Combining gene expression programming and Plant

The use of gene interaction networks to improve the

Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses.

Learn More
Gene Regulation, Modulation, and Their Applications in

Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for

Learn More
RESEARCH ARTICLE

proposed a new evolutionary algorithm -Gene expression programming (GEP). Gene expression programming (GEP) is proposed by a Portuguese scientist called Candida Ferreira in 2000 and it is a new type of adaptive evolutionary algorithm based on biological structure and function. What it learns specifically is about the relationships between variables

Learn More
ARIMA Models versus Gene Expression Programming In

The models obtained state the efficiency of combining pure statistical tests and methods with heuristic approaches. Key-Words: Time series modeling, Gene Expression Programming, ARIMA, Statistical analysis, Precipitation 1 Introduction Time series are ubiquitous in the real world. They are usually generated by dynamical systems and can be

Learn More
Application of gene expression programming, artificial

Nov 27,  · Gene expression programming (GEP) GEP is an evolutionary-based algorithm that explores the genotype from the genetic algorithm (GA) and phenotype from genetic programming (GP). Like a living organism, the GEP utilizes a simple chromosome with a fixed length for keeping and transmitting genetic information and complex tree structures for

Learn More
Christina Smolke's Profile | Stanford Profiles

By providing tight gene-expression control with customizable ligand inputs, RNA-based regulatory systems can greatly improve cellular therapies and advance broad applications in health and medicine. View details for DOI 10.1073/pnas.1001721107. View details for Web of Science ID 000277591200010. View details for PubMedID 20421500

Learn More
7 Main Stages of Recombinant DNA Technology

The following points highlight the seven main stages of recombinant DNA technology. The stages are: 1.Isolation of the Genetic Material (DNA) 2. Cutting of DNA at Specific Locations 3.Isolation of Desired DNA Fragment 4. Amplification of Gene of Interest using PCR 5. Ligation of DNA Fragment into a Vector 6. Insertion of Recombinant DNA into the Host Cell/Organisms 7.

Learn More
Gene Expression Programming and Trading Strategies

Gene Expression Programming and Trading Strategies. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep , Paphos, Greece. pp.497-505, ￿10.1007/978-3

Learn More
8.2 Laws of Inheritance - Concepts of Biology - 1st

The gametes produced by the F 1 individuals must have one allele from each of the two genes. For example, a gamete could get an R allele for the seed shape gene and either a Y or a y allele for the seed color gene. It cannot get both an R and an r allele; each gamete can have only one allele per gene. The law of independent assortment states that a gamete into which an r allele is sorted would

Learn More
Attaining the promise of plant gene editing at scale | PNAS

Jun 01,  · Crop improvement relies heavily on genetic variation that arises spontaneously through mutation. Modern breeding methods are very adept at combining this genetic variation in ways that achieve remarkable improvements in plant performance. Novel traits have also been created through mutation breeding and transgenesis. The advent of gene editing, however, marks a turning point: With gene

Learn More
Semi-autogenous mill power model development using gene

Feb 15,  · Mill powers of the semi-autogenous mill have an effective influence on the mill performance. In this regard, a new predictive model based on gene expression programming (GEP) was developed to predict the mill power of the SAG mill. To achieve this purpose, a total number of 186 full-scale SAG mill works were investigated and the most effective

Learn More
Genome-wide identification ... - BMC Plant Biology

Mar 30,  · Taken together, the variational expression of SlBES1 genes under different plant hormone treatment implied that this gene family involved in multiple hormonal signals in a complicated way. The detailed role of this gene family in the crosstalk of plant hormones thus was worth to studying and may provide us the new insight in the field.

Learn More
Combining gene expression and function in a spatially

The integration of gene expression datasets with gene function information provides valuable insights in unraveling the molecular mechanisms of the brain. In this paper, gene expression maps, acquired by the technique of voxelation, are analyzed using an atlas-based framework and the extracted spatial information is employed to organize genes in significant clusters.

Learn More
CSIRO PUBLISHING | Functional Plant Biology

By combining expression profiling and virus induced gene silencing (VIGS), we are developing a genetic framework for Rpp4-mediated resistance. We measured gene expression in mock-inoculated and P. pachyrhizi -infected leaves of resistant soybean accession PI459025B ( Rpp4 ) and the susceptible cultivar (Williams 82) across a 12-day time course.

