767368
On optimizing distance-based similarity search for biological databases
Similarity search leveraging distance-based index structures is increasingly being used for both multimedia and biological database applications. We consider distance-based indexing for three important biological data types, protein k-mers with the metric PAM model, DNA k-mers with Hamming distance and peptide fragmentation spectra with a pseudo-metric derived from cosine distance. To date, the primary driver of this research has been multimedia applications, where similarity functions are often Euclidean norms on high dimensional feature vectors. We develop results showing that the character of these biological workloads is different from multimedia workloads. In particular, they are not intrinsically very high dimensional, and deserving different optimization heuristics. Based on MVP-trees, we develop a pivot selection heuristic seeking centers and show it outperforms the most widely used corner seeking heuristic. Similarly, we develop a data partitioning approach sensitive to the actual data distribution in lieu of median splits.
115053
A metric model of amino acid substitution
MOTIVATION
We address the question of whether there exists an effective evolutionary model of amino-acid substitution that forms a metric-distance function. There is always a trade-off between speed and sensitivity among competing computational methods of determining sequence homology. A metric model of evolution is a prerequisite for the development of an entire class of fast sequence analysis algorithms that are both scalable, O(log n) and sensitive.


RESULTS
We have reworked the mathematics of the point accepted mutation model (PAM) by calculating the expected time between accepted mutations in lieu of calculating log-odds probabilities. The resulting substitution matrix (mPAM) forms a metric. We validate the application of the mPAM evolutionary model for sequence homology by executing sequence queries from a controlled yeast protein homology search benchmark. We compare the accuracy of the results of mPAM and PAM similarity matrices as well as three prior metric models. The experiment shows that mPAM significantly outperforms the other three metrics and sufficiently approaches the sensitivity of PAM250 to make it applicable to the management of protein sequence databases.
32102689
On Integrating Peptide Sequence Analysis and Relational Distance-Based Indexing
Managing data with distance-based indexing methods has the potential to provide scalability and integration with relational database management systems and the SQL programming model. We previously demonstrated the advantages of such an approach for nucleotide sequences using Hamming distance (mismatch). However, the larger alphabet size of peptide sequences increases the dimensionality of the problem, making algorithmic results more challenging. The development of a metric-PAM substitution matrix enables metric-distance based indexing for peptide sequences. The performance of distance-based indexing for homologous protein retrieval entails trade-off among accuracy, scalability and computational cost. We investigate the application of the multi-vantage point (MVP) tree algorithm to index peptide k-mers based on global mPAM alignment. We show that k-mer retrieval can still maintain accuracy when k is at least as large as 6 that creates a domain of over 60 million key values and enables scalability sufficient for effective performance on large disk-resident sequence databases
19634047
Automatic generation of symbolic model for parameterized synchronous systems
With the purpose of making the verification of parameterized system more general and easier, in this paper, a new and intuitive language PSL (Parameterized-system Specification Language) is proposed to specify a class of parameterized synchronous systems. From a PSL script, an automatic method is proposed to generate a constraint-based symbolic model. The model can concisely symbolically represent the collections of global states by counting the number of processes in a given state. Moreover, a theorem has been proved that there is a simulation relation between the original system and its symbolic model. Since the abstract and symbolic techniques are exploited in the symbolic model, state-explosion problem in traditional verification methods is efficiently avoided. Based on the proposed symbolic model, a reachability analysis procedure is implemented using ANSI C++ on UNIX platform. Thus, a complete tool for verifying the parameterized synchronous systems is obtained and tested for some cases. The experimental results show that the method is satisfactory.
16849942
Tetris: a new register pressure control technique for VLIW processors
The run-time performance of VLIW (very long instruction word) microprocessors depends heavily on the effectiveness of its associated optimizing compiler. Typical VLIW compiler phases include instruction scheduling, which maximizes instruction level parallelism (ILP), and register allocation, which minimizes data spills to external memory. If ILP is maximized without considering register constraints, high register pressure may result, leading to increased spill code and reduced run-time performance. In this paper, a new register pressure reduction technique for embedded VLIW processors is presented to control register pressure prior to instruction scheduling and register allocation. By modifying the relative ordering of operations, this technique restructures code to better reduce spills. Our technique has been implemented in Trimaran, an academic VLIW compiler, and evaluated using a series of VLIW benchmarks. Experimental results show that, on average, our algorithm reduces dynamic spills and improves overall cycle counts by 6% for a VLIW architecture with 8 functional units and 32 registers versus previous spill code reduction techniques.
19638393
Channel surfing: defending wireless sensor networks from jamming and interference
14482286
Document clustering by concept factorization
In this paper, we propose a new data clustering method called concept factorization that models each concept as a linear combination of the data points, and each data point as a linear combination of the concepts. With this model, the data clustering task is accomplished by computing the two sets of linear coefficients, and this linear coefficients computation is carried out by finding the non-negative solution that minimizes the reconstruction error of the data points. The cluster label of each data point can be easily derived from the obtained linear coefficients. This method differs from the method of clustering based on non-negative matrix factorization (NMF) \citeXu03 in that it can be applied to data containing negative values and the method can be implemented in the kernel space. Our experimental results show that the proposed data clustering method and its variations performs best among 11 algorithms and their variations that we have evaluated on both TDT2 and Reuters-21578 corpus. In addition to its good performance, the new method also has the merit in its easy and reliable derivation of the clustering results.
17071534
Document clustering with prior knowledge
Document clustering is an important tool for text analysis and is used in many different applications. We propose to incorporate prior knowledge of cluster membership for document cluster analysis and develop a novel semi-supervised document clustering model. The method models a set of documents with weighted graph in which each document is represented as a vertex, and each edge connecting a pair of vertices is weighted with the similarity value of the two corresponding documents. The prior knowledge indicates pairs of documents that known to belong to the same cluster. Then, the prior knowledge is transformed into a set of constraints. The document clustering task is accomplished by finding the best cuts of the graph under the constraints. We apply the model to the Normalized Cut method to demonstrate the idea and concept. Our experimental evaluations show that the proposed document clustering model reveals remarkable performance improvements with very limited training samples, and hence is a very effective semi-supervised classification tool.