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Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis [chapter]

Ruggero G. Pensa, Jean-François Boulicaut
2005 Lecture Notes in Computer Science  
In the domain of gene expression data analysis, several researchers have recently emphasized the promising application of local pattern (e.g., association rules, closed sets) discovery techniques from  ...  To take the most from local set pattern mining approaches, a needed step concerns gene expression property encoding (e.g., over-expression).  ...  The authors want to thank Céline Robardet, Sylvain Blachon and Olivier Gandrillon for the pre-processing of the SAGE data set, and Sophie Rome for her participation to microarray data preparation.  ... 
doi:10.1007/11504245_8 fatcat:asxz7ttcnbfojlxeeac5owhqla

Redescription Mining and Applications in Bioinformatics [chapter]

Naren Ramakrishnan, Mohammed Zaki
2009 Chapman & Hall/CRC Data Mining and Knowledge Discovery Series  
We present algorithms for redescription mining based on formal concept analysis and applications of redescription mining to multiple biological datasets.  ...  Given a set of biological objects (e.g., genes, proteins) and a collection of descriptors defined over this set, the goal of redescription mining is to use the given descriptors as a vocabulary and find  ...  The key property of a redescription, like most data mining patterns, is that it must be falsifiable in some interpretation (dataset).  ... 
doi:10.1201/9781420086850.ch22 fatcat:lelc5qyxrzggnkuahgu7ticihe

Integrated analysis of gene expression by Association Rules Discovery

Pedro Carmona-Saez, Monica Chagoyen, Andres Rodriguez, Oswaldo Trelles, Jose M Carazo, Alberto Pascual-Montano
2006 BMC Bioinformatics  
In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique.  ...  The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns.  ...  We also want to thank the reviewers of this paper for their constructive comments that have aided in improving this work.  ... 
doi:10.1186/1471-2105-7-54 pmid:16464256 pmcid:PMC1386712 fatcat:eukxnzloozdz3pr7ibtyk4sa2a

Large-Scale Differentiable Causal Discovery of Factor Graphs [article]

Romain Lopez, Jan-Christian Hütter, Jonathan K. Pritchard, Aviv Regev
2022 arXiv   pre-print
RNA sequencing data set with hundreds of genetic interventions.  ...  We propose Differentiable Causal Discovery of Factor Graphs (DCD-FG), a scalable implementation of f-DAG constrained causal discovery for high-dimensional interventional data.  ...  She was an SAB member of ThermoFisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics, and Asimov until July 31st, 2020; she has been an employee of Genentech since August 1st, 2020, and has equity  ... 
arXiv:2206.07824v1 fatcat:vsiwsimwvbhahmbeipjmskuime


Pauli Miettinen, Jilles Vreeken
2014 ACM Transactions on Knowledge Discovery from Data  
We discuss how to construct an appropriate encoding: starting from a simple and intuitive approach, we arrive at a highly efficient data-to-model-based encoding for BMF.  ...  We extend an existing algorithm for BMF to use MDL to identify the best Boolean matrix factorization, analyze the complexity of the problem, and perform an extensive experimental evaluation to study its  ...  We discussed how to construct an appropriate encoding for BMF, starting by introducing simple and intuitive approaches, and progressing to a highly efficient typed DtM-based encoding.  ... 
doi:10.1145/2601437 fatcat:grz322vafjaxthpxdohnonw74y

On the discovery of association rules by means of evolutionary algorithms

María J. del Jesus, José A. Gámez, Pedro González, José M. Puerta
2011 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery  
Association rule learning is a data mining task that tries to discover interesting relations between variables in large databases.  ...  A review of association rule learning is presented that focuses on the use of evolutionary algorithms not only applied to Boolean variables but also to categorical and quantitative ones.  ...  Thus, the following applications can be found: 66 apply knowledge discovery in the form of ARs to the analysis of gene expression data in order to identify patterns of genes and regulatory network.  ... 
doi:10.1002/widm.18 fatcat:hblu7qeye5ac3it5f3n6yma7wa

Supporting scientific knowledge discovery with extended, generalized Formal Concept Analysis

Francisco J. Valverde-Albacete, José María González-Calabozo, Anselmo Peñas, Carmen Peláez-Moreno
2016 Expert systems with applications  
techniques that support scientific hypothesis-making and discovery in a range of domains: semiring theory, perceptual studies, natural language semantics, and gene expression data analysis.  ...  In this paper we fuse together the Landscapes of Knowledge of Wille's and Exploratory Data Analysis by leveraging Formal Concept Analysis (FCA) to support data-induced scientific enquiry and discovery.  ...  Acknowledgments FJVA and AP were partially supported by EU FP7 project LiMo-SINe (contract 288024) for this research.  ... 
doi:10.1016/j.eswa.2015.09.022 fatcat:l7fequzx65hkxkrota5umcgtoa


