標(biāo)題: Titlebook: Data Mining for Systems Biology; Methods and Protocol Hiroshi Mamitsuka Book 2018Latest edition Springer Science+Business Media, LLC, part [打印本頁] 作者: ONSET 時(shí)間: 2025-3-21 17:32
書目名稱Data Mining for Systems Biology影響因子(影響力)
書目名稱Data Mining for Systems Biology影響因子(影響力)學(xué)科排名
書目名稱Data Mining for Systems Biology網(wǎng)絡(luò)公開度
書目名稱Data Mining for Systems Biology網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Mining for Systems Biology被引頻次
書目名稱Data Mining for Systems Biology被引頻次學(xué)科排名
書目名稱Data Mining for Systems Biology年度引用
書目名稱Data Mining for Systems Biology年度引用學(xué)科排名
書目名稱Data Mining for Systems Biology讀者反饋
書目名稱Data Mining for Systems Biology讀者反饋學(xué)科排名
作者: 教育學(xué) 時(shí)間: 2025-3-21 22:37 作者: 瘙癢 時(shí)間: 2025-3-22 04:04 作者: 效果 時(shí)間: 2025-3-22 06:38 作者: 正面 時(shí)間: 2025-3-22 11:02
Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG,ing an increasingly serious threat to the global public health. Here we present how knowledge on AMR is accumulated in the KEGG Pathogen resource and how such knowledge can be utilized by BlastKOALA and other web tools.作者: AUGUR 時(shí)間: 2025-3-22 13:17 作者: AUGUR 時(shí)間: 2025-3-22 19:06
Commercial Aircraft Composite Technologyles analyzed by short read high-throughput sequencing. By grouping closely related strains together in clusters, the BIB pipeline is capable of estimating the relative abundances of the clusters contained in a sequencing sample.作者: 非實(shí)體 時(shí)間: 2025-3-23 01:15
https://doi.org/10.1007/978-3-319-31918-6. We provide a step-by-step guideline on how we trained the classification models and how it can easily generalize to user-defined reference genomes and specific applications. We also give additional details on what effect parameters in the algorithm have on performances.作者: FLAG 時(shí)間: 2025-3-23 04:51 作者: 愛哭 時(shí)間: 2025-3-23 06:21 作者: JECT 時(shí)間: 2025-3-23 12:52
Wheels, Brakes, and Landing Gearor manipulating a gene set network, where users can find target gene sets based on the enriched network. This chapter provides a user guide/instruction of SiBIC with background of having developed this software. SiBIC is available at ..作者: PALL 時(shí)間: 2025-3-23 16:02 作者: decode 時(shí)間: 2025-3-23 18:30 作者: 音樂會(huì) 時(shí)間: 2025-3-24 01:25
Generative Models for Quantification of DNA Modifications, parallel destructive genomic measurements. . also considers the variation in measurements introduced by different imperfect experimental steps; the experimental variation can be quantified by using appropriate spike-in controls, allowing . to deconvolve the measurements and recover accurately the underlying signal.作者: Ibd810 時(shí)間: 2025-3-24 05:47 作者: MODE 時(shí)間: 2025-3-24 06:40 作者: maroon 時(shí)間: 2025-3-24 13:42
,Sparse Modeling to Analyze Drug–Target Interaction Networks,us on drug chemical substructures and protein domains. Workflows for applying these methods are presented, and an application is described in detail. We consider the characteristics of each method and suggest possible directions for future research.作者: linguistics 時(shí)間: 2025-3-24 15:51 作者: Detonate 時(shí)間: 2025-3-24 19:17
Hiroshi MamitsukaIncludes cutting-edge techniques for data mining in systems biology.Provides step-by-step guidance essential for reproducible results.Contains expert tips and implementation advice from practitioners 作者: 女上癮 時(shí)間: 2025-3-25 01:30
Methods in Molecular Biologyhttp://image.papertrans.cn/d/image/262955.jpg作者: 呼吸 時(shí)間: 2025-3-25 05:56
DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank,d into two types: feature-based and similarity-based methods. By utilizing the “Learning to rank” framework, we propose a new method, DrugE-Rank, to combine these two different types of methods for improving the prediction performance of new candidate drugs and targets. DrugE-Rank is available at ..作者: Exclude 時(shí)間: 2025-3-25 10:26
Data Mining for Systems Biology978-1-4939-8561-6Series ISSN 1064-3745 Series E-ISSN 1940-6029 作者: 遠(yuǎn)足 時(shí)間: 2025-3-25 12:18 作者: genuine 時(shí)間: 2025-3-25 15:56
https://doi.org/10.1007/978-1-4939-8561-6Metagenomics; Epigenomics; Metabolomics; Data sciences; Machine learning; Pharmaceutical science; Artifici作者: 我怕被刺穿 時(shí)間: 2025-3-25 23:47 作者: 食物 時(shí)間: 2025-3-26 03:53 作者: 泥沼 時(shí)間: 2025-3-26 07:54 作者: FLAIL 時(shí)間: 2025-3-26 11:44
Commercial Applications of Ionic LiquidsDatabase Project, and IMG, handling massive amounts of raw data and meta information. 16s rRNA gene contains hypervariable regions with great classification power. As a result, numerous classification tools have emerged including state-of-the-art tools such as Mothur, Qiime, and the 16s classifier. 作者: Exploit 時(shí)間: 2025-3-26 12:55
Commercial Applications of Ionic Liquidsdate no single experiment can capture these separate modifications, and integrative experimental designs are needed to fully characterize cytosine methylation and chemical modification. This chapter describes a generative probabilistic model, ., for integrative analysis of cytosine methylation and i作者: 敲詐 時(shí)間: 2025-3-26 20:27
Julia L. Shamshina,Robin D. Rogersed approaches like the Illumina 450 K or 850 K EPIC chips have been used in a wide range of studies mostly comparing a disease group with healthy control, but also to correlate with survival times, for instance. Processing, normalization, and analysis of raw data require extensive knowledge in stati作者: 為敵 時(shí)間: 2025-3-26 23:02 作者: 腐蝕 時(shí)間: 2025-3-27 03:11 作者: 以煙熏消毒 時(shí)間: 2025-3-27 07:35
