Gene Clustering via Integrated Markov Models Combining Individual and Pairwise Features
نویسندگان
چکیده
منابع مشابه
Combining Optimal Clustering And Hidden Markov Models For Extractive Summarization
We propose Hidden Markov models with unsupervised training for extractive summarization. Extractive summarization selects salient sentences from documents to be included in a summary. Unsupervised clustering combined with heuristics is a popular approach because no annotated data is required. However, conventional clustering methods such as K-means do not take text cohesion into consideration. ...
متن کاملPairwise Clustering and Graphical Models
Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datapoints. In particular, spectral clustering methods have the advantage of being able to divide arbitrarily shaped clusters and are based on efficient eigenvector calculations. However, spectral methods lack a straightforward probabilistic interpretation which makes it difficul...
متن کاملIntegrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations
Most datasets in real applications come in from multiple sources. As a result, we often have attributes information about data objects and various pairwise relations (similarity) between data objects. Traditional clustering algorithms use either data attributes only or pairwise similarity only. We propose to combine K-means clustering on data attributes and normalized cut spectral clustering on...
متن کاملHMMGEP: clustering gene expression data using hidden Markov models
SUMMARY The package HMMGEP performs cluster analysis on gene expression data using hidden Markov models. AVAILABILITY HMMGEP, including the source code, documentation and sample data files, is available at http://www.bioinfo.tsinghua.edu.cn:8080/~rich/hmmgep_download/index.html.
متن کاملArtifacts from Combining Hidden Markov Models
Hidden Markov models (HMMs) have found wide spread use in bioinformatics. In short, an HMM consists of a set of states; each state has a probability distribution over what state to move to next when being in this state (often an HMM is drawn as a directed graph with nodes representing states and edges showing transitions from one state to another with non-zero probability, cf. e.g. Figure 1) an...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE/ACM Transactions on Computational Biology and Bioinformatics
سال: 2009
ISSN: 1545-5963
DOI: 10.1109/tcbb.2007.70248