18125589
Zerg: A Very Fast BLAST Parser Library
SUMMARY
Zerg is a library of sub-routines that parses the output from all NCBI BLAST programs (Blastn, Blastp, Blastx, Tblastn and Tblastx) and returns the attributes of a BLAST report to the user. It is optimized for speed, being especially useful for large-scale genomic analysis. Benchmark tests show that Zerg is over two orders of magnitude faster than some widely used BLAST parsers.


AVAILABILITY
http://bioinfo.iq.usp.br/zerg
32839673
Genomics and Gene Expression Management Tools for the Schistosoma Mansoni cDNA Microarray Project
IV BSB 22 Favor ver os Anais do Simpósio em Springer Verlag, Lecture Notes in Bioinformatics (LNBI número 3594) para este trabalho.
30725726
An integrated MOLP method base package - A guided tour of tommix
Abstract An implementation of a MOLP interactive method base is presented in the form of a guided tour to a computer package. The MOLP method base (associated to a procedure toolbox) is a component of a decision support system framework which also integrates a dialogue base and a data management module. Besides providing the standard working of each method the interactive MOLP method base offers the possibility of method switching at any interaction with the decision maker (DM), in order to make the most of the potentialities of each method and support a “learning” process. Some key issues in the development of interactive MOLP approaches and computational tools are also discussed. Special attention is paid to the development of an user-friendly environment for interacting with the DM.
12706002
Computer-Assisted Discrepancy Management - A Case Study in Research Transfer to Industry
This paper reports on the “repackaging” and transfer to industry of research results in ViewpointBased Requirements Engineering (VBRE). Our experience, as indicated here, is a particular case of the problems and solutions that small research groups can find in their transfer from theoretical work to practical application. In particular, starting with a formal approach for discrepancy management, we first developed a “light-weight version” of VBRE and empirically validated their most important claims. Then we developed a software tool (discRman) that encapsulates the main points of the approach and, finally, partner organizations used it in small projects. We think that this “theoretical-empirical-light-weight-tool” sequence can be applied in similar situations, especially by small research groups seeking for transfer of their results to industry. ACM Classification: D.2.1 (Software Engineering – Requirements/Specifications – Methodologies, Tools), D.2.8 (Software Engineering – Metrics – Performance measures), D.2.9 (Software Engineering – Management – Productivity)
7409552
Swarm optimisation as a new tool for data mining
This paper proposes the use of particle swarm optimisers as a tool for data mining. To evaluate its usefulness, we empirically compare the performance of three variants of the particle optimiser with another evolutionary algorithm, namely a genetic algorithm, in rule discovery for classification tasks. Such tasks are considered core tools for decision support systems in a widespread area, ranging from the industry, commerce, military and scientific fields. The data sources used here for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that particle swarm optimisers are competitive with other evolutionary techniques, and can be successfully applied to more demanding problem domains.
15673525
Genetic Algorithms for Component Analysis
Adapted waveform analysis is used extensively in audio, speech, and video coding. In this context, the basic problem is to find out the best representation for a given signal. The strategy is to decompose the signal into time-frequency atoms. The main analysis tool is the expansion of the signal in or-thonormal bases whose elements have good time-frequency localizations. We show that genetic algorithms can be exploited to search libraries of bases for atomic pattern recognition in noisy signals. The proposed algorithm generates best basis decomposition trees through the evolution of well-adapted genetic sequences. Two new types of constraint operators are introduced to guarantee that valid genetic sequences are generated. The approach behaves as an atomic analyzer for signal processing. A test environment is proposed to assess recognition of time-frequency atoms in signal spaces.
14281129
An abstraction for awareness management in collaborative virtual environments
This paper describes an object-oriented abstraction for the problem of awareness management in Collaborative Virtual Environments (CVEs). The described abstraction allows for different types of awareness information and awareness management policies to be used. It is also described how the defined abstraction was used to support the awareness management policies of two demo CVEs application.