took approximately 150 minutes for a 40-processor Linux cluster to optimize some 200 fuzzy (poorly aligned) regions of the orthopoxvirus alignment. Overall sequence identity increased only slightly; but significantly, this occurred at the same time that the overall alignment length decreased--through the removal of gaps--by approximately 200 gapped regions representing roughly 1,300 gaps. CONCLUSION: We have implemented a genetic algorithm in parallel mode to optimize multiple genomic sequence alignments initially generated by various alignment tools. Benchmarking experiments showed that the refinement algorithm improved genomic sequence alignments within a reasonable period of time.

Algorithms↗

PubMed2005

A new dynamical layout algorithm for complex biochemical reaction networks.

BACKGROUND: To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every kind of pathway in biochemical research. This is mainly due to certain conventions to which biochemists/biologists are used to and which are not in accordance to conventional layout algorithms. A number of approaches has been developed to improve this situation. Some of these are used in the context of biochemical databases and make more or less use of the information in these databases to aid the layout process. However, visualisation becomes also more and more important in modelling and simulation tools which mostly do not offer additional connections to databases. Therefore, layout algorithms used in these tools have to work independently of any databases. In addition, all of the existing algorithms face some limitations with respect to the number of edge crossings when it comes to larger biochemical systems due to the interconnectivity of these. Last but not least, in some cases, biochemical conventions are not met properly. RESULTS: Out of these reasons we have developed a new algorithm which tackles these problems by reducing the number of edge crossings in complex systems, taking further biological conventions into account to identify and visualise cycles. Furthermore the algorithm is independent from database information in order to be easily adopted in any application. It can also be tested as part of the SimWiz package (free to download for academic users at 1). CONCLUSION: The new algorithm reduces the complexity of pathways, as well as edge crossings and edge length in the resulting graphical representation. It also considers existing and further biological conventions to create a drawing most biochemists are familiar with. A lot of examples can be found on 2.

Algorithms↗

PubMed2005

An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem.

BACKGROUND: The protein folding problem is a fundamental problems in computational molecular biology and biochemical physics. Various optimisation methods have been applied to formulations of the ab-initio folding problem that are based on reduced models of protein structure, including Monte Carlo methods, Evolutionary Algorithms, Tabu Search and hybrid approaches. In our work, we have introduced an ant colony optimisation (ACO) algorithm to address the non-deterministic polynomial-time hard (NP-hard) combinatorial problem of predicting a protein's conformation from its amino acid sequence under a widely studied, conceptually simple model - the 2-dimensional (2D) and 3-dimensional (3D) hydrophobic-polar (HP) model. RESULTS: We present an improvement of our previous ACO algorithm for the 2D HP model and its extension to the 3D HP model. We show that this new algorithm, dubbed ACO-HPPFP-3, performs better than previous state-of-the-art algorithms on sequences whose native conformations do not contain structural nuclei (parts of the native fold that predominantly consist of local interactions) at the ends, but rather in the middle of the sequence, and that it generally finds a more diverse set of native conformations. CONCLUSIONS: The application of ACO to this bioinformatics problem compares favourably with specialised, state-of-the-art methods for the 2D and 3D HP protein folding problem; our empirical results indicate that our rather simple ACO algorithm scales worse with sequence length but usually finds a more diverse ensemble of native states. Therefore the development of ACO algorithms for more complex and realistic models of protein structure holds significant promise.

Algorithms↗

PubMed2006

A fast parallel algorithm for finding the longest common sequence of multiple biosequences.

