標(biāo)題: Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 6th European Confere Elena Marchiori,Jason H. Moore Conferenc [打印本頁(yè)] 作者: 大腦 時(shí)間: 2025-3-21 20:08
書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics影響因子(影響力)
書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics影響因子(影響力)學(xué)科排名
書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics網(wǎng)絡(luò)公開度
書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics被引頻次
書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics被引頻次學(xué)科排名
書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics年度引用
書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics年度引用學(xué)科排名
書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics讀者反饋
書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics讀者反饋學(xué)科排名
作者: entrance 時(shí)間: 2025-3-21 22:26
Jeanne Peijnenburg,David Atkinsonin both family-based and case-control datasets. In this manuscript, we demonstrate how LD patterns of the simulated data change under different population growth curve parameter initialization settings. These results provide guidelines to simulate WGA datasets whose properties resemble the HapMap.作者: DECRY 時(shí)間: 2025-3-22 04:10 作者: 大暴雨 時(shí)間: 2025-3-22 05:30 作者: 最高點(diǎn) 時(shí)間: 2025-3-22 11:51 作者: Herpetologist 時(shí)間: 2025-3-22 16:55 作者: Herpetologist 時(shí)間: 2025-3-22 18:31 作者: 藕床生厭倦 時(shí)間: 2025-3-22 22:49
Mining Gene Expression Patterns for the Discovery of Overlapping Clusters, both a local pairwise similarity measure between gene expression profiles and a global probabilistic measure of interestingness of hidden patterns. When performing re-clustering, the proposed approach is able to distinguish between relevant and irrelevant expression data. In addition, it is able to作者: molest 時(shí)間: 2025-3-23 02:50
Development and Evaluation of an Open-Ended Computational Evolution System for the Genetic Analysisal researchers. The goal of the present study was to develop and evaluate a prototype CE system for the analysis of human genetics data. We describe here this new open-ended CE system and provide initial results from a simulation study that suggests more complex operators result in better solutions.作者: Obvious 時(shí)間: 2025-3-23 08:12 作者: 深陷 時(shí)間: 2025-3-23 13:27
Nicos C. Sifakis,Stefania Kordiahers in classifier combination that the major factor for producing better accuracy is the diversity in the classifier team. Re-sampling based approaches like bagging, boosting and random subspace generate multiple models by training a single learning algorithm on multiple random replicates or sub-sa作者: 鋪?zhàn)?nbsp; 時(shí)間: 2025-3-23 15:05
https://doi.org/10.1007/978-3-319-10497-3is employing population-based optimization algorithms such as Particle Swarm Optimization (PSO)-based method and Ant Colony Optimization (ACO)-based method. This paper presents a novel feature selection method that is based on Ant Colony Optimization (ACO). This approach is easily implemented and be作者: Conducive 時(shí)間: 2025-3-23 20:13 作者: AROMA 時(shí)間: 2025-3-23 22:22 作者: Distribution 時(shí)間: 2025-3-24 03:54 作者: indifferent 時(shí)間: 2025-3-24 06:57 作者: CARE 時(shí)間: 2025-3-24 12:44 作者: 思考 時(shí)間: 2025-3-24 15:00
European Political Facts 1918–73otype-associated genes using appropriate methods. The limitations for the existing approaches are discussed. We propose a hierarchical mixture model in which the relationship between gene expressions and phenotypic values is described using orthogonal polynomials. Gene specific coefficient, which re作者: 遺忘 時(shí)間: 2025-3-24 19:50
Boris Johnson’s Approach to the Pandemics interpretation. Our technique is suitable for trees built on the same leafset as well as for trees where the leafset varies. The proposed solution has a very good interpretation, as it returns different, maximal sets of taxa that are connected with the same relations in the input trees. In contras作者: Insensate 時(shí)間: 2025-3-25 02:22 作者: 被詛咒的人 時(shí)間: 2025-3-25 06:44
https://doi.org/10.1007/978-1-349-20280-5ups of proteins in order to serve different biological roles, when responding to different external stimulants, the genes that produce these proteins are expected to co-express with more than one group of genes and therefore belong to more than one cluster. This poses a challenge to existing cluster作者: 收到 時(shí)間: 2025-3-25 08:00 作者: 勤勞 時(shí)間: 2025-3-25 13:52
