Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice.
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Microarray Meta-Analysis Focused on the Response of Genes Involved in Redox Homeostasis to Diverse Abiotic Stresses in RicePaenibacillus lentimorbus Inoculation Enhances Tobacco Growth and Extenuates the Virulence of Cucumber mosaic virusexpVIP: a Customizable RNA-seq Data Analysis and Visualization Platform.CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules.Enhancing crop resilience to combined abiotic and biotic stress through the dissection of physiological and molecular crosstalk.Transcriptome analysis reveals genes commonly induced by Botrytis cinerea infection, cold, drought and oxidative stresses in Arabidopsis.Novel and conserved heat-responsive microRNAs in wheat (Triticum aestivum L.).Identification of Arabidopsis candidate genes in response to biotic and abiotic stresses using comparative microarrays.Identification of conserved drought-adaptive genes using a cross-species meta-analysis approachComprehensive meta-analysis, co-expression, and miRNA nested network analysis identifies gene candidates in citrus against Huanglongbing disease.Transcriptome Analysis of Sunflower Genotypes with Contrasting Oxidative Stress Tolerance Reveals Individual- and Combined- Biotic and Abiotic Stress Tolerance Mechanisms.Genome-wide association analysis of seedling traits in diverse Sorghum germplasm under thermal stress.Low-Temperature-Induced Expression of Rice Ureidoglycolate Amidohydrolase is Mediated by a C-Repeat/Dehydration-Responsive Element that Specifically Interacts with Rice C-Repeat-Binding Factor 3Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.Microarray: gateway to unravel the mystery of abiotic stresses in plants.Global Transcriptome Analysis of Combined Abiotic Stress Signaling Genes Unravels Key Players in Oryza sativa L.: An In silico ApproachCell Wall Metabolism in Response to Abiotic Stress.Plant adaptations to the combination of drought and high temperatures.The tomato res mutant which accumulates JA in roots in non-stressed conditions restores cell structure alterations under salinity.Differential regulation of genes by retrotransposons in rice promoters.Effect of Co-segregating Markers on High-Density Genetic Maps and Prediction of Map Expansion Using Machine Learning Algorithms.Revealing shared and distinct gene network organization in Arabidopsis immune responses by integrative analysis.The phenotype alterations showed by the res tomato mutant disappear when the plants are grown under semi-arid conditions: Is the res mutant tolerant to multiple stresses?Rice WRKY11 Plays a Role in Pathogen Defense and Drought Tolerance.Identification and comparative analysis of microRNAs in barnyardgrass (Echinochloa crus-galli) in response to rice allelopathy.Integrative network analyses of wilt transcriptome in chickpea reveal genotype dependent regulatory hubs in immunity and susceptibility.Cross-species multiple environmental stress responses: An integrated approach to identify candidate genes for multiple stress tolerance in sorghum (Sorghum bicolor (L.) Moench) and related model species.Cultivar-Dependent Responses of Eggplant ( L.) to Simultaneous Infection and Drought
P2860
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P2860
Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice.
description
2013 nî lūn-bûn
@nan
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
2013年论文
@zh
2013年论文
@zh-cn
name
Machine learning approaches di ...... for broad resistance in rice.
@en
type
label
Machine learning approaches di ...... for broad resistance in rice.
@en
prefLabel
Machine learning approaches di ...... for broad resistance in rice.
@en
P2860
P356
P1433
P1476
Machine learning approaches di ...... for broad resistance in rice.
@en
P2093
Rafi Shaik
P2860
P304
P356
10.1104/PP.113.225862
P407
P577
2013-11-14T00:00:00Z