BACKGROUND: Searching for the longest common sequence (LCS) of multiple biosequences is one of the most fundamental tasks in bioinformatics. In this paper, we present a parallel algorithm named FAST_LCS to speedup the computation for finding LCS. RESULTS: A fast parallel algorithm for LCS is presented. The algorithm first constructs a novel successor table to obtain all the identical pairs and their levels. It then obtains the LCS by tracing back from the identical character pairs at the last level. Effective pruning techniques are developed to significantly reduce the computational complexity. Experimental results on gene sequences in the tigr database show that our algorithm is optimal and much more efficient than other leading LCS algorithms. CONCLUSION: We have developed one of the fastest parallel LCS algorithms on an MPP parallel computing model. For two sequences X and Y with lengths n and m, respectively, the memory required is max{4*(n+1)+4*(m+1), L}, where L is the number of identical character pairs. The time complexity is O(L) for sequential execution, and O(|LCS(X, Y)|) for parallel execution, where |LCS(X, Y)| is the length of the LCS of X and Y. For n sequences X1, X2, ..., Xn, the time complexity is O(L) for sequential execution, and O(|LCS(X1, X2, ..., Xn)|) for parallel execution. Experimental results support our analysis by showing significant improvement of the proposed method over other leading LCS algorithms.

Algorithms↗

PubMed2004

The economic implications of three biochemical screening algorithms for pheochromocytoma.

Pheochromocytoma is a rare, life-threatening condition. Using a modeling technique, we studied the economic implications of detection strategies for pheochromocytoma (third-party payer perspective). The diagnostic efficacy of biochemical tests was based on Mayo Clinic Rochester data. In all hypothetical algorithms, positive biochemical tests were followed by abdominal computerized tomography and, if negative, metaiodobenzylguanidine scintigraphy. In each hypothetical algorithm, imaging would be indicated after positive biochemical testing as follows: algorithm A, fractionated plasma metanephrine measurements above the laboratory reference range; or algorithm B, abnormal measurements of 24-h urinary total metanephrines or catecholamines. In algorithm C, subjects with fractions of plasma metanephrine at or above 0.5 nmol/liter or normetanephrine at or above 1.80 nmol/liter would undergo imaging, whereas those with values between the reference range and these cutoffs would undergo 24-h urinary measurements (total metanephrines and fractionated catecholamines) and be imaged if positive. We determined that, if 100,000 hypertensive patients (including 500 patients with pheochromocytoma) were tested, algorithm A (measurement of fractionated plasma metanephrines alone) would detect 489 pheochromocytoma patients at a cost of 56.6 million dollars, whereas B (24-h urinary measurements) would detect 457 pheochromocytoma patients for 39.5 million dollars, and C (combination of measurements of fractionated plasma metanephrines and urines) would detect 478 patients for 28.6 million dollars. None of the screening strategies for pheochromocytoma described are affordable if implemented on a routine basis in extremely low-risk patients. However, algorithm C may be the least costly, and at a reasonable level of sensitivity, for subjects in whom the suspicion of disease is moderate.

Adrenal Gland Neoplasms↗

PubMed1993

A 4, 2, and 1 stepping algorithm for quick and accurate estimation of cutaneous sensation threshold.

In quantitative sensory testing, certain methods may lead to incorrect estimates of vibratory (VDT), cool (CDT), or warm (WDT) detection thresholds. We have shown that the specific forced-choice algorithm of testing employed in our Computer-Assisted Sensory Examination (CASE IV) system, when compared with other tests of nerve dysfunction, provides accurate and reproducible estimates of these thresholds. Because this forced-choice algorithm is time consuming and performance might be made worse by drowsiness or boredom, we explored other algorithms that might provide estimates of threshold similar to those obtained with the forced-choice algorithm, but more quickly. In a trial of 25 healthy subjects and 25 patients with neuropathy, the 4, 2, and 1 stepping algorithm with null stimuli, based in part on comparative data from computer simulation and insights from patient decision making, provides an accurate estimate of threshold. On average, the time needed for forced-choice testing was 12.8 +/- 2.9 minutes (mean +/- SD). For 4, 2, and 1 stepping testing, it was 2.7 +/- 2.5 minutes--a large saving of time. Since null stimuli were employed in the 4, 2, and 1 stepping algorithm, it was possible to monitor for spurious responses and repeat the test if they occurred at an excessive rate. The algorithm appears to be sufficiently robust to be recommended for clinical use and for some controlled clinical and epidemiologic trials.

Algorithms↗

PubMed2005

Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms.

We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5' end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3' UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps. (TargetBoost is available as a Web tool from http://www.interagon.com/demo/.).

5' Untranslated Regions↗