Scientific Programming - Volume 16, issue 1

Authors: Perrott, Ron

Article Type: Other

DOI: 10.3233/SPR-2008-0244

Citation: Scientific Programming, vol. 16, no. 1, pp. 1-1, 2008

Authors: Zaki, Mohammed J. | Ramakrishnan, Naren | Parthasarathy, Srinivasan

Article Type: Other

DOI: 10.3233/SPR-2008-0245

Citation: Scientific Programming, vol. 16, no. 1, pp. 3-3, 2008

Authors: King, Brian R. | Guda, Chittibabu

Article Type: Research Article

Abstract: Protein sequence data continue to become available at an exponential rate. Annotation of functional and structural attributes of these data lags far behind, with only a small fraction of the data understood and labeled by experimental methods. Classification methods that are based on semi-supervised learning can increase the overall accuracy of classifying partly labeled data in many domains, but very few methods exist that have shown their effect on protein sequence classification. We show how proven methods from text classification can be applied to protein sequence data, as we consider both existing and novel extensions to the basic methods, and …demonstrate restrictions and differences that must be considered. We demonstrate comparative results against the transductive support vector machine, and show superior results on the most difficult classification problems. Our results show that large repositories of unlabeled protein sequence data can indeed be used to improve predictive performance, particularly in situations where there are fewer labeled protein sequences available, and/or the data are highly unbalanced in nature. Show more

Keywords: Bioinformatics, protein sequence classification, semi-supervised learning, expectation maximization, EM

DOI: 10.3233/SPR-2008-0241

Citation: Scientific Programming, vol. 16, no. 1, pp. 5-29, 2008

Authors: Zhang, Ji | Gao, Qigang | Wang, Hai

Article Type: Research Article

Abstract: Discovering gene co-regulatory relationships is one of most important research in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a particular gene of interest (called target gene), identify the condition subsets where strong gene co-regulations of the target gene are observed and its co-regulated genes in these condition subsets. The co-regulations are local in the sense that they occur in some subsets of full experimental conditions. The study on this problem can contribute to better understanding and characterizing the target gene during the biological activity involved. In this paper, we propose an innovative method …for finding gene specific co-regulations using genetic algorithm (GA). A sliding window is used to delimit the allowed length of conditions in which gene co-regulations occur and an ad hoc GA, called the progressive GA, is performed in each window position to find those condition subsets having high fitness. It is called progressive because the initial population for the GA in a window position inherits the top-ranked individuals obtained in its preceding window position, enabling the GA to achieve a better accuracy than the non-progressive algorithm. kNN Lookup Table is utilized to substantially speed up fitness evaluation in the GA. Experimental results with a real-life gene expression data demonstrate the efficiency and effectiveness of our technique in discovering gene specific co-regulations. Show more

DOI: 10.3233/SPR-2008-0243

Citation: Scientific Programming, vol. 16, no. 1, pp. 31-47, 2008

Authors: Patnaik, Debprakash | Sastry, P.S. | Unnikrishnan, K.P.

Article Type: Research Article

Abstract: Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery …framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity. Show more

Keywords: Data mining, temporal data mining, temporal constraints, frequent episodes, multiple neural spike train data, spike train data analysis, functional connectivity, neuronal connectivity patterns, synfire chains, synchrony

DOI: 10.3233/SPR-2008-0242

Citation: Scientific Programming, vol. 16, no. 1, pp. 49-77, 2008

Article Type: Other

Citation: Scientific Programming, vol. 16, no. 1, pp. 79-79, 2008

Authors: Hascoët, L. | Utke, J. | Naumann, U.

Article Type: Research Article

Abstract: The reverse mode of automatic differentiation is widely used in science and engineering. A severe bottleneck for the performance of the reverse mode, however, is the necessity to recover certain intermediate values of the program in reverse order. Among these values are computed addresses, which traditionally are recovered through forward recomputation and storage in memory. We propose an alternative approach for recovery that uses inverse computation based on dependency information. Address storage constitutes a significant portion of the overall storage requirements. An example illustrates substantial gains that the proposed approach yields, and we show use cases in practical applications.

Keywords: Program transformation, automatic differentiation, inverse computation

DOI: 10.3233/SPR-2008-0238

Citation: Scientific Programming, vol. 16, no. 1, pp. 81-92, 2008

Authors: Wester, Michael

Article Type: Other

DOI: 10.3233/SPR-2008-0237

Citation: Scientific Programming, vol. 16, no. 1, pp. 93-96, 2008

Authors: Bailey, David H.

Article Type: Book Review

DOI: 10.3233/SPR-2008-0239

Citation: Scientific Programming, vol. 16, no. 1, pp. 97-98, 2008

Article Type: Other

DOI: 10.3233/SPR-2008-0240

Citation: Scientific Programming, vol. 16, no. 1, pp. 99-99, 2008