標(biāo)題: Titlebook: Machine Learning in Single-Cell RNA-seq Data Analysis; Khalid Raza Book 2024 The Editor(s) (if applicable) and The Author(s), under exclus [打印本頁] 作者: 我贊成 時間: 2025-3-21 16:46
書目名稱Machine Learning in Single-Cell RNA-seq Data Analysis影響因子(影響力)
書目名稱Machine Learning in Single-Cell RNA-seq Data Analysis影響因子(影響力)學(xué)科排名
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書目名稱Machine Learning in Single-Cell RNA-seq Data Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning in Single-Cell RNA-seq Data Analysis被引頻次
書目名稱Machine Learning in Single-Cell RNA-seq Data Analysis被引頻次學(xué)科排名
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書目名稱Machine Learning in Single-Cell RNA-seq Data Analysis年度引用學(xué)科排名
書目名稱Machine Learning in Single-Cell RNA-seq Data Analysis讀者反饋
書目名稱Machine Learning in Single-Cell RNA-seq Data Analysis讀者反饋學(xué)科排名
作者: 6Applepolish 時間: 2025-3-21 22:53
2191-530X provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. The advent of scRNA-seq technology has revolutionized our understanding of c作者: 營養(yǎng) 時間: 2025-3-22 04:10
Dimensionality Reduction and Clustering,nderlying biological structures. The chapter details PCA and t-SNE algorithms, their applications, and software tools, providing Python-based case studies to demonstrate their practical implementation in scRNA-seq data analysis.作者: 放逐某人 時間: 2025-3-22 05:46 作者: FRAX-tool 時間: 2025-3-22 11:10
Introduction to Single-Cell RNA-seq Data Analysis, single-cell sequencing technologies, the critical impact of scRNA-seq, and the powerful role of machine learning in overcoming analytical challenges, thereby facilitating advancements in personalized medicine and targeted therapies.作者: 催眠 時間: 2025-3-22 14:26 作者: 雀斑 時間: 2025-3-22 18:12 作者: altruism 時間: 2025-3-22 23:50 作者: Ledger 時間: 2025-3-23 01:25 作者: 神圣不可 時間: 2025-3-23 09:11 作者: 偽書 時間: 2025-3-23 13:37
Dimensionality Reduction and Clustering,analysis. Dimensionality reduction, including methods like Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and t-distributed Stochastic Neighbor Embedding (t-SNE), simplifies high-dimensional data for visualization and downstream analysis. Clustering, exempl作者: Arb853 時間: 2025-3-23 16:47 作者: Middle-Ear 時間: 2025-3-23 18:56
Trajectory Inference and Cell Fate Prediction,rofiling of heterogeneous cell populations. This chapter delves into the concepts of trajectory inference and cell fate prediction, essential for understanding dynamic biological processes such as development, differentiation, and cellular response to stimuli. The chapter also highlights various com作者: 哀悼 時間: 2025-3-23 22:55 作者: MANIA 時間: 2025-3-24 05:38
bility of an integrated technological route for recovering titanium from bauxite ore residue was verified in this study. Titanium-bearing iron concentrate was first recycled through magnetic separation process, and titanium was further leached from the non-magnetic material derived from the upper-st作者: 任意 時間: 2025-3-24 10:21 作者: 多節(jié) 時間: 2025-3-24 12:18 作者: 舞蹈編排 時間: 2025-3-24 18:41 作者: 侵害 時間: 2025-3-24 20:41
Khalid Razant developments, discoveries, and practices in primary alumiThe Light Metals symposia at the TMS Annual Meeting & Exhibition present the most recent developments, discoveries, and practices in primary aluminum science and technology. The annual Light Metals volume has become the definitive reference作者: 容易懂得 時間: 2025-3-24 23:35 作者: Canary 時間: 2025-3-25 03:53
Khalid Razaow-grade bauxite ore which is commonly used for the alumina-based abrasives and refractories productions. The alumina-silica and alumina-ferrite complexes are the foremost impurities present in the low-grade bauxite. They affect its commercial utilities due to development of poor binding property in作者: 猛擊 時間: 2025-3-25 08:00 作者: nettle 時間: 2025-3-25 12:13 作者: meditation 時間: 2025-3-25 19:02
Khalid Raza and magnetic recoveries of iron and titanium attained 55.79 and, 17.37% respectively. 96.36% TiO. was subsequently leached from the non-magnetic material under the optimal conditions of sulfuric acid concentration of 8?mol/L, leaching temperature of 70?°C, leaching time of 120?min, and liquid to so作者: granite 時間: 2025-3-25 21:14
Khalid Raza and magnetic recoveries of iron and titanium attained 55.79 and, 17.37% respectively. 96.36% TiO. was subsequently leached from the non-magnetic material under the optimal conditions of sulfuric acid concentration of 8?mol/L, leaching temperature of 70?°C, leaching time of 120?min, and liquid to so作者: Harpoon 時間: 2025-3-26 04:03 作者: Conflagration 時間: 2025-3-26 06:13
Khalid Raza such as sensitivity investigations, “What-if” analyses, production optimization, business planning and estimation of capital investment efficiency. RUSAL has organized departments for mathematical modeling at their refineries and a central development team in its St. Petersburg office. Refinery spe作者: vasculitis 時間: 2025-3-26 08:41
Khalid Razametabolic products can mobilize or polarize different impurities present in the low-grade bauxite by means of the active redox environment created by them in the indigenous atmosphere. Many reports have suggested that . efficiently removes calcium from the low-grade bauxite. Similarly, iron-oxidizin作者: 講個故事逗他 時間: 2025-3-26 14:31 作者: linear 時間: 2025-3-26 19:19
2191-530X ed experts at the intersection of genomics and artificial intelligence, this book serves as a roadmap for leveraging machine learning algorithms to extract meaningful patterns and uncover hidden biological insights within scRNA-seq datasets.?.978-981-97-6702-1978-981-97-6703-8Series ISSN 2191-530X Series E-ISSN 2191-5318 作者: ANA 時間: 2025-3-26 23:03
Machine Learning in Single-Cell RNA-seq Data Analysis作者: debunk 時間: 2025-3-27 02:24
978-981-97-6702-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: Irascible 時間: 2025-3-27 09:01
Machine Learning in Single-Cell RNA-seq Data Analysis978-981-97-6703-8Series ISSN 2191-530X Series E-ISSN 2191-5318 作者: conifer 時間: 2025-3-27 09:47
https://doi.org/10.1007/978-981-97-6703-8Single Cell Data Analysis; Machine Learning in Genomics; Single Cell RNA-seq; Machine Learning in Singl作者: 空氣傳播 時間: 2025-3-27 14:24
Khalid RazaCovers basic concepts of single cell RNA-seq.Discusses integration of ML and scRNA-seq.Presents hands-on examples and case studies作者: avulsion 時間: 2025-3-27 18:57 作者: fleeting 時間: 2025-3-27 23:51
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