Filters








15,650 Hits in 4.3 sec

Mining for Mutually Exclusive Gene Expressions [chapter]

George Tzanis, Ioannis Vlahavas
2010 Lecture Notes in Computer Science  
Several extensions of the traditional association rules mining model have been proposed so far, however, the problem of mining for mutually exclusive items has not been investigated.  ...  First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we apply on gene expression data  ...  Discretization The data that will be used for mining the mutually exclusive gene expressions should contain binary values.  ... 
doi:10.1007/978-3-642-12842-4_29 fatcat:egv5gikox5g3nfoe4dz46av4om

Mining and visualising contradictory data

Honour Chika Nwagwu, George Okereke, Chukwuemeka Nwobodo
2017 Journal of Big Data  
The dataset was also manually explored for issues of contradictory data in tissues associated with the gene "TSPAN6" where the gene expresses a medium and a low expression levels.  ...  They queried the dataset for instances where a tissue is associated with the gene TSPAN6 which expresses a medium and a low expression levels.  ...  Consent for publication Not applicable. Ethics approval and consent to participate Not applicable. Funding None.  ... 
doi:10.1186/s40537-017-0100-9 fatcat:v5d7dyxttjejzfqli6cmehb3gi

Mining phenotypes and informative genes from gene expression data

Chun Tang, Aidong Zhang, Jian Pei
2003 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03  
In this paper, we propose a new problem of simultaneously mining phenotypes and informative genes from gene expression data.  ...  Mining microarray gene expression data is an important research topic in bioinformatics with broad applications.  ...  Nevertheless, in phenotype and informative gene mining, a sample must be in a phenotype and the phenotypes are exclusive.  ... 
doi:10.1145/956804.956835 fatcat:5gm3t7lwtzaknb6bws7xxhr6ja

Mining phenotypes and informative genes from gene expression data

Chun Tang, Aidong Zhang, Jian Pei
2003 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03  
Mutual Reinforcing Adjustment Divide the original matrix into a series of exclusive sub-matrices based on partitioning both the samples and genes.  ...  Iteratively adjust the partition and the gene set toward the optimal solution. o for each gene, try possible insert/remove o for each sample, try best movement.  ... 
doi:10.1145/956750.956835 dblp:conf/kdd/TangZP03 fatcat:dvutqtk7dbbhllak7g3nctdx4y

Mining colon cancer specific alternative splicing in EST database

Tien-Hsiung Ku, Fang Rong Hsu
2005 AMIA Annual Symposium Proceedings  
There were 53 3' splicing, 42 5' splicing, 40 exon skipping and 2 mutual exclusive cancer specific splicing isoforms.  ...  Among 75218 splicing sites, 137 colon cancer specific alternative splicing isoforms were found by mining EST database.  ...  We demonstrate here the colon cancer specific alternative splicing isoforms found by mining human expressed sequence tags (EST) database.  ... 
pmid:16779299 pmcid:PMC1560692 fatcat:77557qqkg5h7hk5mu6bspgvcgy

Evaluation of K-ras and p53 expression in pancreatic adenocarcinoma using the cancer genome atlas

Liming Lu, Jingchun Zeng, Sumitra Deb
2017 PLoS ONE  
The mutual exclusivity analysis showed that events in K-ras and p53 were likely to co-occur in pancreatic adenocarcinoma (Log odds ratio = 1.599, P = 0.006).  ...  by the cBioPortal for Cancer Genomics.  ...  The funder had no further role in study design, data collection, analysis and interpretation of data, writing of the report or the decision to submit the paper for publication.  ... 
doi:10.1371/journal.pone.0181532 pmid:28742845 pmcid:PMC5526503 fatcat:fmsrwdrwj5dgjndphvabowz2am

Mining TCGA Data Using Boolean Implications

Subarna Sinha, Emily K. Tsang, Haoyang Zeng, Michela Meister, David L. Dill, Vladimir B. Bajic
2014 PLoS ONE  
A direct comparison of the relationships found by Boolean implications and those found by commonly used methods for mining associations show that existing methods would miss relationships found by Boolean  ...  In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression) from the glioblastoma  ...  Boolean implications provide a conceptually simple and computationally efficient tool for mining subset and mutual exclusion relationships in cancer data.  ... 
doi:10.1371/journal.pone.0102119 pmid:25054200 pmcid:PMC4108374 fatcat:2z6g3onyu5cqlbuj4i2j4pqcyy

Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets

Songjian Lu, Kevin N. Lu, Shi-Yuan Cheng, Bo Hu, Xiaojun Ma, Nicholas Nystrom, Xinghua Lu, Niko Beerenwinkel
2015 PLoS Computational Biology  
Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data.  ...  However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways.  ...  Acknowledgments The authors would like to thank the Extreme Science and Engineering Discovery Environment (XSEDE) for providing use of the Blacklight system at the Pittsburgh Supercomputing Center (PSC  ... 
doi:10.1371/journal.pcbi.1004257 pmid:26317392 pmcid:PMC4552843 fatcat:ej4d225hufgz3fapshcdmz72di

