標(biāo)題: Titlebook: Bioinformatics Using Computational Intelligence Paradigms; Udo Seiffert,Lakhmi C. Jain,Patric Schweizer Book 2005 Springer-Verlag Berlin H [打印本頁(yè)] 作者: 人工合成 時(shí)間: 2025-3-21 18:36
書目名稱Bioinformatics Using Computational Intelligence Paradigms影響因子(影響力)
書目名稱Bioinformatics Using Computational Intelligence Paradigms影響因子(影響力)學(xué)科排名
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書目名稱Bioinformatics Using Computational Intelligence Paradigms網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Bioinformatics Using Computational Intelligence Paradigms被引頻次
書目名稱Bioinformatics Using Computational Intelligence Paradigms被引頻次學(xué)科排名
書目名稱Bioinformatics Using Computational Intelligence Paradigms年度引用
書目名稱Bioinformatics Using Computational Intelligence Paradigms年度引用學(xué)科排名
書目名稱Bioinformatics Using Computational Intelligence Paradigms讀者反饋
書目名稱Bioinformatics Using Computational Intelligence Paradigms讀者反饋學(xué)科排名
作者: VEST 時(shí)間: 2025-3-21 21:04 作者: 欺騙世家 時(shí)間: 2025-3-22 03:52 作者: Prostaglandins 時(shí)間: 2025-3-22 06:36
Markus Oestreich,Oliver Rombergegions in eukaryotic DNA. The availability of large amounts of sequenced DNA makes the development of fast and reliable tools for automatic identi.cation of important functional regions of DNA necessary. We present a prototype based pattern recognition tool trained for automatic donor and acceptor r作者: calumniate 時(shí)間: 2025-3-22 12:39
Markus Oestreich,Oliver Rombergimages of one specific experiment the time factor is often rather secondary, and other features like lossless compression and a high compression ratio are much more important. Due to the similarity of all images within one experiment series, a content based compression seems to be especially applica作者: 吹氣 時(shí)間: 2025-3-22 14:54
Markus Oestreich,Oliver Rombergesponse program. Survival of especially single-cell organisms depends on their ability to adapt to the environmental changes and therefore stress response has received much attention. In the budding yeast . several hundred genes out of about 6500 present in the genome have previously been found invo作者: 不透明性 時(shí)間: 2025-3-22 17:26
Parametersch?tzung, Mr. Spock l?sst grü?enpate to regulatory networks that control the response of the cell to external input signals. One of the most important challenge to biologists is undoubtedly to understand the mechanisms that govern this regulation, and to identify among a set of genes which play a regulator role and which are regul作者: STING 時(shí)間: 2025-3-22 22:03
https://doi.org/10.1007/978-3-8348-9360-4ole in the use and interpretation of microarray data. For example, classifiers could be constructed indicating the detailed subtype of a disease, its expected progression and the best treatment strategy. In this chapter we outline the main stages involved in the development of a successful class pre作者: 無(wú)能力 時(shí)間: 2025-3-23 04:12 作者: 后來 時(shí)間: 2025-3-23 07:34
Markus Oestreich,Oliver Romberglone of a gene from one specific tissue. At the same time, some mRNA samples are labeled with two different kinds of dyes, for example,Cy5 (red) and Cy3 (blue). After that, the mRNA samples will be put on the chip and interact with the genes on the chip. This process is called hybridization. After h作者: Intersect 時(shí)間: 2025-3-23 12:59 作者: Allure 時(shí)間: 2025-3-23 16:43 作者: dilute 時(shí)間: 2025-3-23 19:12 作者: accrete 時(shí)間: 2025-3-23 22:13 作者: Interim 時(shí)間: 2025-3-24 03:48 作者: Progesterone 時(shí)間: 2025-3-24 09:18
1434-9922 ligence) against the background of bioinformatics.Combines t.Bioinformatics and computational intelligence are undoubtedly remarkably fast growing fields of research and real-world applications with enormous potential for current and future developments. Bioinformatics Using Computational Intelligen作者: Cacophonous 時(shí)間: 2025-3-24 11:27
Markus Oestreich,Oliver Romberg is one reason for the actual success of bioinformatics (Collado-Vides and Hofest?dt 2002). Today, besides the genome, protein and pathway data, a new domain of data is arising - the so-called proteomic project, which allows the identi.cation of speci.c protein pro.les in concentration and location.作者: 美色花錢 時(shí)間: 2025-3-24 18:54 作者: confederacy 時(shí)間: 2025-3-24 22:07 作者: 尾隨 時(shí)間: 2025-3-25 02:46
