標(biāo)題: Titlebook: Computational Intelligence in Biomedicine and Bioinformatics; Current Trends and A Tomasz G. Smolinski,Mariofanna G. Milanova,Aboul-E Book [打印本頁] 作者: 嚴(yán)峻 時間: 2025-3-21 18:08
書目名稱Computational Intelligence in Biomedicine and Bioinformatics影響因子(影響力)
書目名稱Computational Intelligence in Biomedicine and Bioinformatics影響因子(影響力)學(xué)科排名
書目名稱Computational Intelligence in Biomedicine and Bioinformatics網(wǎng)絡(luò)公開度
書目名稱Computational Intelligence in Biomedicine and Bioinformatics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Intelligence in Biomedicine and Bioinformatics被引頻次
書目名稱Computational Intelligence in Biomedicine and Bioinformatics被引頻次學(xué)科排名
書目名稱Computational Intelligence in Biomedicine and Bioinformatics年度引用
書目名稱Computational Intelligence in Biomedicine and Bioinformatics年度引用學(xué)科排名
書目名稱Computational Intelligence in Biomedicine and Bioinformatics讀者反饋
書目名稱Computational Intelligence in Biomedicine and Bioinformatics讀者反饋學(xué)科排名
作者: fixed-joint 時間: 2025-3-22 00:03
IT Strategy — Using IT for Value Creationand the ovarian cancer data set. In all cases we obtained 100% accuracy with fewer genes in comparison with previously published results. Our result shows the FNN classifier not only improves the accuracy of cancer classification problem but also helps biologists to find a better relationship between important genes and development of cancers.作者: Sigmoidoscopy 時間: 2025-3-22 04:06
https://doi.org/10.1007/978-3-8349-9325-0 are the use of simple and effective techniques rather than complex non-linear models, the use of interpretable methods and the use of scalable computational solutions able to deal with multiplatform and multisource data.作者: Keratin 時間: 2025-3-22 06:40 作者: Induction 時間: 2025-3-22 12:08 作者: 蔓藤圖飾 時間: 2025-3-22 16:19
Assisting Cancer Diagnosis with Fuzzy Neural Networksand the ovarian cancer data set. In all cases we obtained 100% accuracy with fewer genes in comparison with previously published results. Our result shows the FNN classifier not only improves the accuracy of cancer classification problem but also helps biologists to find a better relationship between important genes and development of cancers.作者: 蔓藤圖飾 時間: 2025-3-22 19:15 作者: chemoprevention 時間: 2025-3-22 23:28
Power-Law Signatures and Patchiness in Genechip Oligonucleotide Microarraystablished on publicly available microarrays generated across laboratories investigating the same paradigm. Persistence of these similarities across raw as well as background subtracted probe intensities is also investigated. The results presented raise fundamental concerns in interpreting Genechip oligonucleotide microarray data.作者: Palpable 時間: 2025-3-23 01:24
1860-949X verview of powerful state-of-the-art methodologies that are currently utilized for biomedicine and/ or bioinformatics-oriented applications, so that researchers working in those fields could learn of new methods to help them tackle their problems. On the other hand, the CI community will find this b作者: craven 時間: 2025-3-23 08:40
Book 2008informatics-oriented applications, so that researchers working in those fields could learn of new methods to help them tackle their problems. On the other hand, the CI community will find this book useful by discovering a new and intriguing area of applications. In order to help fill the gap between作者: 馬具 時間: 2025-3-23 12:14 作者: granite 時間: 2025-3-23 16:30
IT Strategy — Using IT for Value Creationl foundations and hands-on experience that allow researchers to figure out novel applications of AIS in bioinformatics and, at the same time, providing researchers with necessary insights for implementation in daily research. The contribution will be organised in 5 sections.作者: MAG 時間: 2025-3-23 19:38
Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challengespplied to solve bioinformatic problems and how bioinformatics could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are presented. An extensive bibliography is also included.作者: 逃避責(zé)任 時間: 2025-3-24 00:58
Artificial Immune Systems in Bioinformaticsl foundations and hands-on experience that allow researchers to figure out novel applications of AIS in bioinformatics and, at the same time, providing researchers with necessary insights for implementation in daily research. The contribution will be organised in 5 sections.作者: intelligible 時間: 2025-3-24 02:56
https://doi.org/10.1007/978-3-8349-8823-2tic algorithm, a process that mimics evolution. This chapter highlights data mining and the genetic algorithm technique, and it also lists many applications where data mining tools have been beneficial to biological and biomedical researchers, and lists some of the available data mining tools.作者: 心神不寧 時間: 2025-3-24 10:15
