Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 5th International Me Francesco Masulli,Roberto Tagliaferri,Gennady

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Coarse-Grained Modeling of the HIV–1 Protease Binding Mechanisms: I. Targeting Structural Flexibilithe HIV–1 protease crystal structures. A microscopic analysis suggests a binding mechanism, in which the HIV–1 protease drugs may exploit the dynamic equilibrium between thermodynamically stable, high affinity complexes with the closed form of the HIV–1 protease and meta–stable intermediate complexes

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Unsupervised Stability-Based Ensembles to Discover Reliable Structures in Complex Bio-molecular Databased on the concept of stability have been proposed to estimate the reliability of each individual cluster as well as the ”optimal” number of clusters. In this conceptual framework a clustering ensemble is obtained through bootstrapping techniques, noise injection into the data or random projection

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Comparative , Evaluation of MYB Transcription Factors in Eucalyptus, Sugarcane and Rice Transcriptommined 237,954 sugarcane and 110,000 eucalyptus ESTs in search of genes related to MYB factors active during abiotic stresses in arabidopsis and rice. Searches revealed relative abundance and diversity of MYB candidate sequences in both transcriptomes. In general, 71 and 42 MYB sequences have been se

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Building Maps of Drugs Mode-of-Action from Gene Expression Data following perturbations with different compounds in a reference data-set, our method realizes an euclidean embedding providing a map of MOAs in which drugs sharing the therapeutic application or a subset of molecular targets lies in close positions. First we build a low-dimensional, visualizable sp

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Evaluation of Osmoprotectants in , Transcriptomeant genes were selected and used in a search for orthologs in the FORESTs database using . procedures. The studied genes were here analyzed for the first time in ., including 51 identified orthologs from nine gene families (.=3, .=4, .=10, .=8, .=6, .=13, .=1, .=3 and .=3). In general, the most repr

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Splice Site Prediction Using Artificial Neural Networksleotides over a gene and predicts possible splice sites. Based on the neural network output, the exact location of the splice site is found using a curve fitting of a parabolic function. The splice site location is predicted without prior knowledge of any sensor signals, like ‘GT’ or ‘GC’ for the do

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Interval Length Analysis in Multi Layer Modelted, via a Monte Carlo simulation, by the distributions of the interval lengths detected using the Multi-Layer Model (.). The . is based on the generation of several sub-samples of an input signal; in particular a set of optimal cut-set thresholds are applied to the data to detect signal properties.