Pub Date : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735923
Mohammadmahdi R. Yousefi
We take a Markovian approach to modeling gene regulatory networks and assume that phenotypes are characterized by the steady-state probability distribution of such networks. We desire intervention policies that maximally shift the probability mass from undesirable states to desirable ones. In doing so, we might also be concerned about the steady-state mass of some “ambiguous” states, which are not directly related to the pathology of interest but could be associated with some anticipated risks. We propose a direct formulation of this constrained optimization problem, rather than assuming a subjective cost function, and provide optimal intervention policies. Within this framework, we investigate the performance of “compromised” policies, these being policies for which we accept some increase of the ambiguous mass to achieve more decrease in the undesirable mass.
{"title":"Compromised intervention policies for phenotype alteration","authors":"Mohammadmahdi R. Yousefi","doi":"10.1109/GENSIPS.2013.6735923","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735923","url":null,"abstract":"We take a Markovian approach to modeling gene regulatory networks and assume that phenotypes are characterized by the steady-state probability distribution of such networks. We desire intervention policies that maximally shift the probability mass from undesirable states to desirable ones. In doing so, we might also be concerned about the steady-state mass of some “ambiguous” states, which are not directly related to the pathology of interest but could be associated with some anticipated risks. We propose a direct formulation of this constrained optimization problem, rather than assuming a subjective cost function, and provide optimal intervention policies. Within this framework, we investigate the performance of “compromised” policies, these being policies for which we accept some increase of the ambiguous mass to achieve more decrease in the undesirable mass.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":" 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113951963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-10-01DOI: 10.1109/GENSIPS.2013.6735919
Amina Noor, A. Ahmad, E. Serpedin, M. Nounou, H. Nounou
Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF) - gene regulations. We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. ROBNCA algorithm provides a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared to FastNCA and the Non-iterative NCA (NI-NCA) and is shown to estimate the TF activity profiles as well as the TF-gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, and/or amount of outliers in case of synthetic data. The run time of the ROBNCA algorithm is comparable to that of FastNCA, and is hundreds of times faster than NI-NCA.
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