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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 2025-3-28 15:28:38 | 只看該作者
Computational Methods for Elucidating Gene Expression Regulation in Bacteria,umerous small noncoding RNAs (sRNAs) which are ubiquitous regulators of gene expression, (2) a flexible and diverse promoter structure, and (3) transcription termination as another means of gene expression regulation..To understand bacteria gene expression regulation, one needs to identify promoters
42#
發(fā)表于 2025-3-28 19:52:16 | 只看該作者
Computational Approaches for De Novo Drug Design: Past, Present, and Future,o improve the efficiency and decrease costs to develop novel drugs. Over several decades, a variety of methods have been proposed and applied in practice. Traditionally, drug design problems are always taken as combinational optimization in discrete chemical space. Hence optimization methods were ex
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發(fā)表于 2025-3-28 23:09:13 | 只看該作者
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發(fā)表于 2025-3-29 03:26:53 | 只看該作者
Building and Interpreting Artificial Neural Network Models for Biological Systems,formation from these data sets requires the use of sophisticated modeling approaches. Toward that, artificial neural network (ANN) based modeling is increasingly playing a very important role. The “black box” nature of ANNs acts as a barrier in providing biological interpretation of the model. Here,
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發(fā)表于 2025-3-29 10:19:03 | 只看該作者
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發(fā)表于 2025-3-29 12:44:55 | 只看該作者
Applying Machine Learning for Integration of Multi-Modal Genomics Data and Imaging Data to Quantifyignificantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in computational infrastructure and analysis algorithms are realizing image-based digital diagnosis not only in radiology and cardiology but also oncology and other diseases. Machine learning methods, espec
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發(fā)表于 2025-3-29 17:14:14 | 只看該作者
48#
發(fā)表于 2025-3-29 20:10:29 | 只看該作者
49#
發(fā)表于 2025-3-30 02:22:01 | 只看該作者
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發(fā)表于 2025-3-30 08:02:10 | 只看該作者
Using Neural Networks for Relation Extraction from Biomedical Literature,understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichan
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