The dissimilarity space: Bridging structural and statistical pattern recognition

Duin, Robert P. W.; Pękalska, Elżbieta

doi:10.1016/j.patrec.2011.04.019

Cited by 75 publications

(62 citation statements)

References 14 publications

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“…In general, cluster membership defined by different methods increasingly diverged when grouping proteins that were more sparsely represented in the data. Clusters were resolved most effectively when the distance matrix was treated as a “feature vector” in a so-called dissimilarity representation (compare Method: dissimilarity vs. distance, Table 1) [27]. Clustering methods applied to the raw data, or to data where zeros represented the absence of data, were not successful (not shown); they converged on only one large cluster, leaving a number of individual proteins.…”

Section: Resultsmentioning

confidence: 99%

Wrangling Phosphoproteomic Data to Elucidate Cancer Signaling Pathways

et al. 2013

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The interpretation of biological data sets is essential for generating hypotheses that guide research, yet modern methods of global analysis challenge our ability to discern meaningful patterns and then convey results in a way that can be easily appreciated. Proteomic data is especially challenging because mass spectrometry detectors often miss peptides in complex samples, resulting in sparsely populated data sets. Using the R programming language and techniques from the field of pattern recognition, we have devised methods to resolve and evaluate clusters of proteins related by their pattern of expression in different samples in proteomic data sets. We examined tyrosine phosphoproteomic data from lung cancer samples. We calculated dissimilarities between the proteins based on Pearson or Spearman correlations and on Euclidean distances, whilst dealing with large amounts of missing data. The dissimilarities were then used as feature vectors in clustering and visualization algorithms. The quality of the clusterings and visualizations were evaluated internally based on the primary data and externally based on gene ontology and protein interaction networks. The results show that t-distributed stochastic neighbor embedding (t-SNE) followed by minimum spanning tree methods groups sparse proteomic data into meaningful clusters more effectively than other methods such as k-means and classical multidimensional scaling. Furthermore, our results show that using a combination of Spearman correlation and Euclidean distance as a dissimilarity representation increases the resolution of clusters. Our analyses show that many clusters contain one or more tyrosine kinases and include known effectors as well as proteins with no known interactions. Visualizing these clusters as networks elucidated previously unknown tyrosine kinase signal transduction pathways that drive cancer. Our approach can be applied to other data types, and can be easily adopted because open source software packages are employed.

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Section: Resultsmentioning

confidence: 99%

Wrangling Phosphoproteomic Data to Elucidate Cancer Signaling Pathways

et al. 2013

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“…In this work we are going to restrict ourselves to a particular family of embedding algorithms known as Dissimilarity Space (DS) representation [13]. Broadly, DS representations are obtained by computing pairwise dissimilarities between the elements of a representation set, which actually constitutes a subset of the initial structural training data selected following a given criterion.…”

Section: Prototype Generation Over Structural Data Using Dissimilaritmentioning

confidence: 99%

Prototype generation on structural data using dissimilarity space representation

Calvo-Zaragoza

Valero-Mas

Rico-Juan

2016

Neural Comput & Applic

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Data Reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Among the different existing approaches, Prototype Selection (PS) and Prototype Generation (PG) are the most representative ones. These two families differ in the way the reduced set is obtained from the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to delimit more efficiently decision boundaries, the operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs. This work studies the possibility of using Dissimilarity Space (DS) methods as an intermediate process for mapping the initial structural representation to a statistical one, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that the proposed strategy is able to achieve significantly similar results to PS in the initial space, thus standing as a clear alternative to the classic approach, with some additional advantages derived from the DS representation.

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“…The obtained mapping to R p is equipped with the traditional inner product and Euclidean metric, and we have the so-called dissimilarity space (DS). In this way, the dissimilarity matrix (T, Π) is used as input for the classification or clustering algorithms (Pekalska and Duin 2005;Duin and Pkalska 2012).…”

Section: Dissimilarity Representationmentioning

confidence: 99%

Tractome: a visual data mining tool for brain connectivity analysis

Porro-Muñoz

Olivetti

Sharmin

et al. 2015

Data Min Knowl Disc

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Diffusion magnetic resonance imaging data allows reconstructing the neural pathways of the white matter of the brain as a set of 3D polylines. This kind of data sets provides a means of study of the anatomical structures within the white matter, in order to detect neurologic diseases and understand the anatomical connectivity of the brain. To the best of our knowledge, there is still not an effective or satisfactory method for automatic processing of these data. Therefore, a manually guided visual exploration of experts is crucial for the purpose. However, because of the large size of these data sets, visual exploration and analysis has also become intractable. In order to make use of the advantages of both manual and automatic analysis, we have developed a new visual data mining tool for the analysis of human brain anatomical connectivity. With such tool, humans and automatic algorithms capabilities are integrated in an interactive data exploration and analysis process. A very important aspect to take into account when designing this tool, was to provide the user with comfortable interaction. For this purpose, we tackle the scalability issue in the different stages of the system, including the automatic algorithm and the visualization and interaction techniques that are used.

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The dissimilarity space: Bridging structural and statistical pattern recognition

Cited by 75 publications

References 14 publications

Wrangling Phosphoproteomic Data to Elucidate Cancer Signaling Pathways

Wrangling Phosphoproteomic Data to Elucidate Cancer Signaling Pathways

Prototype generation on structural data using dissimilarity space representation

Tractome: a visual data mining tool for brain connectivity analysis

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