Prostaglandins 发表于 2025-3-23 13:34:47

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perjury 发表于 2025-3-23 15:46:40

Qionghai Dai,Yue Gaofor muscular dystrophy is explored here as an example and text mining approaches are utilized with an aim to identify promisingly novel drug targets. Our main objective is to give a basic overview from a biologist’s perspective for whom text mining approaches of data mining and information retrieval

热烈的欢迎 发表于 2025-3-23 21:48:32

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灵敏 发表于 2025-3-24 01:58:32

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悄悄移动 发表于 2025-3-24 06:24:23

Qionghai Dai,Yue Gaoimensional (3D) in vitro model is 3D bioprinting, which makes realistic in vitro disease models and mimics the actual cellular arrangement of any human tissue or organ. Organ-on-a-chip as 3D in vitro model has shown its potential to understand the disease mechanism along with evaluation of new thera

抓住他投降 发表于 2025-3-24 08:40:04

Qionghai Dai,Yue Gaoh essentially integrated nanofluidics into microfluidics. A microscopic theoretical model has also been presented which gives insight of CAD and may help in effectively guiding the researchers to develop various methods to quantify its various aspects. Various useful software have also been listed i

极力证明 发表于 2025-3-24 14:08:36

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正式通知 发表于 2025-3-24 15:16:08

Qionghai Dai,Yue GaoFor the integration of IoT and cloud, the key factors to reckon are: validation methods and IoT framework, which must be reliable and secure. This chapter is an exhaustive analysis of the IoT, Cloud, Cloud model, integration of Cloud with IoT, and its applications in biomedical. The study also enlis

thalamus 发表于 2025-3-24 22:22:45

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Jogging 发表于 2025-3-24 23:58:13

2365-3051 cial media analysis, .etc.. We summarize these attempts as a new computing paradigm, called hypergraph computation, which is to formulate thehigh-order correlations underneath the data using hypergraph, and the978-981-99-0187-6978-981-99-0185-2Series ISSN 2365-3051 Series E-ISSN 2365-306X
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查看完整版本: Titlebook: Hypergraph Computation; Qionghai Dai,Yue Gao Book‘‘‘‘‘‘‘‘ 2023 The Editor(s) (if applicable) and The Author(s) 2023 Hypergraph.Hypergraph