[PDF] Top 20 Semi-supervised consensus clustering for gene expression data analysis
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Semi-supervised consensus clustering for gene expression data analysis
... Keywords: Semi-supervised clustering, Consensus clustering, Semi-supervised consensus clustering, Gene expression Background Simple ... Voir le document complet
14
Spectral analysis of gene expression profiles using gene networks
... of gene interactions (and not just crude characteristics such as the inter/intra- module connectivity) into the core microarray data ...method for correlating interaction graphs and different types ... Voir le document complet
14
The Variable Neighborhood Search Metaheuristic for Fuzzy Clustering cDNA Microarray Gene Expression Data
... each gene to each cluster, thereby including the possibility for gene ...fuzzy clustering algorithms to analysis of cDNA microarray data is that these algorithms inherently ... Voir le document complet
8
Data mining techniques for large-scale gene expression analysis
... The ranked data is often useful when mining the database for phenotypic trends across large sets of samples [47] (e.g., clustering sam- ples related to particular tumo[r] ... Voir le document complet
256
De Novo Clustering of Long Reads by Gene from Transcriptomics Data
... to data with reference Long reads make it possible to skip the transcript reconstruction step that is necessary with short reads, although this is particularly difficult when it involves ...downstream ... Voir le document complet
13
Deep triplet-driven semi-supervised embedding clustering
... exploit for guiding the analysis process. In this context, semi-supervised clustering can be employed to lever- age such knowledge and enable the discovery of clusters that meet the ... Voir le document complet
16
Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data
... Keywords: Clustering; compositional data; data transformations; K-means Abstract: Although there is no shortage of clustering algorithms proposed in the literature, the question of the most ... Voir le document complet
36
Impact of the distance choice on clustering gene expression data using graph decompositions
... this analysis, we de ne one interval per distance function, which ful lls these ...criteria. For each distance function, we choose a threshold whithin this interval near the local minimum of the number of ... Voir le document complet
21
Bayesian Mixture Models For Semi-Supervised Clustering
... Introduction Semi-supervised learning gained considerable interest from re- searchers recently due to the availability of large amounts of unla- beled data and a small fraction of labeled data ... Voir le document complet
8
Evaluation and Optimization of Clustering in Gene Expression Data Analysis
... per gene (NDPG), which uses scientific literature to assess whether a group of genes are functionally ...in gene expression levels to assess the reliability of gene clusters identified from ... Voir le document complet
12
Utilization of Gene Ontology in Semi-supervised Clustering
... on semi-supervised clustering has been very active over the past ...comprehensive semi-supervised clustering algorithm, MPCKMeans, that integrates both constraint learning and ... Voir le document complet
8
Efficient supervised and semi-supervised approaches for affiliations disambiguation
... citation analysis, semantic ...a semi-supervised approach, mixing soft-clustering and Bayesian ...unbalanced data. Alternatives solutions are possible for future developments, ... Voir le document complet
11
Gene expression analysis of flax seed development
... vegetative tissues, DGAT expression in vegetative tissues is too low to detect with the EST counts. Desaturation is the key step that results in the desirable omega-3 and omega-6 fatty acids [44]. This seems to ... Voir le document complet
50
Semi-Supervised Self-Training for Sentence Subjectivity Classification
... Fig. 3. The experimental results of self-training with different numbers of unlabeled instances for next iteration. ent even within the same leaf node, which makes ranking on unlabeled instances perform better. ... Voir le document complet
14
Multiview semi-supervised learning for ranking multilingual documents
... the data is imbalanced (a vast majority of the examples belong to the same class) or ...algorithms for AUC optimization have been designed [13, ...of supervised learning algorithms from bipartite ... Voir le document complet
17
Enhancing graph-based semi-supervised learning via knowledge-aware data embedding
... our data is equal to p, we sample uniformly at random the size of the first/third hidden layer in the range [p/2, p) while we sample uniformly at random the size of the bottleneck layer in the range [p/4, ... Voir le document complet
9
Game Theory applied to gene expression analysis
... large for large ...(see for a summary Amaratunga and Cabrera (2004)), mainly assuming independence of the test ...each gene (interacting with many others) in determining the association between the ... Voir le document complet
104
Hemodynamic estimation based on Consensus Clustering
... the data performed before JDE ...using consensus clustering techniques based on random parcellations of the data, we combine hemodynamics results provided by different parcellations, so as to ... Voir le document complet
5
Integrating clinical, gene expression, protein expression and preanalytical data for in silico cancer research.
... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r] ... Voir le document complet
7
Semi-supervised model adaptation for statistical machine translation
... unlabeled data contains some information which can be explored in semi-supervised ...training data when doing full re-training, or by using a mixture ...large data track Chinese–English ... Voir le document complet
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