標(biāo)題: Titlebook: Computational Reconstruction of Missing Data in Biological Research; Feng Bao Book 2021 Tsinghua University Press 2021 Machine Learning.Bi [打印本頁(yè)] 作者: 深謀遠(yuǎn)慮 時(shí)間: 2025-3-21 16:04
書(shū)目名稱(chēng)Computational Reconstruction of Missing Data in Biological Research影響因子(影響力)
書(shū)目名稱(chēng)Computational Reconstruction of Missing Data in Biological Research影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Computational Reconstruction of Missing Data in Biological Research網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Computational Reconstruction of Missing Data in Biological Research網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Computational Reconstruction of Missing Data in Biological Research被引頻次
書(shū)目名稱(chēng)Computational Reconstruction of Missing Data in Biological Research被引頻次學(xué)科排名
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書(shū)目名稱(chēng)Computational Reconstruction of Missing Data in Biological Research讀者反饋
書(shū)目名稱(chēng)Computational Reconstruction of Missing Data in Biological Research讀者反饋學(xué)科排名
作者: brother 時(shí)間: 2025-3-21 21:47 作者: 辭職 時(shí)間: 2025-3-22 00:51 作者: 咆哮 時(shí)間: 2025-3-22 04:48 作者: Hangar 時(shí)間: 2025-3-22 11:15
Computational Reconstruction of Missing Data in Biological Research作者: Collision 時(shí)間: 2025-3-22 15:57 作者: Collision 時(shí)間: 2025-3-22 17:16
Fast Computational Recovery of Missing Features for Large-scale Biological Data, focuses on missing gene features in single-cell transcriptomics data. In the rapidly development of single-cell sequencing, the latest technological advances have made it possible to measure the intrinsic activity of single cells on a large scale, and enable to analyze the composition of cells with作者: Corroborate 時(shí)間: 2025-3-23 01:12 作者: Magnificent 時(shí)間: 2025-3-23 02:11 作者: DEI 時(shí)間: 2025-3-23 07:42 作者: 多嘴 時(shí)間: 2025-3-23 11:52 作者: Obstruction 時(shí)間: 2025-3-23 14:59
Challenges of Real-Time Decision Supportr most of existing datasets, only about 20% of the genetic profiles can be effectively measured. Facing this problem, this chapter proposes deep recurrent autoencoder learning to achieve accurate and rapid imputation of missing gene expressions from millions of cell expression data.作者: 講個(gè)故事逗他 時(shí)間: 2025-3-23 19:51 作者: Obsessed 時(shí)間: 2025-3-23 23:49
Fast Computational Recovery of Missing Features for Large-scale Biological Data,r most of existing datasets, only about 20% of the genetic profiles can be effectively measured. Facing this problem, this chapter proposes deep recurrent autoencoder learning to achieve accurate and rapid imputation of missing gene expressions from millions of cell expression data.作者: dragon 時(shí)間: 2025-3-24 05:54 作者: 有偏見(jiàn) 時(shí)間: 2025-3-24 07:04
Emily Banwell,Terry Hanley,Aaron Sefisis of internal structure of the data, the proposed method tries to rebalance the unbalanced data. On the association analysis and prediction tasks, we demonstrate the strucure-aware rebalancing method can efficiently improve the analysis of imbalanced data.作者: 高腳酒杯 時(shí)間: 2025-3-24 14:33
Computational Recovery of Sample Missings,sis of internal structure of the data, the proposed method tries to rebalance the unbalanced data. On the association analysis and prediction tasks, we demonstrate the strucure-aware rebalancing method can efficiently improve the analysis of imbalanced data.作者: Afflict 時(shí)間: 2025-3-24 15:16 作者: 巨大沒(méi)有 時(shí)間: 2025-3-24 22:23
Murray Turoff,Connie White,Linda Plotnickpast decade, the vigorous development of new biological technologies has provided effective tools for life science study, making it possible to collect biological data and reveal the life science functionalities on large scale, deep level, and multiple angles. Deriving meaningful biological conclusi作者: 不能妥協(xié) 時(shí)間: 2025-3-25 02:35 作者: CAMP 時(shí)間: 2025-3-25 06:00 作者: 沒(méi)有貧窮 時(shí)間: 2025-3-25 09:06 作者: Ruptured-Disk 時(shí)間: 2025-3-25 15:35 作者: Sigmoidoscopy 時(shí)間: 2025-3-25 16:23 作者: 特別容易碎 時(shí)間: 2025-3-25 20:04
Mixed Methods Research to Build Bridgestuation, we proposed different computational recovery theories under the machine learning framework, to impute missing biological observations, improve data quality, and improve the interpretation of biological data.作者: 否決 時(shí)間: 2025-3-26 00:51 作者: theta-waves 時(shí)間: 2025-3-26 06:44
https://doi.org/10.1007/978-981-16-3064-4Machine Learning; Biological data analysis; Data imputation; Imbalance learning; Single-cell analysis作者: 不能平靜 時(shí)間: 2025-3-26 12:08
978-981-16-3063-7Tsinghua University Press 2021作者: 牽連 時(shí)間: 2025-3-26 13:57
Springer Theseshttp://image.papertrans.cn/c/image/232935.jpg作者: 替代品 時(shí)間: 2025-3-26 17:45
Enzyme-Carrying Electrospun Nanofibers,ation supports. This chapter describes a protocol for the preparation of nanofibrous enzyme, involving the synthesis and end-group functionalization of polystyrene, production of electrospun nanofibers, and surface immobilization of enzyme via covalent attachment.作者: ANIM 時(shí)間: 2025-3-27 00:45 作者: GUEER 時(shí)間: 2025-3-27 04:26
ychotherapists: the neurotic is plunged into despair because he takes the world, life, situations, people and things badly. The radical treatment of pathological stress caused by neurosis calls for the elimination of this latter. I shall briefly discuss neuroses, their mechanisms and some ways of tr作者: antipsychotic 時(shí)間: 2025-3-27 06:51
Book 2003n Jahre Industrieerfahrung. Dr. Werner Becker ist Associate Partner bei Accenture. Er verfügt über langj?hrige.Industrieerfahrung (u. a. Gesch?ftsleitungsmitglied Roche Schweiz). Andreas Fibig ist Gesch?ftsführer der Pharmacia GmbH in Erlangen.作者: 討好女人 時(shí)間: 2025-3-27 10:26 作者: antiandrogen 時(shí)間: 2025-3-27 16:06
Quantum Information Meets Quantum Matter978-1-4939-9084-9Series ISSN 2364-9054 Series E-ISSN 2364-9062 作者: Engaging 時(shí)間: 2025-3-27 19:27