https://doi.org/10.1007/978-3-8348-9360-4a method for determining how much data is required for a classification task given an initial sample set. We illustrate this process with both public domain datasets and a new dataset for predicting relapse versus non-relapse for a paediatric tumour.作者: 反復(fù)無(wú)常 時(shí)間: 2025-3-25 06:02
Markus Oestreich,Oliver Rombergristics of the tissue at the molecular level. If we make microarrays for different tissues, biological and biomedical researchers are able to compare the difference of those tissues at the molecular level. Figure 1 is a description of the process of making microarrays.作者: 刺穿 時(shí)間: 2025-3-25 09:06 作者: charisma 時(shí)間: 2025-3-25 15:32 作者: 奇思怪想 時(shí)間: 2025-3-25 15:51
A Dynamic Model of Gene Regulatory Networks Based on Inertia Principle,xpression of thousands of genes of a given organism or a given tissue can now be measured simultaneously on the same chip. This revolution opens a large avenue for research on reconstruction of gene regulatory networks from experimental data.作者: Inflated 時(shí)間: 2025-3-25 20:59
Class Prediction with Microarray Datasets,a method for determining how much data is required for a classification task given an initial sample set. We illustrate this process with both public domain datasets and a new dataset for predicting relapse versus non-relapse for a paediatric tumour.作者: 樸素 時(shí)間: 2025-3-26 01:12 作者: 的事物 時(shí)間: 2025-3-26 04:48 作者: SPASM 時(shí)間: 2025-3-26 10:25 作者: arsenal 時(shí)間: 2025-3-26 15:27
Random Voronoi Ensembles for Gene Selection in DNA Microarray Data,nt response to treatments, can manifest themselves with undistinguishable appearances, not only at morphological inspection, but also from the immunophenotyping, biochemical, and cytogenetic profiles.作者: 高調(diào) 時(shí)間: 2025-3-26 19:03 作者: 平項(xiàng)山 時(shí)間: 2025-3-26 22:10 作者: 入伍儀式 時(shí)間: 2025-3-27 01:32
Markus Oestreich,Oliver Rombergnt response to treatments, can manifest themselves with undistinguishable appearances, not only at morphological inspection, but also from the immunophenotyping, biochemical, and cytogenetic profiles.作者: amyloid 時(shí)間: 2025-3-27 07:32 作者: Interdict 時(shí)間: 2025-3-27 09:29
Discriminative Clustering of Yeast Stress Response,n2p and Msn4p transcription factor pair. We have extended the study by in silico data mining using a new supervised discriminative clustering (DC) technique, which directly searches for responses potentially regulated by the Msn2/4p factors. We observed a cluster of CESR/CER genes, comparable to tho作者: 傲慢物 時(shí)間: 2025-3-27 15:16 作者: Inflamed 時(shí)間: 2025-3-27 21:37 作者: 殖民地 時(shí)間: 2025-3-28 01:17 作者: BRIDE 時(shí)間: 2025-3-28 04:01
Medical Bioinformatics: Detecting Molecular Diseases with Case-Based Reasoning,A catalytic element of this process is that the methods of molecular biology (DNA sequencing, proteomics etc.) allow the automated generation of data from cellular components. Based on this technology, robots are able to sequence small genomes in a few weeks. Moreover, the semi-automatic assembly an作者: delta-waves 時(shí)間: 2025-3-28 07:04
Prototype Based Recognition of Splice Sites,egions in eukaryotic DNA. The availability of large amounts of sequenced DNA makes the development of fast and reliable tools for automatic identi.cation of important functional regions of DNA necessary. We present a prototype based pattern recognition tool trained for automatic donor and acceptor r作者: 土產(chǎn) 時(shí)間: 2025-3-28 11:37
Content Based Image Compression in Biomedical High-Throughput Screening Using Artificial Neural Netimages of one specific experiment the time factor is often rather secondary, and other features like lossless compression and a high compression ratio are much more important. Due to the similarity of all images within one experiment series, a content based compression seems to be especially applica作者: 到婚嫁年齡 時(shí)間: 2025-3-28 17:34 作者: ingestion 時(shí)間: 2025-3-28 21:55 作者: reptile 時(shí)間: 2025-3-29 02:27
Class Prediction with Microarray Datasets,ole in the use and interpretation of microarray data. For example, classifiers could be constructed indicating the detailed subtype of a disease, its expected progression and the best treatment strategy. In this chapter we outline the main stages involved in the development of a successful class pre
