Not Available

One of the most important challenges facing Indian agriculture is to provide food & nutritional security for rural resource-poor communities in the wake of decreasing land holdings. Hence, selection of suitable cultivar or variety for specific environment is very much essential. The farmer’s risk can be minimized and it may improve their economic condition through selection of stable genotypes by using a suitable stability measure. Evaluation of genotypes on the basis of stability measure is essential for yield trials in different environments. Though, large numbers of stability measures are available in literature, but deciding the proper stability measure for selecting stable genotypes is problematic. Multiple Criteria Decision-Making (MCDM) technique or Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been employed to develop the proposed measure. In the present study, a suitable composite measure is developed by combining several methods into a single aggregate method by using MCDM technique, for selecting suitable genotypes which would be stable to environmental variations.Not Availabl

Development of Composite Stability Measure using Multi Criteria Decisions Making (MCDM) Techniques

Not AvailableOne of the most important challenges facing Indian agriculture is to provide food & nutritional security for rural resource-poor communities in the wake of decreasing land holdings. Hence, the selection of a suitable cultivar or variety for a specific environment is very much essential. The farmer’s risk can be minimized and it may improve their economic condition through selection of stable genotypes by using a suitable stability measure. Evaluation of genotypes on the basis of stability measures is essential for yield trials in different environments. Though large numbers of stability measures are available in the literature, deciding the proper stability measure for selecting stable genotypes is problematic. Multiple Criteria Decision-Making (MCDM) technique or Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been employed to develop the proposed measure. In the present study, a suitable composite measure is developed by combining several methods into a single aggregate method by using MCDM technique, for selecting suitable genotypes which would be stable to environmental variations.ICA

Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (<i>Glycine max</i> L.)

<div><p>Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case <i>vs</i>. control study. Based on this proposed approach, an R package, i.e., dhga (<a href="https://cran.r-project.org/web/packages/dhga" target="_blank">https://cran.r-project.org/web/packages/dhga</a>) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.</p></div

Clustering dendrogram of selected genes and gene modules under Al stress and control condition.

<p>The correspondence between Consensus Modules (CM) with modules under Stress (SM) (A) and control (NM) (B) conditions is represented.</p

Comparison of proposed and existing approach in terms of predicted hub genes.

<p>Comparison of proposed and existing approach in terms of predicted hub genes.</p

Comparison of Boot-SVM-RFE with other competitive algorithms for different sliding window sizes.

<p>Comparison of Boot-SVM-RFE with other competitive algorithms for different sliding window sizes.</p

Groups of hub genes predicted using DHGA approach.

<p>Groups of hub genes predicted using DHGA approach.</p

Decision matrix for differential hub gene analysis.

<p>Decision matrix for differential hub gene analysis.</p

Functional enrichment analysis of selected genes and hub genes under Al stress.

<p>The GO term enrichment analysis of 981 selected informative genes (A) and hub genes (B) for Al stress condition using <i>Agrig</i>o is shown for different gene ontology categories (CC, MF and BP). For (A), the GO terms are chosen whose p-values < 0.008 and FDR values (false discovery rate) < 0.6. For (B), the GO terms are chosen whose p-values < 0.1 and FDR values < 0.8.</p