Titlebook: Biological and Artificial Intelligence Environments; Bruno Apolloni,Maria Marinaro,Roberto Tagliaferri Conference proceedings 2005 Springe

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書(shū)目名稱(chēng)Biological and Artificial Intelligence Environments影響因子(影響力)




書(shū)目名稱(chēng)Biological and Artificial Intelligence Environments影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Biological and Artificial Intelligence Environments網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Biological and Artificial Intelligence Environments網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Biological and Artificial Intelligence Environments被引頻次




書(shū)目名稱(chēng)Biological and Artificial Intelligence Environments被引頻次學(xué)科排名




書(shū)目名稱(chēng)Biological and Artificial Intelligence Environments年度引用




書(shū)目名稱(chēng)Biological and Artificial Intelligence Environments年度引用學(xué)科排名




書(shū)目名稱(chēng)Biological and Artificial Intelligence Environments讀者反饋




書(shū)目名稱(chēng)Biological and Artificial Intelligence Environments讀者反饋學(xué)科排名

一起

https://doi.org/10.1007/978-3-7091-6191-3f diseases. We describe the workflow of a proteomic experiment for early detection of cancer which combines MS and DM, giving details of sample treatment and preparation, MS data generation, MS data preprocessing, data clustering and classification.

blight

https://doi.org/10.1007/978-3-7091-6191-3the data by randomly sampling subsets of features and improving accuracy by aggregating the resulting base classifiers. In this paper we experiment the combination of random subspace with feature selection methods, showing preliminary experimental results that seem to confirm the effectiveness of the proposed approach.

弄污

Entrancing

Formidable

P. Kitslaar,M. Lemson,C. Schreurs,H. Bergs traditional MLP. Test error below 25% is archived by all architectures in two different simulation strategies. EαNet performance are 1 to 2%better on test error with respect to the other two architectures using the smaller network topology. The design of a digital implementation of the proposed neural network is also outlined.

GLOOM

Progengrid: A Grid Framework for Bioinformaticsion to simplify interaction between bioinformatics tools and biological databases. This paper presents ProGenGrid (Proteomics & Genomics Grid), a distributed and ubiquitous grid environment, accessible through the web, for supporting “.” experiments in bioinformatics.

Substitution

Mass Spectrometry Data Analysis for Early Detection of Inherited Breast Cancerf diseases. We describe the workflow of a proteomic experiment for early detection of cancer which combines MS and DM, giving details of sample treatment and preparation, MS data generation, MS data preprocessing, data clustering and classification.

Felicitous

Feature Selection Combined with Random Subspace Ensemble for Gene Expression Based Diagnosis of Malithe data by randomly sampling subsets of features and improving accuracy by aggregating the resulting base classifiers. In this paper we experiment the combination of random subspace with feature selection methods, showing preliminary experimental results that seem to confirm the effectiveness of the proposed approach.

敘述