https://doi.org/10.1007/978-3-8349-9325-0erns. This algorithm feeds into our enhanced Neural Query System [2, 12] database application to facilitate data-mining. We have used our algorithm to identify and classify activity from both simulated and experimental seizures.作者: Gobble 時間: 2025-3-24 10:56 作者: jealousy 時間: 2025-3-24 17:12 作者: cumber 時間: 2025-3-24 22:03 作者: 注射器 時間: 2025-3-24 23:31
Computational Intelligence Techniques in Image Segmentation for Cytopathology. In this chapter we present and discuss some modified versions of watershed algorithm, active contours, cellular automata, GrowCut technique, as well as new approaches like fuzzy sets of I and II type, and the sonar-like method.作者: Occlusion 時間: 2025-3-25 06:15
Book 2008 benefit to practitioners in the fields of biomedicine and bioinformatics dealing with problems of data exploration and mining, search-space exploration, optimization, etc. Part II of this book, Computational Intelligence in Biomedicine, contains a collection of contributions on current state-of-the作者: LEER 時間: 2025-3-25 09:47
Integrating Local and Personalised Modelling with Global Ontology Knowledge Bases for Biomedical and nervous system cancer are revealed from existing data and local profiles of patients are derived. Through ontology analysis, these genes are found to be related to different functions, areas, and other diseases of the brain. Two other case studies discussed in the paper are chronic disease ontology作者: FOLLY 時間: 2025-3-25 13:45
Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizersent automatic metric adaptation (feature selection), fuzzy classification, and similarity based visualization of data. These properties offer new possibilities for analysis of mass spectrometric data. In this contribution we concentrate on recent extensions of SOMs as universal tools in the light of作者: Ledger 時間: 2025-3-25 19:03
Curvature Flow Based 3D Surface Evolution Model for Polyp Detection and Visualization in CT Colonogrsures the amount of protrudeness. We also designed a new color coding scheme for better visualization of the detected polyps. The proposed method has been evaluated by using synthetic phantoms and real colon datasets.作者: HUMP 時間: 2025-3-25 22:32 作者: Missile 時間: 2025-3-26 03:15
Flexible Protein Folding by Ant Colony Optimization achieve good results without local search methods. By testing some benchmark two-dimensional hydrophobic-polar (2D-HP) protein sequences, the performance shows that the proposed algorithm is quite competitive compared with some other well-known methods for solving the same protein folding problems.作者: Oratory 時間: 2025-3-26 06:02
Considering Stem-Loops as Sequence Signals for Finding Ribosomal RNA Genes in an effort to identify rRNA genes in genomes outside of the training set..Results: The values for the stem-loop metrics we tested are sensitive to G+C content. Two of the metrics reported here are able to identify rRNA genes when there is a marked difference in G+C content between rRNAs and their作者: 全國性 時間: 2025-3-26 10:41 作者: Saline 時間: 2025-3-26 12:57 作者: decipher 時間: 2025-3-26 20:50 作者: 淡紫色花 時間: 2025-3-26 23:54 作者: Myelin 時間: 2025-3-27 01:34 作者: 無禮回復(fù) 時間: 2025-3-27 05:55
IT Strategy — Using IT for Value Creation achieve good results without local search methods. By testing some benchmark two-dimensional hydrophobic-polar (2D-HP) protein sequences, the performance shows that the proposed algorithm is quite competitive compared with some other well-known methods for solving the same protein folding problems.作者: Paraplegia 時間: 2025-3-27 11:11
IT Strategy — Using IT for Value Creation in an effort to identify rRNA genes in genomes outside of the training set..Results: The values for the stem-loop metrics we tested are sensitive to G+C content. Two of the metrics reported here are able to identify rRNA genes when there is a marked difference in G+C content between rRNAs and their作者: 下船 時間: 2025-3-27 15:13 作者: Gastric 時間: 2025-3-27 19:40 作者: Meditate 時間: 2025-3-27 23:15 作者: aesthetician 時間: 2025-3-28 02:51 作者: 的事物 時間: 2025-3-28 07:16 作者: 劇毒 時間: 2025-3-28 13:55
The Use of Rough Sets as a Data Mining Tool for Experimental Bio-datahe computing community. However, due to the mathematical nature of the Rough Sets methodology, many experimental scientists lacking sufficient mathematical background have been hesitant to use it. The goal of this chapter is twofold: (1) to introduce “Rough Sets” methodology (along with one of its d作者: Myosin 時間: 2025-3-28 18:12 作者: 吊胃口 時間: 2025-3-28 19:11
