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Titlebook: Artificial Neural Networks; Hugh Cartwright Book 2021Latest edition Springer Science+Business Media, LLC, part of Springer Nature 2021 bio

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發(fā)表于 2025-3-21 16:23:13 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Neural Networks
影響因子2023Hugh Cartwright
視頻videohttp://file.papertrans.cn/163/162628/162628.mp4
發(fā)行地址Includes cutting-edge methods and protocols.Provides step-by-step detail essential for reproducible results.Contains key notes and implementation advice from the experts?
學(xué)科分類Methods in Molecular Biology
圖書(shū)封面Titlebook: Artificial Neural Networks;  Hugh Cartwright Book 2021Latest edition Springer Science+Business Media, LLC, part of Springer Nature 2021 bio
影響因子.This volume presents examples of how Artificial Neural Networks (ANNs) are applied in biological sciences and related areas. Chapters cover a wide variety of topics, including the analysis of intracellular sorting information,?prediction of the behavior of bacterial communities, biometric authentication, studies of Tuberculosis, gene signatures in breast cancer classification, the use of mass spectrometry in metabolite identification,?visual navigation, and?computer diagnosis. Written in the highly successful?.Methods in Molecular Biology.?series format, chapters include introductions to their respective topics, application details for both the expert and non-expert reader, and tips on troubleshooting and avoiding known pitfalls...?..Authoritative and practical,?.Artificial Neural Networks: Third Edition. should be of value to all scientists interested in the hands-on application of ANNs in the biosciences..
Pindex Book 2021Latest edition
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書(shū)目名稱Artificial Neural Networks影響因子(影響力)




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,Identifying Genotype–Phenotype Correlations via Integrative Mutation Analysis,ng variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype–phenotype correlation methods by using the wealth of protein structural information.
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Siamese Neural Networks: An Overview,cosine distance. The output generated by a siamese neural network execution can be considered the semantic similarity between the projected representation of the two input vectors. In this overview we first describe the siamese neural network architecture, and then we outline its main applications i
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