17686405
A time-series biclustering algorithm for revealing co-regulated genes
Although existing bicluster algorithms claimed to be able to discover co-regulated genes under a subset of given experiment conditions, they ignore the inherent sequential relationship between crucial time points and thus are not applicable to analyze time-series gene expression data. A simple and effective deletion-based algorithm, using the mean squared residue score as a measure, was developed to bicluster time-series gene expression data. While enforcing a threshold value for the score, the algorithm alternately eliminates genes and time points according to their correlation to the bicluster. To ensure the time locality, only the starting and ending points in the time interval are eligible for deletion. As a result, the number of genes and the length of time interval are simultaneously maximized. Our experimental results shown that the proposed method is capable of identifying co-regulated genes characterized by partial time-course data that previous methods failed to discover.
27561759
Splice site prediction using support vector machines with a Bayes kernel
One of the most important tasks in correctly annotating genes in higher organisms is to accurately locate the DNA splice sites. Although relatively high accuracy has been achieved by existing methods, most of these prediction methods are computationally extensive. Due to the enormous amount of DNA sequences to be processed, the computational speed is an important issue to consider. In this paper, we present a new machine learning method for predicting DNA splice sites, which first applies a Bayes feature mapping (kernel) to project the data into a new feature space and then uses a linear Support Vector Machine (SVM) as a classifier to recognize the true splice sites. The computation time is linear to the number of sequences tested, while the performance is notably improved compared with the Naive Bayes classifier in terms of classification accuracy, precision, and recall. Our classification results are also comparable to the solution quality obtained by the SVMs with polynomial kernels, while the speed of our proposed method is significantly faster. This is a notable improvement in computational modeling considering the huge amount of DNA sequences to be processed.
24078054
Greedy wire-sizing is linear time
The greedy wire-sizing algorithm (GWSA) has been experimentally shown to be very efficient, but no mathematical analysis on its convergence rate has ever been reported. In this paper, we consider GWSA for continuous wire sizing. We prove that GWSA converges linearly to the optimal solution, which implies that the run time of GWSA is linear with respect to the number of wire segments for any fixed precision of the solution. Moreover, we also prove that this is true for any starting solution. This is a surprising result because previously it was believed that in order to guarantee convergence, GWSA had to start from a solution in which every wire segment is set to the minimum (or maximum) possible width. Our result implies that GWSA can use a good starting solution to achieve faster convergence. We demonstrate this point by showing that the minimization of maximum delay and the minimization of area subject to maximum delay bound using Lagrangian relaxation can be sped up by more than 50%.
38628485
Dilation-5 Embedding of 3-Dimensional Grids into Hypercubes
We present an algorithm to map the nodes of a 3-dimensional grid to the nodes of its optimal hypercube on a one-to-one basis with dilation at most 5.
666927
Greedy wire-sizing is linear time
In interconnect optimization by wire-sizing, minimizing weighted sink delay has been shown to be the key problem. Wire-sizing with many important objectives such as minimizing total area subject to delay bounds or minimizing maximum delay can all be reduced to solving a sequence of weighted sink delay problems by Lagrangian relaxation [1, 3]. GWSA, first introduced in [10] for discrete wire-sizing and later extended in [2] to continuous wire-sizing, is a greedy wire-sizing algorithm for the weighted sink delay problem. Although GWSA has been experimentally shown to be very efficient, no mathematical analysis on its convergence rate has ever been reported. In this paper, we consider GWSA for continuous wire sizing. We prove that GWSA converges linearly to the optimal solution, which implies that the run time of GWSA is linear with respect to the number of wire segments for any fixed precision of the solution. Moreover, we also prove that this is true for any starting solution. This is a surprising result because previously it was believed that in order to guarantee convergence, GWSA had to start from a solution in which every wire segment is set to the minimum (or maximum) possible width. Our result implies that GWSA can use a good starting solution to achieve faster convergence. We demonstrate this point by showing that the minimization of maximum delay using Lagrangian relaxation can be speed up by 57.7%.
41348498
Fast and exact simultaneous gate and wire sizing by Lagrangian relaxation
37782928
A multi-dimensional version of the I test
Abstract Two-dimensional arrays with linear subscripts occur quite frequently in real programs. In general, for multi-dimensional linear arrays under constant bounds the Lambda test is an efficient data dependence method to check whether there exist real solutions. In this paper, we propose a multi-dimensional version of the I test, the multi-dimensional I test, that can be applied to testing whether there are integer solutions for multi-dimensional linear arrays under constant limits. Experiments with benchmark showing the effects of the multi-dimensional I test on testing precision and testing efficiency are also presented.