Active Clustering of Biological Sequences
نویسندگان
چکیده
Given a point set S and an unknown metric d on S, we study the problem of efficiently partitioning S into k clusters while querying few distances between the points. In our model we assume that we have access to one versus all queries that given a point s ∈ S return the distances between s and all other points. We show that given a natural assumption about the structure of the instance, we can efficiently find an accurate clustering using only O(k) distance queries. Our algorithm uses an active selection strategy to choose a small set of points that we call landmarks, and considers only the distances between landmarks and other points to produce a clustering. We use our procedure to cluster proteins by sequence similarity. This setting nicely fits our model because we can use a fast sequence database search program to query a sequence against an entire data set. We conduct an empirical study that shows that even though we query a small fraction of the distances between the points, we produce clusterings that are close to a desired clustering given by manual classification.
منابع مشابه
Clustering of Short Read Sequences for de novo Transcriptome Assembly
Given the importance of transcriptome analysis in various biological studies and considering thevast amount of whole transcriptome sequencing data, it seems necessary to develop analgorithm to assemble transcriptome data. In this study we propose an algorithm fortranscriptome assembly in the absence of a reference genome. First, the contiguous sequencesare generated using de Bruijn graph with d...
متن کاملA computational method to analyze the similarity of biological sequences under uncertainty
In this paper, we propose a new method to analyze the difference and similarity of biological sequences, based on the fuzzy sets theory. Considering the sequence order and some chemical and structural properties, we present a computational method to cluster the biological sequences. By some examples, we show that the new method is relatively easy and we are able to compare the sequences of arbi...
متن کاملClustering of a Number of Genes Affecting in Milk Production using Information Theory and Mutual Information
Information theory is a branch of mathematics. Information theory is used in genetic and bioinformatics analyses and can be used for many analyses related to the biological structures and sequences. Bio-computational grouping of genes facilitates genetic analysis, sequencing and structural-based analyses. In this study, after retrieving gene and exon DNA sequences affecting milk yield in dairy ...
متن کاملFinding Exact and Solo LTR-Retrotransposons in Biological Sequences Using SVM
Finding repetitive subsequences in genome is a challengeable problem in bioinformatics research area. A lot of approaches have been proposed to solve the problem, which could be divided to library base and de novo methods. The library base methods use predetermined repetitive genome’s subsequences, where library-less methods attempt to discover repetitive subsequences by analytical approach...
متن کاملSignal processing approaches as novel tools for the clustering of N-acetyl-β-D-glucosaminidases
Nowadays, the clustering of proteins and enzymes in particular, are one of the most popular topics in bioinformatics. Increasing number of chitinase genes from different organisms and their sequences have beenidentified. So far, various mathematical algorithms for the clustering of chitinase genes have been used butmost of them seem to be confusing and sometimes insufficient. In the...
متن کاملHigh-Dimensional Unsupervised Active Learning Method
In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Journal of Machine Learning Research
دوره 13 شماره
صفحات -
تاریخ انتشار 2012