標(biāo)題: Titlebook: Computational Intelligence in Bioinformatics; Arpad Kelemen,Ajith Abraham,Yuehui Chen Book 2008 Springer-Verlag Berlin Heidelberg 2008 Ann [打印本頁(yè)] 作者: Conformist 時(shí)間: 2025-3-21 19:57
書目名稱Computational Intelligence in Bioinformatics影響因子(影響力)
書目名稱Computational Intelligence in Bioinformatics影響因子(影響力)學(xué)科排名
書目名稱Computational Intelligence in Bioinformatics網(wǎng)絡(luò)公開度
書目名稱Computational Intelligence in Bioinformatics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Intelligence in Bioinformatics被引頻次
書目名稱Computational Intelligence in Bioinformatics被引頻次學(xué)科排名
書目名稱Computational Intelligence in Bioinformatics年度引用
書目名稱Computational Intelligence in Bioinformatics年度引用學(xué)科排名
書目名稱Computational Intelligence in Bioinformatics讀者反饋
書目名稱Computational Intelligence in Bioinformatics讀者反饋學(xué)科排名
作者: Preamble 時(shí)間: 2025-3-21 22:14 作者: 變量 時(shí)間: 2025-3-22 03:14 作者: PAD416 時(shí)間: 2025-3-22 07:30
978-3-642-09550-4Springer-Verlag Berlin Heidelberg 2008作者: 蘑菇 時(shí)間: 2025-3-22 10:52 作者: 附錄 時(shí)間: 2025-3-22 14:48 作者: 附錄 時(shí)間: 2025-3-22 17:39 作者: 顯而易見 時(shí)間: 2025-3-23 01:16 作者: EVEN 時(shí)間: 2025-3-23 03:16 作者: 洞察力 時(shí)間: 2025-3-23 07:34
Strategic Followership Frameworkms and their application to DNA microarray experimental data analysis. Additionally, dimension reduction techniques are evaluated. Our aim is to study and compare various Computational Intelligence approaches and demonstrate their applicability as well as their weaknesses and shortcomings to efficie作者: entreat 時(shí)間: 2025-3-23 13:46
https://doi.org/10.1057/9781137354426mines the production of proteins essential for cellular function. The level of expression of each gene in the genome is modified by controlling whether and how vigorously it is transcribed to RNA, and subsequently translated to protein. RNA and protein expression will influence expression rates of o作者: STRIA 時(shí)間: 2025-3-23 15:57 作者: STELL 時(shí)間: 2025-3-23 21:03 作者: DEFER 時(shí)間: 2025-3-24 01:11
https://doi.org/10.1057/9781137354426pment and drug effect at the molecular level. In this chapter for both exploring and prediction purposes a Time Lagged Recurrent Neural Network with trajectory learning is proposed for identifying and classifying the gene functional patterns from the heterogeneous nonlinear time series microarray ex作者: contradict 時(shí)間: 2025-3-24 05:00 作者: 任意 時(shí)間: 2025-3-24 07:38
Political and Economic Challenges,ods take a set of estimated covariance model parameters for a non-coding RNA family as given. The difference lies in how the score of a database position with respect to the covariance model is computed. Dynamic programming returns an exact score at the cost of very large computational resource usag作者: flammable 時(shí)間: 2025-3-24 11:57
Case Studies: From Region to Site,se studies is deemed increasingly important due to its potential of contributing to the understanding of fundamental questions in biology and clinical medicine. One important aspect of microarray expression analysis is the classification of the recorded samples which poses many challenges due to the作者: Nonconformist 時(shí)間: 2025-3-24 15:23 作者: 反對(duì) 時(shí)間: 2025-3-24 19:09 作者: 紅腫 時(shí)間: 2025-3-25 00:08 作者: RAG 時(shí)間: 2025-3-25 03:49
Stage 1: Strategies for HR Developmenttems Biology. Our working plan is designed to built an ontology-based system with connected biomodules that could be globally analysed, as far as possible. Supported by the advantages of the Semantic Web, we can keep the objective to work on the way to obtain an automated form to integrate both info作者: FIG 時(shí)間: 2025-3-25 11:33 作者: LIMN 時(shí)間: 2025-3-25 14:04
Inferring Gene Regulatory Networks from Expression Data,ly, we can hope that this will provide us with new therapeutic approaches and targets for drug design..It is thus no surprise that many efforts have been undertaken to reconstruct gene regulatory networks from gene expression measurements. In this chapter, we will provide an introductory overview ov作者: ineluctable 時(shí)間: 2025-3-25 18:58
Belief Networks for Bioinformatics,the chapter is to help the reader to understand and apply belief networks in the domain of bioinformatics. To achieve this, we (1) make the reader acquainted with the basic mathematical background of belief networks, (2) introduce algorithms to learn and to query belief networks, (3) describe the cu作者: flex336 時(shí)間: 2025-3-25 20:36 作者: 含糊 時(shí)間: 2025-3-26 02:22 作者: 某人 時(shí)間: 2025-3-26 07:49 作者: CANT 時(shí)間: 2025-3-26 11:07 作者: Gerontology 時(shí)間: 2025-3-26 15:39 作者: esculent 時(shí)間: 2025-3-26 17:11 作者: Excise 時(shí)間: 2025-3-27 00:51 作者: Mystic 時(shí)間: 2025-3-27 05:03 作者: Creatinine-Test 時(shí)間: 2025-3-27 07:05 作者: 結(jié)果 時(shí)間: 2025-3-27 10:39 作者: irradicable 時(shí)間: 2025-3-27 15:02 作者: Fibroid 時(shí)間: 2025-3-27 21:39
Strategic Health Technology Incorporation limits of parameters, the algorithm is implemented to infer gene regulatory networks for . and . The computation results not only prove the validity of the data-driven algorithm, but also offer a possible explanation concerning the difference of network stabilities between the budding yeast and the fission yeast.作者: HACK 時(shí)間: 2025-3-28 01:39 作者: Felicitous 時(shí)間: 2025-3-28 04:35 作者: MOTTO 時(shí)間: 2025-3-28 07:05
, and , Gene Regulatory Network Inference Using the Fuzzy Logic Network, limits of parameters, the algorithm is implemented to infer gene regulatory networks for . and . The computation results not only prove the validity of the data-driven algorithm, but also offer a possible explanation concerning the difference of network stabilities between the budding yeast and the fission yeast.作者: radiograph 時(shí)間: 2025-3-28 14:21 作者: 生命 時(shí)間: 2025-3-28 15:35 作者: endarterectomy 時(shí)間: 2025-3-28 19:44 作者: Airtight 時(shí)間: 2025-3-28 23:09 作者: 主講人 時(shí)間: 2025-3-29 05:42 作者: constitute 時(shí)間: 2025-3-29 09:32 作者: Hyperlipidemia 時(shí)間: 2025-3-29 14:44 作者: Monocle 時(shí)間: 2025-3-29 15:59
Political and Economic Challenges,e. Presently, databases are prefiltered using non-structural algorithms such as BLAST in order to make dynamic programming search feasible. The evolutionary computing approach allows for faster approximate search, but uses the RNA secondary structure information in the covariance model from the start.作者: 躲債 時(shí)間: 2025-3-29 21:29
Book 2008istics, informatics, and biochemistry to solve biological problems usually on the molecular level. Major research efforts in the field include sequence analysis, gene finding, genome annotation, protein structure alignment analysis and prediction, prediction of gene expression, protein-protein docki作者: 埋葬 時(shí)間: 2025-3-29 23:57