Kun-Mao Chao
2004 Selected Topics in Post-Genome Knowledge Discovery  
an optimal solution, and the traceback for delivering an optimal solution.  ...  Here we introduce these basic ideas by developing dynamic-programming solutions for problems from different application areas.  ...  The author would like to thank Drs. Louis Chen and Louxin Zhang for their support and encouragement.  ... 
doi:10.1142/9789812794840_0001 fatcat:d2c7nvvc75fwbpmhrak74vhm4u

Pattern discovery in gene regulation; designing an analysis environment

S Veretnik, B Schatz
1993 Proceedings. International Conference on Intelligent Systems for Molecular Biology  
The analysis tools will serve as an ideal environment for 'dry biology' experimentation and provide a context for 'wet' experiments.  ...  'Wet lab' biology has an inherent difficulty in considering multiple components within one experimental set-up.  ...  Analysis environment, wCS supports the beginning of an analysis environment: an exploratory navigation across multiple sources to discover patterns within the data and literature.  ... 
pmid:7584365 fatcat:5p6cymynivc6ljvg5tua32uyjy

Advanced Systems Biology Methods in Drug Discovery and Translational Biomedicine

Jun Zou, Ming-Wu Zheng, Gen Li, Zhi-Guang Su
2013 BioMed Research International  
The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research  ...  gene prediction.  ...  Furthermore, numerous systems biology approaches based on gene expression data for in silico drug repositioning have been published [62, 63] .  ... 
doi:10.1155/2013/742835 pmid:24171171 pmcid:PMC3792523 fatcat:nhsv3ig7vzgspjhnzkaj3pwypm

Model order selection for boolean matrix factorization

Pauli Miettinen, Jilles Vreeken
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
We extend an existing algorithm for BMF to use MDL to identify the best Boolean matrix factorization, analyze the complexity of the problem, and perform an extensive experimental evaluation to study its  ...  We formulate the description length function for BMF in generalmaking it applicable for any BMF algorithm.  ...  As such, BMF essentially describes the data with a set of patterns. Therefore, pattern set mining techniques are related.  ... 
doi:10.1145/2020408.2020424 dblp:conf/kdd/MiettinenV11 fatcat:pb7xvqypa5ayff5gvwis4uc2wu

Genomics, "Discovery Science," Systems Biology, and Causal Explanation: What Really Works?

Eric H. Davidson
2015 Perspectives in biology and medicine  
alternatives, such as "discovery science," some productions of the ENCODE project, and aspects of large data set systems biology.  ...  Their properties emerge in high relief when contrasted (as an example) to a recent, system-wide, predictive analysis of a developmental regulatory apparatus that was instead based directly on hypothesis-driven  ...  (also known as discovery-based science) is a scientific methodology which emphasizes analysis of large volumes of experimental data with the goal of finding new patterns or correlations, leading to hypothesis  ... 
doi:10.1353/pbm.2015.0025 pmid:26750600 fatcat:tpxw5j5hmvf73m5y5wvjju7cxu

Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue

Matthias E Futschik, Anthony Reeve, Nikola Kasabov
2003 Artificial Intelligence in Medicine  
Microarray techniques have made it possible to observe the expression of thousands of genes simultaneously. They have recently been applied to study gene expression patterns in tissue samples.  ...  Statistical and machine learning methods have recently been used to classify cancer tissue based on gene expression data.  ...  Acknowledgements We would like to thank the reviewers for their constructive comments and Bronwyn Carlisle for proof-reading.  ... 
doi:10.1016/s0933-3657(03)00063-0 pmid:12893118 fatcat:e4c2dcu7qbdjdprbze63jecisy

Selfish: Discovery of Differential Chromatin Interactions via a Self-Similarity Measure [article]

Abbas Roayaei Ardakany, Ferhat Ay, Stefano Lonardi
2019 bioRxiv   pre-print
A fundamental problem inthe analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments.  ...  :We present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps.  ...  Then we computed the percentage of those genes having an expression fold change of two or greater. For the set of genes overlapping FIND's DCIs, 71.46% of them were over-expressed.  ... 
doi:10.1101/540708 fatcat:hbmcrt2phzb5ne3ynrzy2uig6m

Enhancing knowledge discovery via association-based evolution of neural logic networks

H.W.K. Chia, C.L. Tan, S.Y. Sung
2006 IEEE Transactions on Knowledge and Data Engineering  
Coupled with a sequential covering approach for generating a list of neulonets, the straightforward extraction of human-like logic rules from each neulonet provides an alternate perspective to the greater  ...  This is due to the richness in logic expression inherent in the neulonet learning paradigm.  ...  for extracting patterns from data and . the interpretation component that relates to the novelty, utility, and understanding of the mined patterns or rules.  ... 
doi:10.1109/tkde.2006.111 fatcat:wdesnhdmgzdm3jornrectecuom
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