Titlebook: 11th International Conference on Practical Applications of Computational Biology & Bioinformatics; Florentino Fdez-Riverola,Mohd Saberi Mo

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書目名稱11th International Conference on Practical Applications of Computational Biology & Bioinformatics被引頻次




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書目名稱11th International Conference on Practical Applications of Computational Biology & Bioinformatics讀者反饋




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Amylase

宣傳

978-3-319-60815-0Springer International Publishing AG 2017

信任

Dennis G. de la Torre,Erwin A. Alampaye used to discover proteins that are differentially expressed between two groups (e.g. two disease conditions) obtaining thus a set of potential biomarkers. Biomarker discovery requires a lot of data processing in order to prepare data for analysis or in order to merge data from different sources. T

MAOIS

https://doi.org/10.1007/978-3-031-31553-4e-dependent input usually results in a rather laborious optimization. Here we discuss how the optimization of the input enzyme concentrations might be efficiently reduced to a calculation of reachable sets. Under some general conditions, the original system has star-shaped reachable sets that can be

簡略

教唆

incredulity

DOLT

https://doi.org/10.1007/978-3-030-32126-0nected information. Protein interaction networks form part of this puzzle, and extracting this information from the scientific literature is an important but challenging task..In this work, we present a supervised classification approach for identifying and ranking literature documents that contain

SPURN

https://doi.org/10.1007/978-3-030-32126-0r samples. However, the high dimensionality of gene expression data affects the classification accuracy of an experiment. Thus, feature selection is needed to select informative genes and remove non-informative genes. Some of the feature selection methods, yet, ignore the interaction between genes.