Coupled two-way clustering analysis of breast cancer and colon cancer
gene expression data

Getz, Gad; Gal, Hilah; Kela, Itai; Notterman, Daniel A.; Domany, Eytan

doi:10.1093/bioinformatics/btf876

Cited by 66 publications

(37 citation statements)

References 15 publications

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“…In our approach of representing the pathway activity with a discrete probability distribution Pr(PA i , s j ), the representation of sample pathway activity PAV(s j , PA) of a sample s j for a set of A pathways is a vector of probability distributions as shown in Eq. (2). As each element of a pathway activity vector PAV(s j , PA) is a probability distribution rather than a scalar value, a new method is necessary to compute the distance between two vectors of probability distributions PAV(s l , PA) and PAV(s m , PA) from two samples s l and s m .…”

Section: Discrepancy Measure Between Two Sample Pathway Activity Vectorsmentioning

confidence: 99%

In-silico Interaction-Resolution Pathway Activity Quantification and Application to Identifying Cancer Subtypes

Jung

2015

Proceedings of the ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics

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Background: Identifying subtypes of complex diseases such as cancer is the very first step toward developing highly customized therapeutics on such diseases, as their origins significantly vary even with similar physiological characteristics. There have been many studies to recognize subtypes of various cancer based on genomic signatures, and most of them rely on approaches based on the signatures or features developed from individual genes. However, the idea of network-driven activities of biological functions has gained a lot of interests, as more evidence is found that biological systems can show highly diverse activity patterns because genes can interact differentially across specific molecular contexts.

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Section: Discrepancy Measure Between Two Sample Pathway Activity Vectorsmentioning

confidence: 99%

In-silico Interaction-Resolution Pathway Activity Quantification and Application to Identifying Cancer Subtypes

Jung

2015

Proceedings of the ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics

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show abstract

“…-Iterative row and column clustering combination: applying the standard clustering methods on rows and columns of the data matrix and then combining the row and column clusters to form biclusters [5]. -Divide and conquer: breaking the problem into smaller problems, solving them recursively, and combining the solutions of sub-problems to form the solution for the original problem [6].…”

Section: Related Workmentioning

confidence: 99%

A Repeated Local Search Algorithm for BiClustering of Gene Expression Data

Truong

Battiti

Brunato

2013

Similarity-Based Pattern Recognition

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Abstract. Given a gene expression data matrix where each cell is the expression level of a gene under a certain condition, biclustering is the problem of searching for a subset of genes that coregulate and coexpress only under a subset of conditions. The traditional clustering algorithms cannot be applied for biclustering as one cannot measure the similarity between genes (or rows) and conditions (or columns) by normal geometric similarities. Identifying a network of collaborating genes and a subset of experimental conditions which activate the specific network is a crucial part of the problem. In this paper, the BIClustering problem is solved through a REpeated Local Search algorithm, called BICRELS. The experiments on real datasets show that our algorithm is not only fast but it also significantly outperforms other state-of-the-art algorithms.

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“…It has been widely used for many quantitative studies, including gene expression data analysis [9,10]. This is a natural choice of approach to find relevant or similar set of abstracts or genes given co-observations of genes and abstracts.…”

Section: Related Workmentioning

confidence: 99%

BioLog: A Browser Based Collaboration and Resource Navigation Assistant for BioMedical Researchers

Singh

Bhimavarapu

Davulcu

et al. 2005

Lecture Notes in Computer Science

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Abstract. We often realize that communicating with other colleagues who are studying similar topics helps to identify information relevant to our area of study, which otherwise may not have been found. We wish to accelerate acquisition of collective knowledge in a defined area by identifying specific spheres of inquiry. Such spheres correspond to groups of people who are experts in a field. In this paper we provide a systematic way to gain knowledge from their online search activity, and enable them to organize and share their search findings for further analysis. We have built a prototype system, BioLog, to help biomedical researchers share this implicit knowledge among their peers and store their access patterns into a central system for reuse. BioLog has been deployed it in two labs within TGen as a pilot study. The data has been gathered and analyzed by preliminary text-mining and collaborative filtering methods.

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Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

Abstract: http://www.weizmann.ac.il/physics/complex/compphys/bioinfo2/

Cited by 66 publications

References 15 publications

In-silico Interaction-Resolution Pathway Activity Quantification and Application to Identifying Cancer Subtypes

In-silico Interaction-Resolution Pathway Activity Quantification and Application to Identifying Cancer Subtypes

A Repeated Local Search Algorithm for BiClustering of Gene Expression Data

BioLog: A Browser Based Collaboration and Resource Navigation Assistant for BioMedical Researchers

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