標(biāo)題: Titlebook: Big Data Analytics in Genomics; Ka-Chun Wong Book 2016 Springer International Publishing Switzerland (Outside the USA) 2016 Big Data.Genom [打印本頁(yè)] 作者: whiplash 時(shí)間: 2025-3-21 19:30
書目名稱Big Data Analytics in Genomics影響因子(影響力)
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書目名稱Big Data Analytics in Genomics網(wǎng)絡(luò)公開度
書目名稱Big Data Analytics in Genomics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Big Data Analytics in Genomics被引頻次
書目名稱Big Data Analytics in Genomics被引頻次學(xué)科排名
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書目名稱Big Data Analytics in Genomics讀者反饋
書目名稱Big Data Analytics in Genomics讀者反饋學(xué)科排名
作者: gospel 時(shí)間: 2025-3-21 21:37
Causal Inference and Structure Learning of Genotype–Phenotype Networks Using Genetic Variation–phenotype relations. We discuss four recent algorithms for genotype–phenotype network structure learning, namely (1) QTL-directed dependency graph, (2) QTL+Phenotype supervised orientation, (3) QTL-driven phenotype network, and (4) sparsity-aware maximum likelihood (SML).作者: 團(tuán)結(jié) 時(shí)間: 2025-3-22 01:27
State-of-the-Art in Smith–Waterman Protein Database Search on HPC Platforms implementation. Additionally, as energy efficiency is becoming more important every day, we also survey performance/power consumption works. Finally, we give our view on the future of Smith–Waterman protein searches considering next generations of hardware architectures and its upcoming technologie作者: 大吃大喝 時(shí)間: 2025-3-22 06:46 作者: apiary 時(shí)間: 2025-3-22 10:09 作者: inquisitive 時(shí)間: 2025-3-22 13:25
Perspectives of Machine Learning Techniques in Big Data Mining of Cancerional process of various genes identified by different genomics efforts. This might be useful to understand the modern trends and strategies of the fast evolving cancer genomics research. In the recent years, parallel, incremental, and multi-view machine learning algorithms have been proposed. This 作者: cloture 時(shí)間: 2025-3-22 17:33 作者: bypass 時(shí)間: 2025-3-23 00:44 作者: 令人心醉 時(shí)間: 2025-3-23 02:09
A Bioinformatics Approach for Understanding Genotype–Phenotype Correlation in Breast Cancererns, which can assign known phenotypes to BC TN patients, focusing more on paired or more complicated nucleotide/gene mutational patterns, by using three machine learning methods: limitless arity multiple procedure (LAMP), decision trees, and hierarchical disjoint clustering. Association rules obta作者: Adrenaline 時(shí)間: 2025-3-23 06:56 作者: perimenopause 時(shí)間: 2025-3-23 12:06 作者: 外觀 時(shí)間: 2025-3-23 15:42 作者: ineptitude 時(shí)間: 2025-3-23 20:15
Adding Some Atmosphere-Lighting and Fog,ray data, or a combination of these different types of data. Here we review these methods and the state of protein function prediction, emphasizing recent algorithmic developments, remaining challenges, and prospects for future research.作者: 苦惱 時(shí)間: 2025-3-24 00:52 作者: 旁觀者 時(shí)間: 2025-3-24 06:06 作者: Uncultured 時(shí)間: 2025-3-24 08:50
Introduction to Accountancy and Finance data repositories that can be utilized to search for therapeutic targets for cancer treatment. We then introduce software tools frequently used for genomic data mining. Finally, we summarize working algorithms for the discovery of therapeutic biomarkers.作者: 運(yùn)動(dòng)的我 時(shí)間: 2025-3-24 13:25
Introduction to Advanced AstrophysicsROVEAN scoring scheme to assess each variant’s functional consequences, followed by PubMed searches to link the variant to previous reports. Users can then select subjects to visualize their PROVEAN score profiles with Circos diagrams and to compare the proportions of variant occurrences between dif作者: 小口啜飲 時(shí)間: 2025-3-24 16:23
Introduction to Advanced Astrophysicserns, which can assign known phenotypes to BC TN patients, focusing more on paired or more complicated nucleotide/gene mutational patterns, by using three machine learning methods: limitless arity multiple procedure (LAMP), decision trees, and hierarchical disjoint clustering. Association rules obta作者: 碎石 時(shí)間: 2025-3-24 22:07
n areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein978-3-319-82312-6978-3-319-41279-5作者: 詳細(xì)目錄 時(shí)間: 2025-3-25 01:09 作者: SPALL 時(shí)間: 2025-3-25 03:34 作者: accrete 時(shí)間: 2025-3-25 10:28
,Vers un système de gestion de données, association, we examine how the integration of heterogeneous prior knowledge on the correlation structures between SNPs, and between genes can improve the robustness and the interpretability of eQTL mapping.作者: 不妥協(xié) 時(shí)間: 2025-3-25 14:30
Robust Methods for Expression Quantitative Trait Loci Mapping association, we examine how the integration of heterogeneous prior knowledge on the correlation structures between SNPs, and between genes can improve the robustness and the interpretability of eQTL mapping.作者: Compass 時(shí)間: 2025-3-25 18:03 作者: 加劇 時(shí)間: 2025-3-26 00:02
https://doi.org/10.1007/2-287-31090-8ations as well as our contribution to the NP classification theory and algorithms. We also provide simulation examples and a genomic case study to demonstrate how to use the NP classification algorithm in practice.作者: SOB 時(shí)間: 2025-3-26 00:09 作者: 憂傷 時(shí)間: 2025-3-26 05:53
Genomic Applications of the Neyman–Pearson Classification Paradigmations as well as our contribution to the NP classification theory and algorithms. We also provide simulation examples and a genomic case study to demonstrate how to use the NP classification algorithm in practice.作者: 冒煙 時(shí)間: 2025-3-26 11:30 作者: cylinder 時(shí)間: 2025-3-26 15:58 作者: 沉默 時(shí)間: 2025-3-26 16:54
