By Sergio Torres-Sánchez, Rosana Montes-Soldado, Nuria Medina-Medina, Andrés R. Masegosa (auth.), Miguel P. Rocha, Juan M. Corchado Rodríguez, Florentino Fdez-Riverola, Alfonso Valencia (eds.)

ISBN-10: 3642199135

ISBN-13: 9783642199134

ISBN-10: 3642199143

ISBN-13: 9783642199141

The progress within the Bioinformatics and Computational Biology fields over the past few years has been outstanding and the fashion is to extend its speed. in truth, the necessity for computational thoughts which can successfully deal with the large quantities of knowledge produced via the recent experimental options in Biology remains to be expanding pushed by way of new advances in subsequent iteration Sequencing, different types of the so referred to as omics information and picture acquisition, simply to identify a number of. The research of the datasets that produces and its integration demand new algorithms and methods from fields akin to Databases, data, info Mining, laptop studying, Optimization, laptop technological know-how and synthetic Intelligence. inside of this state of affairs of accelerating facts availability, platforms Biology has additionally been rising as a substitute to the reductionist view that ruled organic examine within the final a long time. certainly, Biology is an increasing number of a technology of knowledge requiring instruments from the computational sciences. within the previous few years, we've seen the surge of a brand new new release of interdisciplinary scientists that experience a powerful history within the organic and computational sciences. during this context, the interplay of researchers from assorted medical fields is, greater than ever, of ultimate significance boosting the learn efforts within the box and contributing to the schooling of a brand new new release of Bioinformatics scientists. PACBB‘11 hopes to give a contribution to this attempt selling this fruitful interplay. PACBB'11 technical software integrated 50 papers from a submission pool of seventy eight papers spanning many alternative sub-fields in Bioinformatics and Computational Biology. hence, the convention will surely have promoted the interplay of scientists from assorted study teams and with a unique heritage (computer scientists, mathematicians, biologists). The medical content material will surely be demanding and should advertise the advance of the paintings that's being constructed by way of all of the participants.

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A Kolmogorov-Smirnov normality test was applied on the three quantification variables and their results suggest non-significant differences to normality at 95% confidence (p=0,63; p=0,36; p=0,64). Specifically, hsf-1 mutation caused a statistical significant increase in the mean ratio of area of aggregates (inner area of the user interface) to the total area (outer area of the user interface) (Bonferroni mean comparison test, p=2,02×10-4) (Fig. 3a). Similarly, there was a significant increase in mean ratio of the number of aggregates to the total area and in the absolute number of aggregates (number of elements of the user interface) (Bonferroni test, p=9,3×10-5 and p=3,14×10-5, respectively) (Fig.

Short G-rich oligonucleotides as a potential therapeutic for Huntington’s Disease. : Cytosolic chaperonin prevents polyglutamine toxicity with altering the aggregation state. : The threshold for polyglutamine-expansion protein aggregation and cellular toxicity is dynamic and influenced by aging in Caenorhabditis elegans. : Neuronal signaling modulates protein homeostasis in Caenorhabditis elegans postsynaptic muscle cells. : Graph-based tools for microscopic cellular image segmentation. : Object density-based image segmentation and its applications in biomedical image analysis.

Hidden markov models in computational biology: Applications to protein modeling. J. Mol. Biol. : Profile-based string kernels for remote homology detection and motif extraction. J. Bioinform. Comput. Biol. : The spectrum kernel: astring kernel for SVM protein classification. In: Proc. Pac. Symp. , vol. 7, pp. : Mismatch string kernels for discriminative protein classification. Bioinform. 1093/bioinformatics/btg431 30 L. Morgado et al. : Svm-fold: a tool for discriminative multi-class protein fold and superfamily recognition.

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5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011) by Sergio Torres-Sánchez, Rosana Montes-Soldado, Nuria Medina-Medina, Andrés R. Masegosa (auth.), Miguel P. Rocha, Juan M. Corchado Rodríguez, Florentino Fdez-Riverola, Alfonso Valencia (eds.)


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