Angela Carpenter,Rodrigo Lozanorly cancer diagnosis, prognosis and treatment. These measurements are represented by the expression levels of thousands of genes in normal and tumor sample tissues. In this paper we present a two-phase algorithm for gene expression data classification. In the first phase, a novel gene selection meth作者: 倔強(qiáng)一點(diǎn) 時(shí)間: 2025-3-25 17:08
The Rise of Democratisation Studies,Revealing these modular structures is significant in understanding how cells function. Protein interaction networks can be constructed by representing nodes as proteins and edges as interactions between proteins. In this paper, we use a graph based distance measure, .-clubs, to detect protein comple作者: 世俗 時(shí)間: 2025-3-25 23:26 作者: adumbrate 時(shí)間: 2025-3-26 00:33 作者: LEER 時(shí)間: 2025-3-26 06:04 作者: Tinea-Capitis 時(shí)間: 2025-3-26 10:40
The Responsibility Lies in Your Hands Now!,arly correct structures are included, leading to the problem of assessing the quality of alternative 3D conformations. This problem has been mostly approached by focusing on the final 3D conformation, with machine learning techniques playing a leading role. We argue in this paper that additional inf作者: hegemony 時(shí)間: 2025-3-26 13:25 作者: 歸功于 時(shí)間: 2025-3-26 16:49
978-3-540-78756-3Springer-Verlag Berlin Heidelberg 2008作者: deforestation 時(shí)間: 2025-3-26 21:32
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics978-3-540-78757-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 抗原 時(shí)間: 2025-3-27 01:07 作者: Pageant 時(shí)間: 2025-3-27 08:49
A Hybrid Random Subspace Classifier Fusion Approach for Protein Mass Spectra Classification,hers in classifier combination that the major factor for producing better accuracy is the diversity in the classifier team. Re-sampling based approaches like bagging, boosting and random subspace generate multiple models by training a single learning algorithm on multiple random replicates or sub-sa作者: Priapism 時(shí)間: 2025-3-27 12:36 作者: Circumscribe 時(shí)間: 2025-3-27 16:10 作者: 壓艙物 時(shí)間: 2025-3-27 21:41
DEEPER: A Full Parsing Based Approach to Protein Relation Extraction,ore general language structures such as parsing and dependency information for the construction of feature vectors that can be used by standard machine learning algorithms in deciding whether a sentence describes a protein interaction or not. As our approach is not dependent on the use of specific i作者: Functional 時(shí)間: 2025-3-28 01:28 作者: AORTA 時(shí)間: 2025-3-28 05:00
Protein Interaction Inference Using Particle Swarm Optimization Algorithm,rds the complete understanding of cellular functions. As the experimental techniques for this purpose are expensive and potentially erroneous, there are many computational methods being put forward for prediction of protein-protein interactions. These methods use different genomic features for indir作者: 結(jié)束 時(shí)間: 2025-3-28 09:25 作者: FAWN 時(shí)間: 2025-3-28 11:38
Detection of Quantitative Trait Associated Genes Using Cluster Analysis,otype-associated genes using appropriate methods. The limitations for the existing approaches are discussed. We propose a hierarchical mixture model in which the relationship between gene expressions and phenotypic values is described using orthogonal polynomials. Gene specific coefficient, which re作者: 瑪瑙 時(shí)間: 2025-3-28 16:42
Frequent Subsplit Representation of Leaf-Labelled Trees,s interpretation. Our technique is suitable for trees built on the same leafset as well as for trees where the leafset varies. The proposed solution has a very good interpretation, as it returns different, maximal sets of taxa that are connected with the same relations in the input trees. In contras作者: 共和國(guó) 時(shí)間: 2025-3-28 18:59 作者: 確定的事 時(shí)間: 2025-3-28 23:48
Mining Gene Expression Patterns for the Discovery of Overlapping Clusters,ups of proteins in order to serve different biological roles, when responding to different external stimulants, the genes that produce these proteins are expected to co-express with more than one group of genes and therefore belong to more than one cluster. This poses a challenge to existing cluster作者: 引導(dǎo) 時(shí)間: 2025-3-29 06:11 作者: 容易生皺紋 時(shí)間: 2025-3-29 09:07