Book 2016hroughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. ?To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be su作者: BRUNT 時(shí)間: 2025-3-26 21:38 作者: 善辯 時(shí)間: 2025-3-27 01:44 作者: 凹槽 時(shí)間: 2025-3-27 06:35 作者: 澄清 時(shí)間: 2025-3-27 09:34
Genomic Applications of the Neyman–Pearson Classification Paradigmcontrolling type I error under some specified level ., usually a small number. This problem is often faced in many genomic applications involving binary classification tasks. The terminology Neyman–Pearson classification paradigm arises from its connection to the Neyman–Pearson paradigm in hypothesi作者: 拉開這車床 時(shí)間: 2025-3-27 15:10
Improving Re-annotation of Annotated Eukaryotic Genomese annotated sequenced genome of the corresponding organism and improve the existing gene models. In addition, misleading annotations propagate in multiple databases by comparative approaches of annotation, automatic annotation, and lack of curating power in the face of large data volume. In this pur作者: BAIT 時(shí)間: 2025-3-27 17:48 作者: ICLE 時(shí)間: 2025-3-27 23:36 作者: AROMA 時(shí)間: 2025-3-28 05:01
Genome-Wide Mapping of Nucleosome Position and Histone Code Polymorphisms in Yeasthanges. Its positioning across the genome leaves a significant impact on the DNA dependent processes, particularly on gene regulation. Though they form structural repeating units of chromatin they differ from each other by DNA/histone covalent modifications establishing diversity in natural populati作者: mechanical 時(shí)間: 2025-3-28 08:26 作者: 性別 時(shí)間: 2025-3-28 14:24
Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Alg entire genome at the DNA, RNA, protein, and epigenetic levels. Due to the complex nature of cancer, several organizations have launched comprehensive molecular profiling for thousands of cancer patients using multiple high-throughput technologies to investigate cancer genomics, transcriptomics, pro作者: Neolithic 時(shí)間: 2025-3-28 17:34
NGS Analysis of Somatic Mutations in Cancer Genomese analysis of these data has confirmed the early predictions of extensive sequence and structural diversity of cancer genomes, fueling the development of new computational approaches to decipher inter- and intratumoral somatic variation within and among cancer patients. Overall, these techniques hav作者: 過去分詞 時(shí)間: 2025-3-28 22:02
OncoMiner: A Pipeline for Bioinformatics Analysis of Exonic Sequence Variants in Cancerhich scientists can explore the overall mutational landscape in patients with various types of cancers. We have developed the OncoMiner pipeline for mining WES data to identify exonic sequence variants, link them with associated research literature, visualize their genomic locations, and compare the作者: CHOKE 時(shí)間: 2025-3-29 01:10
A Bioinformatics Approach for Understanding Genotype–Phenotype Correlation in Breast Cancerreatments. The serious problem is that the patients, called “triple negative” (TN), who cannot be fallen into any of these three categories, have no clear treatment options. Thus linking TN patients to the main three phenotypes clinically is very important. Usually BC patients are profiled by gene e作者: 斜谷 時(shí)間: 2025-3-29 04:54 作者: 盡忠 時(shí)間: 2025-3-29 10:58
,Vers un système de gestion de données,arch interest. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A?major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to b作者: Lipohypertrophy 時(shí)間: 2025-3-29 12:01
https://doi.org/10.1007/2-287-31090-8measurements. Causal networks have been widely used in systems genetics for modeling gene regulatory systems and for identifying causes and risk factors of diseases. In this chapter, we describe fundamental concepts and algorithms for constructing causal networks from observational data. In biologic作者: 打火石 時(shí)間: 2025-3-29 17:42
https://doi.org/10.1007/2-287-31090-8controlling type I error under some specified level ., usually a small number. This problem is often faced in many genomic applications involving binary classification tasks. The terminology Neyman–Pearson classification paradigm arises from its connection to the Neyman–Pearson paradigm in hypothesi作者: modifier 時(shí)間: 2025-3-29 19:54
https://doi.org/10.1007/2-287-31090-8e annotated sequenced genome of the corresponding organism and improve the existing gene models. In addition, misleading annotations propagate in multiple databases by comparative approaches of annotation, automatic annotation, and lack of curating power in the face of large data volume. In this pur作者: 自作多情 時(shí)間: 2025-3-30 03:17
https://doi.org/10.1007/2-287-31090-8 accurate method for this kind of search. Unfortunately, this algorithm is computationally demanding and the situation gets worse due to the exponential growth of biological data in the last years. For that reason, the scientific community has made great efforts to accelerate Smith–Waterman biologic作者: 抵押貸款 時(shí)間: 2025-3-30 04:53 作者: CAB 時(shí)間: 2025-3-30 10:38 作者: 克制 時(shí)間: 2025-3-30 13:52
Introduction to Abdominal Ultrasonographyhigh-throughput techniques. Analysis and interpretation of the immense amount of data that gets produced from clinical samples is highly complicated and it remains as a great challenge. The future of cancer medical discoveries will mostly depend on our ability to process and analyze large genomic da作者: CLAP 時(shí)間: 2025-3-30 18:51
Introduction to Accountancy and Finance entire genome at the DNA, RNA, protein, and epigenetic levels. Due to the complex nature of cancer, several organizations have launched comprehensive molecular profiling for thousands of cancer patients using multiple high-throughput technologies to investigate cancer genomics, transcriptomics, pro作者: Acetabulum 時(shí)間: 2025-3-31 00:39