Identifying driver genomic alterations in cancers by searching minimum-weight, mutually exclusive sets

Songjian Lu, Kevin Lu, Shi-Yuan Cheng, Bo Hu, Xiaojun Ma, Nicholas Nystrom, Xinghua Lu
2015 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data.  ...  However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways.  ...  Acknowledgments The authors would like to thank the Extreme Science and Engineering Discovery Environment (XSEDE) for providing use of the Blacklight system at the Pittsburgh Supercomputing Center (PSC  ... 
doi:10.1109/bibm.2015.7359942 dblp:conf/bibm/LuLCHMNL15 fatcat:ob6mviigbzfnpenpqbdtccb34e

Data mining analysis of miR-638 and key genes interaction in cisplatin resistant triple-negative breast cancer

Adam Hermawan, Herwandhani Putri
2019 Indonesian Journal of Biotechnology  
This present study aimed to identfy the key gene regulatory networks of miR‐638 and evaluate the potental role of the miR‐638 and its targets as potental prognosis biomarkers for cisplatn‐resistance triple‐negatve  ...  Moreover, a Kaplan‐Meier survival plot showed that breast cancer patents with low mRNA levels of FZD7 had signifcantly worse overall survival than those in higher mRNA expression group.  ...  SRGAP1 FZD7 0.911 <-3 Mutual exclusivity HIC2 FZD7 0.992 <-3 Mutual exclusivity  ... 
doi:10.22146/ijbiotech.48732 fatcat:bcl5njxxwjacvkqevya7txd7rm

Understanding Genotype-Phenotype Effects in Cancer via Network Approaches

Yoo-Ah Kim, Dong-Yeon Cho, Teresa M. Przytycka, Rachel Karchin
2016 PLoS Computational Biology  
Indeed, network-centric approaches have proven to be helpful for finding genotypic causes of diseases, classifying disease subtypes, and identifying drug targets.  ...  Cancer is now increasingly studied from the perspective of dysregulated pathways, rather than as a disease resulting from mutations of individual genes.  ...  Some methods were extended to detect multiple mutually exclusive groups of genes [67, 68] or to refine mutual exclusivity models to account for temporal dynamics [69] .  ... 
doi:10.1371/journal.pcbi.1004747 pmid:26963104 pmcid:PMC4786343 fatcat:p2zqfax3gfag7jutp3tq52tqve

Mining Large Heterogeneous Cancer Data Sets Using Boolean Implications [article]

Subarna Sinha, David L Dill
2016 bioRxiv   pre-print
In this paper, we describe their usage in mining associations from large, heterogeneous cancer data sets.  ...  We hypothesized that Boolean implications would be useful in the context of mining heterogeneous cancer data sets because (1) they can expose subset and mutual exclusion relationships, both of which have  ...  Accordingly, we extracted Boolean implications between mutation, copy number alteration, DNA methylation and gene expression for several large TCGA data sets.  ... 
doi:10.1101/045021 fatcat:zhts6yr3gvfpfgkmg6xuggevm4

Network-Based Method for Inferring Cancer Progression at the Pathway Level from Cross-Sectional Mutation Data

Hao Wu, Lin Gao, Nikola K. Kasabov
2016 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
level rather than at the gene level.  ...  To solve the problem efficiently, we present a Network-based method (NetInf) to Infer cancer progression at the pathway level from cross-sectional data across many patients, leveraging on the exclusive  ...  In mutually exclusive patterns, the mutations tend to occur in different patients.  ... 
doi:10.1109/tcbb.2016.2520934 pmid:26915128 fatcat:ezza7jynzzb4renufa533f2364

Exploiting synthetic lethal vulnerabilities for cancer therapy [article]

Sriganesh Srihari
2016 arXiv   pre-print
In a recent study, we showed that mutual exclusive combinations of genetic events in cancer hint at naturally occurring synthetic lethal combinations, and therefore by systematically mining for these combinations  ...  Based on this, we had identified a list of 718 genes that are mutually exclusive to six DNA-damage response genes in cancer.  ...  Figure 3: A network of 718 genes mutually exclusive (predicted to be synthetic lethal) to defects in at least one of the six DDR genes in the four cancers analysed in the study [9] .  ... 
arXiv:1602.00096v1 fatcat:zc65icvyefagdjn2cr4vl4s65m

Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features

Sung-Bae Cho, Jungwon Ryu
2002 Proceedings of the IEEE  
In order to predict the cancer class of patients from the gene expression profile, this paper presents a classification framework that combines a pair of classifiers trained with mutually exclusive features  ...  Gene expression profiles are just sequences of numbers, and the necessity of tools analyzing them to get useful information has risen significantly.  ...  In order to classify the gene expression profile, we suggest a classifier ensemble composed of multiple classifiers and show the usefulness of mutually exclusive features.  ... 
doi:10.1109/jproc.2002.804682 fatcat:spzlt33aeza6flphxfz2qelx6u
« Previous Showing results 1 — 15 out of 15,650 results