Rough Sets for Selection of Functionally Diverse Genes from Microarray Data

2011 ◽  
pp. 477-484
Author(s):  
Sushmita Paul ◽  
Pradipta Maji
Keyword(s):  
Microarray Data ◽  
Rough Sets ◽  
Functionally Diverse ◽  
Selection Of

scholarly journals Linear Diophantine Fuzzy Soft Rough Sets for the Selection of Sustainable Material Handling Equipment

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1215
Author(s):  
Muhammad Riaz ◽  
Masooma Raza Hashmi ◽  
Humaira Kalsoom ◽  
Dragan Pamucar ◽  
Yu-Ming Chu
Keyword(s):  
Fuzzy Sets ◽  
Rough Sets ◽  
Material Handling ◽  
Optimal Decision ◽  
Soft Sets ◽  
Sustainable Material ◽  
Material Handling Equipment ◽  
Core Set ◽  
Modeling Uncertainties ◽  
Selection Of

The concept of linear Diophantine fuzzy sets (LDFSs) is a new approach for modeling uncertainties in decision analysis. Due to the addition of reference or control parameters with membership and non-membership grades, LDFS is more flexible and reliable than existing concepts of intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PFSs), and q-rung orthopair fuzzy sets (q-ROFSs). In this paper, the notions of linear Diophantine fuzzy soft rough sets (LDFSRSs) and soft rough linear Diophantine fuzzy sets (SRLDFSs) are proposed as new hybrid models of soft sets, rough sets, and LDFS. The suggested models of LDFSRSs and SRLDFSs are more flexible to discuss fuzziness and roughness in terms of upper and lower approximation operators. Certain operations on LDFSRSs and SRLDFSs have been established to discuss robust multi-criteria decision making (MCDM) for the selection of sustainable material handling equipment. For these objectives, some algorithms are developed for the ranking of feasible alternatives and deriving an optimal decision. Meanwhile, the ideas of the upper reduct, lower reduct, and core set are defined as key factors in the proposed MCDM technique. An application of MCDM is illustrated by a numerical example, and the final ranking in the selection of sustainable material handling equipment is computed by the proposed algorithms. Finally, a comparison analysis is given to justify the feasibility, reliability, and superiority of the proposed models.

scholarly journals Transcriptional profiling in an MPNST-derived cell line and normal human Schwann cells

2004 ◽  
Vol 1 (2) ◽  
pp. 135-147 ◽  
Author(s):  
PHILIP R. LEE ◽  
JONATHAN E. COHEN ◽  
ELISABETTA A. TENDI ◽  
ROBERT FARRER ◽  
GEORGE H. DE VRIES ◽  
...  
Keyword(s):  
Cell Line ◽  
Schwann Cells ◽  
Western Blotting ◽  
Microarray Data ◽  
Cdna Microarrays ◽  
Type I ◽  
Nerve Sheath Tumor ◽  
Wide Range ◽  
Normal Human ◽  
Selection Of

cDNA microarrays were utilized to identify abnormally expressed genes in a malignant peripheral nerve sheath tumor (MPNST)-derived cell line, T265, by comparing the mRNA abundance profiles with that of normal human Schwann cells (nhSCs). The findings characterize the molecular phenotype of this important cell-line model of MPNSTs, and elucidate the contribution of Schwann cells in MPNSTs. In total, 4608 cDNA sequences were screened and hybridizations replicated on custom cDNA microarrays. In order to verify the microarray data, a large selection of differentially expressed mRNA transcripts were subjected to semi-quantitative reverse transcription PCR (LightCycler). Western blotting was performed to investigate a selection of genes and signal transduction pathways, as a further validation of the microarray data. The data generated from multiple microarray screens, semi-quantitative RT–PCR and Western blotting are in broad agreement. This study represents a comprehensive gene-expression analysis of an MPNST-derived cell line and the first comprehensive global mRNA profile of nhSCs in culture. This study has identified ∼900 genes that are expressed abnormally in the T265 cell line and detected many genes not previously reported to be expressed in nhSCs. The results provide crucial information on the T265 cells that is essential for investigation using this cell line in experimental studies in neurofibromatosis type I (NF1), and important information on normal human Schwann cells that is applicable to a wide range of studies on Schwann cells in cell culture.

Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection

2017 ◽  
Vol 32 (01) ◽  
pp. 1860009 ◽  
Author(s):  
Jarvin A. Antón-Vargas ◽  
Yenny Villuendas-Rey ◽  
Cornelio Yáñez-Márquez ◽  
Itzamá López-Yáñez ◽  
Oscar Camacho-Nieto
Keyword(s):  
Information Systems ◽  
Rough Sets ◽  
Nearest Neighbor ◽  
Management Information Systems ◽  
Computational Cost ◽  
Instance Selection ◽  
Management Information ◽  
Similarity Relations ◽  
The Universe ◽  
Selection Of

This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT.

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