Data-Mining of Time-Domain Features from Neural Extracellular Field Data would be desirable for automated seizure detection in both experimental and clinical venues. We have developed a time-domain algorithm denominated SPUD to facilitate data-mining of large electroencephalogram/electrocorticogram datasets to identify the occurrence of spike-wave or other activity patt作者: Overthrow 時間: 2025-3-29 01:19
Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers keys for efficient processing of the complex data. One major class are prototype based algorithms. Prototype based vector quantizers or classifiers are intuitive approaches realizing the principle of characteristic representatives for data subsets or decision regions between them. Examples for such作者: 構(gòu)想 時間: 2025-3-29 04:11 作者: Inflammation 時間: 2025-3-29 10:33 作者: 預(yù)示 時間: 2025-3-29 14:59
Assisting Cancer Diagnosis with Fuzzy Neural Networksrk (FNN) proposed earlier for cancer classification. This FNN contains three valuable aspects i.e., automatically generating fuzzy membership functions, parameter optimization, and rule-base simplification. One major obstacle in microarray data set classifier is that the number of features (genes) i作者: QUAIL 時間: 2025-3-29 16:03
Computational Intelligence in Clinical Oncology: Lessons Learned from an Analysis of a Clinical Studtion from gene expression data enabled an improved oncological clinical analysis. This study focuses on a survival analysis of estrogen receptor (ER) positive breast cancer patients treated with tamoxifen. The chapter describes each step of the gene expression data analysis procedure, from the quali作者: EVADE 時間: 2025-3-29 23:23
Artificial Immune Systems in Bioinformatics field of research is still in its infancy, several relevant results have been achieved by using the AIS paradigm in demanding tasks such as the ones coming from computational biology and biochemistry. The chapter will show how AIS have been successfully used in computational biology problems and wi作者: FLAIL 時間: 2025-3-30 00:13
Evolutionary Algorithms for the Protein Folding Problem: A Review and Current Trendsction of a protein is determined by the way it is folded into a specific tri-dimensional structure, known as native conformation. Understanding how proteins fold is of great importance to Biology, Biochemistry and Medicine. Considering the full analytic atomic model of a protein, it is still not pos作者: phytochemicals 時間: 2025-3-30 06:29
Flexible Protein Folding by Ant Colony Optimizationunctions, predicting the folding structure of a protein to judge its functions is meaningful to the humanity. This chapter proposes a flexible ant colony (FAC) algorithm for solving protein folding problems (PFPs) based on the hydrophobic-polar (HP) square lattice model. Different from the previous 作者: 召集 時間: 2025-3-30 09:08
Considering Stem-Loops as Sequence Signals for Finding Ribosomal RNA Genesoops on their way to forming stable structures. Also, stem-loops can be identified along a sequence of length . in .(.) time. We postulate that stem-loops found in structural RNA genes may tend to be longer than those found in their genomic counterparts - coding sequences and noncoding DNA. We also 作者: Acetaldehyde 時間: 2025-3-30 15:19
Power-Law Signatures and Patchiness in Genechip Oligonucleotide Microarrayson estimation precedes biological inference and is given as a complex combination of atomic entities on the array called probes. These probe intensities are further classified into perfect-match (PM) and mismatch (MM) probes. While former is a measure of specific binding, the latter is a measure of 作者: 感情脆弱 時間: 2025-3-30 20:18
Case Study: Structure and Function Prediction of a Protein with No Functionally Characterized Homolooteins. Prediction tools are used to guide the experimental design to test various hypotheses about structure and function of known proteins. However, these tools are particularly useful when studying putative protein sequences with no known function. The genomic era produced a large number of seque作者: CREEK 時間: 2025-3-31 00:20
https://doi.org/10.1007/978-3-540-70778-3algorithms; bioinformatics; biomedicine; computational intelligence; data mining; evolution; evolutionary 作者: 小木槌 時間: 2025-3-31 03:47
978-3-642-08969-5Springer-Verlag Berlin Heidelberg 2008作者: Spinous-Process 時間: 2025-3-31 08:52 作者: forager 時間: 2025-3-31 10:11 作者: Diluge 時間: 2025-3-31 13:54
https://doi.org/10.1007/978-3-8349-8823-2he computing community. However, due to the mathematical nature of the Rough Sets methodology, many experimental scientists lacking sufficient mathematical background have been hesitant to use it. The goal of this chapter is twofold: (1) to introduce “Rough Sets” methodology (along with one of its d作者: Fsh238 時間: 2025-3-31 17:41
