Neural Model of the Genetic Network

Vohradský, Jiřı́

doi:10.1074/jbc.m104391200

Cited by 133 publications

(89 citation statements)

References 25 publications

(32 reference statements)

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“…Anti-ER antibody just marginally decreased such complexes, suggesting that a low amount of ER may be bound to AP-1 sites under these conditions/cells. Moreover, DIBA did not display an inhibitory effect on E2-or TAMstimulated AP-1-binding activity, possibly because ER binding to DNA is not required for its activity through the nonclassical AP-1 pathway (Jakacka et al, 2001;Webb et al, 1999). These data further support the specificity of DIBA influencing ER binding to DNA.…”

Section: Resultsmentioning

confidence: 99%

Endocrine Therapy of Breast Cancer

Clarke¹

2007

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE 01-06-20072. REPORT TYPE Annual DATES COVERED PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBERGeorgetown University Washington, DC 20007 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S) U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland 21702-5012 SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION / AVAILABILITY STATEMENTApproved for Public Release; Distribution Unlimited SUPPLEMENTARY NOTESOriginal contains colored plates: ALL DTIC reproductions will be in black and white. ABSTRACTA recent controversy in the treatment of estrogen receptor positive (ER+) breast cancers is whether an aromatase inhibitor, e.g., letrozole (LET) or TAM should be given as first line endocrine therapy. Unfortunately, response rates are lower, and response durations are shorter, on crossover than when these agents are given as first line therapies, e.g., ~40% of tumors show crossresistance to TAM or an aromatase inhibitor on crossover. Only 50% of ER+ tumors respond to endocrine therapy. Currently, we fail to predict endocrine responsiveness in about 66% of ER+/PgR-(progesterone receptor), 55% of ER-/PgR+, and 25% of ER+/PgR+ tumors. In this new Clinical Translational Research Award, we hypothesize that our analytical methods can extract expression profiles of breast tumors that define their responsiveness (sensitive vs. resistant) to endocrine therapy. These profiles, when combined with known predictive/prognostic factors, will support neural network and biostatistical classifiers or committee machines that predict each tumor's endocrine responsiveness. Our objectives are to array breast cancer cases, build classifiers of endocrine responsiveness (using microarray data), and validate these classifiers in independent data sets. In the long term, we will design custom arrays for use in clinical practice. Genes will be further studied using cellular and molecular methods, and their role as therapeutic targets explored. SUBJECT TERMSAntiestrogen, aromatase inhibitor, anastrazole, bioinformatics, biomarkers, biostatistics, breast c...

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Section: Resultsmentioning

confidence: 99%

Endocrine Therapy of Breast Cancer

Clarke¹

2007

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“…일반적으로 생물이 생리적, 대사활동 등을 제어하는 기능은 단일 유전자의 단독기능이 아니라 여러 유전자들 의 연쇄반응에 결과다 (Geard 와 Wiles, 2005;Schilstra 과 Bolouri, 2002;Vohradsky, 2001). 유전자 칩분석은 이러한 측면에서 대량의 유전자들의 변화를 한번에 효 과적으로 처리하는데 용이한 기법이라고 할 수 있다 (Benjamin와 Carols, 2011;Ki 등, 2009;Leu 등, 2011 (Finney, 1952;Litchfield 와 Wilcoxon, 1949 (Fig.…”

Section: ) 1 서론unclassified

Acute Toxicity of Cadmium on Gene Expression Profiling of Fleshy Shrimp, Fenneropenaeus Chinensis Postlarvae Using a cDNA Microarray

Kim¹,

Qiao²,

Yoon³

et al. 2015

Journal of Environmental Science International

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Microarray technology provides a unique tool for the determination of gene expression at the level of messenger RNA (mRNA). This study, the mRNA expression profiles provide insight into the mechanism of action of cadmium in Fleshy shrimp (Fenneropenaeus chinensis). The ability of genomic technologies was contributed decisively to development of new molecular biomarkers and to the determination of new possible gene targets. Also, it can be approach for monitoring of trace metal using oligo-chip microarray-based in potential model marine user level organisms.15K oligo-chip for F. chinensis that include mostly unique sets of genes from cDNA sequences was developed. A total of 13,971 spots (1,181 mRNAs up-regulated and 996 down regulated) were identified to be significantly expressed on microarray by hierarchical clustering of genes after exposure to cadmium for different conditions (Cd24-5000 and Cd48-1000). Most of the changes of mRNA expression were observed at the long time and low concentration exposure of Cd48-1000. But, gene ontology analysis (GO annotation) were no significant different between experiments groups. It was observed that mRNA expression of main genes involved in metabolism, cell component, molecular binding and catalytic function. It was suggested that cadmium inhibited metabolism and growth of F. chinensis .

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“…The recurrent neural network (RNN) model formulates the genetic interactions in terms of a neural network in which the nodes correspond to genes and the connections correspond to regulatory interactions among genes [6,13]. In canonical RNN model the interactions among genes is represented in terms of a tightly coupled system given by…”

Section: Recurrent Neural Network (Rnn) Modelmentioning

confidence: 99%

Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model

Noman

Palafox

Iba

2013

Proceedings in Information and Communications Technology

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Abstract. In this work we used the decoupled version of the recurrent neural network (RNN) model for gene network inference from gene expression data. In the decoupled version, the global problem of estimating the full set of parameters for the complete network is divided into several sub-problems each of which corresponds to estimating the parameters associated with a single gene. Thus, the decoupling of the model decreases the problem dimensionality and makes the reconstruction of larger networks more feasible from the point of algorithmic perspective. We applied a well established evolutionary algorithm called differential evolution for inferring the underlying network structure as well as the regulatory parameters. We investigated the effectiveness of the reconstruction mechanism in analyzing the gene expression data collected from both synthetic and real gene networks. The proposed method was successful in inferring important gene interactions from expression profiles.

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Neural Model of the Genetic Network

Cited by 133 publications

References 25 publications

Endocrine Therapy of Breast Cancer

Endocrine Therapy of Breast Cancer

Acute Toxicity of Cadmium on Gene Expression Profiling of Fleshy Shrimp, Fenneropenaeus Chinensis Postlarvae Using a cDNA Microarray

Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model

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