Wheels, Brakes, and Landing Gearnsional clustering. For understanding the biological features of genes in a single bicluster, visualizations such as heatmaps or parallel coordinate plots and tools for enrichment analysis are widely used. However, simultaneously handling many biclusters still remains a challenge. Thus, we developed作者: Guileless 時(shí)間: 2025-3-27 11:54 作者: 小鹿 時(shí)間: 2025-3-27 15:06 作者: 肉體 時(shí)間: 2025-3-27 21:42
https://doi.org/10.1057/978-1-137-59442-6ux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA作者: 懶洋洋 時(shí)間: 2025-3-28 01:25
Commercial Banking Risk Management/MS is the ability to accurately identify the peptide responsible for producing each observed spectrum. Recently, a dynamic Bayesian network (DBN) approach was shown to achieve state-of-the-art accuracy for this peptide identification problem. Modeling the stochastic process by which a peptide produ作者: 休戰(zhàn) 時(shí)間: 2025-3-28 05:45 作者: 取消 時(shí)間: 2025-3-28 08:21
https://doi.org/10.1057/978-1-137-59442-6d into two types: feature-based and similarity-based methods. By utilizing the “Learning to rank” framework, we propose a new method, DrugE-Rank, to combine these two different types of methods for improving the prediction performance of new candidate drugs and targets. DrugE-Rank is available at ..作者: 揭穿真相 時(shí)間: 2025-3-28 10:40
Jeffrey R. Gerlach,James B. Oldroydreatly facilitates the application of biomedical information retrieval and text mining. Large-scale automatic MeSH indexing has two challenging aspects: the MeSH side and citation side. For the MeSH side, each citation is annotated by only 12 (on average) out of all 28,000 MeSH terms. For the citati作者: Cumulus 時(shí)間: 2025-3-28 14:59
Commercial Banking in Transitionease-causing proteins. Protein-protein interaction (PPI) networks are effective tools in studying the functional roles of proteins in the development of various diseases. However, they are insufficient without the support of additional biological knowledge for proteins such as their molecular functi作者: 新手 時(shí)間: 2025-3-28 22:37
Commercial Banking in Transitionntains, among others, KEGG pathway maps and BRITE hierarchies (ontologies) representing high-level systemic functions of the cell and the organism. By the processes called pathway mapping and BRITE mapping, information encoded in the genome, especially the repertoire of genes, is converted to such h作者: 不能約 時(shí)間: 2025-3-29 02:46
Book 2018Latest editionsts and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency..作者: Pruritus 時(shí)間: 2025-3-29 04:05 作者: 彩色 時(shí)間: 2025-3-29 08:13
Commercial Applications of Ionic Liquidseospatial map of the world, (2) investigate and hunt for occurrences across generic user-generated surface-specific maps, with an example map of a human female, with data from Bouslimani et al., and (3) classify a user-given sequences dataset through our online platform for visual exploration of the作者: 萬神殿 時(shí)間: 2025-3-29 13:19 作者: 的闡明 時(shí)間: 2025-3-29 18:13
https://doi.org/10.1007/978-3-030-20111-1alculates protein GO functional enrichment, while the “Protein Set” tab calculates functional association between proteins. The NaviGO source code can be also downloaded and used locally or integrated into other software pipelines.作者: HAIL 時(shí)間: 2025-3-29 22:25 作者: 羽毛長成 時(shí)間: 2025-3-30 03:26 作者: 關(guān)心 時(shí)間: 2025-3-30 06:23
Commercial Banking Risk Managementemporal axis and, owing to the generative nature of the model, accurate feature extraction for substantially improved discriminative analysis (i.e., Percolator post-processing), all of which are supported in the DRIP Toolkit (DTK). Herein we describe how DTK may be used to significantly improve MS/M作者: Herpetologist 時(shí)間: 2025-3-30 08:23
Jeffrey R. Gerlach,James B. Oldroydto solve the challenge in the citation side. MeSHLabeler achieved the first place in both BioASQ2 and BioASQ3, and DeepMeSH achieved the first place in both BioASQ4 and BioASQ5 challenges. DeepMeSH is available at ..作者: facetious 時(shí)間: 2025-3-30 14:26
Commercial Banking in Transitionlogical characteristics, we propose to represent them with metagraphs. Compared to the traditional network motif or subgraph, a metagraph can capture the topological arrangements through not only the protein-protein interactions but also protein-keyword associations. We feed those novel metagraph re作者: prediabetes 時(shí)間: 2025-3-30 17:02 作者: 傳染 時(shí)間: 2025-3-30 22:34 作者: nerve-sparing 時(shí)間: 2025-3-31 04:32
Computing and Visualizing Gene Function Similarity and Coherence with NaviGO,alculates protein GO functional enrichment, while the “Protein Set” tab calculates functional association between proteins. The NaviGO source code can be also downloaded and used locally or integrated into other software pipelines.作者: 職業(yè)拳擊手 時(shí)間: 2025-3-31 07:34 作者: 漸變 時(shí)間: 2025-3-31 10:26
Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis,egularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also discuss a sparse variant of PMFA that 作者: irradicable 時(shí)間: 2025-3-31 16:48 作者: Crayon 時(shí)間: 2025-3-31 19:36