Learn More
Heterosis - Wikipedia

Heterosis, hybrid vigor, or outbreeding enhancement is the improved or increased function of any biological quality in a hybrid offspring. An offspring is heterotic if its traits are enhanced as a result of mixing the genetic contributions of its parents. These effects can be due to Mendelian or non-Mendelian inheritance

Learn More
The BAR and other Data Analysis Tools for Plant Biology

Create 'electronic fluorescent pictographic' representations of your gene of interest's expression patterns based on data from van Zhang et al., "Light-responsive expression atlas reveals the effects of light quality and intensity in Kalanchoë fedtschenkoi, a plant with crassulacean acid metabolism", GigaScience, Volume 9, Issue 3, March 2020.

Learn More
Text Mining Gene Selection to Understand Pathological

Whole transcriptome omics experiments allow for the study of gene regulation at the cellular level. During analysis and interpretation of omics data, false discovery can occur. To minimize false discovery and identify true significant cases, multi-test correction has been introduced to bioinformatics algorithms. The scientific literature offers a huge collection of information that can be

Learn More
8.5: Cloning DNA - Plasmid Vectors - Biology LibreTexts

The LibreTexts libraries are Powered by MindTouch ® and are supported by the Department of Education Open Textbook Pilot Project, the UC Davis Office of the Provost, the UC Davis Library, the California State University Affordable Learning Solutions Program, and Merlot. We also acknowledge previous National Science Foundation support under grant numbers 1246120, 1525057, and 1413739.

Learn More
PDF Subtyping of Gliomaby Combining Gene Expression and CNV's

number and expression data in ovarian cancer [1]. The significant correlation between gene expression and patient survival has been found by Magellan. Troyanskaya et al. [2] developed a Bayesian framework to combine heterogeneous data sources for predicting gene function. Improved accuracy of the gene groupings has been

Learn More
PDF Bayesian Clustering of Gene Expression Dynamics

of gene expression dynamics and a program implementing it. The method represents gene expression dynamics as autoregressive equa- the different ways of combining gene expression profiles, and a heuristic search procedure to efficiently explore the space of these combinations.

Learn More
Integrating transcriptomic network reconstruction and eQTL

To reconstruct gene co-expression networks, the fitted gene expression values for each RIL from the limma-voom fit (expression ~ RIL) were used and filtered to keep the top 10,000 genes most variable between RILs. For each sample type, two network reconstruction methods were used. First, mutual correlation rank (MR) networks [] with the

Learn More
Gene co-expression network analysis reveals pathways

Results: Using weighted gene co-expression analysis, variable transcripts were clustered into 10 distinct co-expression networks (modules) based on expression profiles, and genes with the most "hubness" ("hub" genes show the most connections in a network) within each module were predicted. A large proportion of modules were related to Position

Learn More
PDF) Combining gene expression QTL mapping and phenotypic

Combining gene expression QTL mapping and phenotypic spectrum analysis to uncover gene regulatory relationships Lei Bao,1,2 Lai Wei,3,4 Jeremy L. Peirce,2,4 Ramin Homayouni,2,4 Hongqiang Li,1,2 Mi Zhou,1,2 Hao Chen,5 Lu Lu,2,4 Robert W. Williams,2,4,6 Lawrence M. Pfeffer,3 Dan Goldowitz,2,4 Yan Cui1,2 1 Department of Molecular Sciences, University of Tennessee Health Science Center, Memphis

Learn More
Beyond natural: synthetic expansions of botanical form and

User control over gene expression in plants has long been achieved using ligand-responsive systems. Systems that respond to applications of, for example, estradiol (Zuo et al. , 2000 ) typically comprise a constitutively expressed synthetic transcription factor used in combination with a synthetic promoter with cognate binding sites fused to

Learn More
PDF) EGIPSYS: an enhanced gene expression programming

Fundamentals of Gene Expression ProgrammingGene Expression Programming was proposed by Ferreira (2001) as an alternative to overcome the common drawbacks of GA and GP for real-world problems. The main difference between GEP, GA and GP resides in the way individuals of a population of solutions are represented.

Learn More
Paean: A parallel transcriptome quantification tool

RNA-seq is one of the most widely used methods to probe gene expression and alternative splicing events (ASE) at the transcriptome scale. It often generates large amounts of complex sequencing data, which expands rapidly in large scale studies containing many samples. Quantifying short RNA reads distribution on reference genomes is a key step for most analyses of RNA-seq.

Learn More

Leave a comment