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

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发表于 2025-3-21 17:17:48 | 显示全部楼层 |阅读模式
期刊全称11th International Conference on Practical Applications of Computational Biology & Bioinformatics
影响因子2023Florentino Fdez-Riverola,Mohd Saberi Mohamad,Tiago
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发行地址Latest research on Practical Applications of Computational Biology & Bioinformatics.Results of the 11th International Conference on Practical Applications of Computational Biology & Bioinformatics hel
学科分类Advances in Intelligent Systems and Computing
图书封面Titlebook: 11th International Conference on Practical Applications of Computational Biology & Bioinformatics;  Florentino Fdez-Riverola,Mohd Saberi Mo
影响因子Biological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. The impressive capabilities of next-generation sequencing technologies, together with novel and constantly evolving, distinct types of omics data technologies, have created an increasingly complex set of challenges for the growing fields of Bioinformatics and Computational Biology. .The analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as Databases, Statistics, Data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Clearly, Biology is more and more a science of information and requires tools from the computational sciences. In the last few years, we have seen the rise of a new generation of interdisciplinary scientists with a strong background in the biological and computational sciences. .In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance in boosting the research efforts in the field and contributing to the education of a ne
Pindex Conference proceedings 2017
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978-3-319-60815-0Springer International Publishing AG 2017
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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
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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
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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
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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.
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