Gene Selection and Cancer Microarray Data Classification Via Mixed-Integer Optimization,rly cancer diagnosis, prognosis and treatment. These measurements are represented by the expression levels of thousands of genes in normal and tumor sample tissues. In this paper we present a two-phase algorithm for gene expression data classification. In the first phase, a novel gene selection meth作者: MURAL 時(shí)間: 2025-3-29 14:14
Detection of Protein Complexes in Protein Interaction Networks Using n-Clubs,Revealing these modular structures is significant in understanding how cells function. Protein interaction networks can be constructed by representing nodes as proteins and edges as interactions between proteins. In this paper, we use a graph based distance measure, .-clubs, to detect protein comple作者: 改良 時(shí)間: 2025-3-29 19:08
Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control,made, i.e. the so-called false discovery rate (FDR). We present an algorithm aiming at controlling the FDR of edges when learning Gaussian graphical models (GGMs). The algorithm is particularly suitable when dealing with more nodes than samples, e.g. when learning GGMs of gene networks from gene exp作者: CANON 時(shí)間: 2025-3-29 21:56 作者: invert 時(shí)間: 2025-3-29 23:59 作者: Debate 時(shí)間: 2025-3-30 05:21
On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Foldinarly correct structures are included, leading to the problem of assessing the quality of alternative 3D conformations. This problem has been mostly approached by focusing on the final 3D conformation, with machine learning techniques playing a leading role. We argue in this paper that additional inf作者: circumvent 時(shí)間: 2025-3-30 08:47
Frequent Subsplit Representation of Leaf-Labelled Trees,as a very good interpretation, as it returns different, maximal sets of taxa that are connected with the same relations in the input trees. In contrast to other methods known in literature it does not necessarily result in one tree, but may result in a profile of trees, which are usually more resolved than the consensus trees.作者: Cabg318 時(shí)間: 2025-3-30 15:57 作者: cornucopia 時(shí)間: 2025-3-30 17:55 作者: 供過于求 時(shí)間: 2025-3-30 21:17 作者: 茁壯成長(zhǎng) 時(shí)間: 2025-3-31 01:39 作者: Cognizance 時(shí)間: 2025-3-31 06:14 作者: 殺蟲劑 時(shí)間: 2025-3-31 11:28 作者: 誰在削木頭 時(shí)間: 2025-3-31 14:36
Mai’a K. Davis Cross,Jan Melissenet to a minimum. The performance of the proposal was assessed for predicting hydrophobicity, using an ensemble of neural networks for the prediction task. The results showed that the evolutionary approach using a non linear fitness function constitutes a novel and a promising technique for this bioinformatic application.作者: 傾聽 時(shí)間: 2025-3-31 19:30
The Responsibility Lies in Your Hands Now!,ntroduce a kernel function for assessing their similarity. Kernel-based analysis techniques empirically demonstrate a significant correlation between information contained into pseudo-folding trees and features of native folds in a large and non-redundant set of proteins.作者: 拍翅 時(shí)間: 2025-4-1 00:26
A Hybrid Random Subspace Classifier Fusion Approach for Protein Mass Spectra Classification,tra datasets of ovarian cancer demonstrate the usefulness of this approach for six learning algorithms (LDA, 1-NN, Decision Tree, Logistic Regression, Linear SVMs and MLP). The results also show that the proposed strategy outperforms three conventional re-sampling based ensemble algorithms on these datasets.作者: Frisky 時(shí)間: 2025-4-1 02:31 作者: 航海太平洋 時(shí)間: 2025-4-1 08:44 作者: predict 時(shí)間: 2025-4-1 11:37
A Wrapper-Based Feature Selection Method for ADMET Prediction Using Evolutionary Computing,et to a minimum. The performance of the proposal was assessed for predicting hydrophobicity, using an ensemble of neural networks for the prediction task. The results showed that the evolutionary approach using a non linear fitness function constitutes a novel and a promising technique for this bioinformatic application.作者: 大漩渦 時(shí)間: 2025-4-1 16:54
On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Foldinntroduce a kernel function for assessing their similarity. Kernel-based analysis techniques empirically demonstrate a significant correlation between information contained into pseudo-folding trees and features of native folds in a large and non-redundant set of proteins.作者: chalice 時(shí)間: 2025-4-1 20:08 作者: 高貴領(lǐng)導(dǎo) 時(shí)間: 2025-4-2 01:47
