On the Selection of Appropriate Proximity Measurement for Gene Expression Data
Md. Bipul Hossen,
Arefin Mowla,
Md. Harun or Rashid,
Md. Binyamin
Issue:
Volume 5, Issue 5, October 2017
Pages:
59-63
Received:
28 January 2017
Accepted:
17 February 2017
Published:
30 June 2017
Abstract: Gene expression profile has become a useful biological resource in recent years and its plays an important role in a broad range of biology. But a large number of genes and the complexity of biological networks greatly increase the evaluation of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. In the computational analysis of gene expression data, the main aspect is to finding co-expressed genes as the proximity (similarity or dissimilarity) measures that are used in the clustering method. Several number of proximity measures work are used in the gene data but the majority of these works has given emphasis on the biological results and no critical assessment of the suitability of the proximity measures for the analysis of gene expression data. For these consequences this paper is to investigate the appropriate proximity measurement for gene expression data. As a case study, we considered six real datasets. Based on this, we provide a comparative study of five proximity measures: Euclidean distance, Manhattan distance, Pearson correlation, Spearman correlation, Cosine distance. We discuss Adjusted Rand Index, Silhouette Index of clustering to assess the quality and reliability of the results. Our results reveal that the Cosine distance method with complete linkage exhibited the best performance for both Affymetrix and cDNA datasets according to Adjusted Rand Index. Our results also reveal that the Spearman correlation measure with complete linkage exhibited the best performance for both Affymetrix and cDNA datasets according to Silhouette Index.
Abstract: Gene expression profile has become a useful biological resource in recent years and its plays an important role in a broad range of biology. But a large number of genes and the complexity of biological networks greatly increase the evaluation of comprehending and interpreting the resulting mass of data, which often consists of millions of measureme...
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Proliferation of Bovine Albumin Serum on Nano-Scale Titanium Nitride Coated Stainless Steel Substrates for Biomedical Applications
Sumaira Nosheen,
Shahzad Alam,
Muhammad Irfan,
Bilal Waseem,
Muhammad Shahid,
Badaruddin Soomro,
Quratulain Syed
Issue:
Volume 5, Issue 5, October 2017
Pages:
64-67
Received:
29 May 2017
Accepted:
13 June 2017
Published:
24 November 2017
Abstract: Biocompatible and corrosion resistant coatings are frequently used to protect and enhance the life time of bio-implants. TiN (titanium nitride) is being used as coating material on different surgical and orthopedic implants due to its excellent biocompatibility. TiN coatings are attractive because of low coefficient friction, high hardness, chemical inertness and smooth surface finish, which they will provide to biochemical devices. These coatings were prepared by PVD (physical Vapor deposition) technique on SS 304L plates to investigate the protein adhesion on coated stainless steel samples. The protein adhesion and growth of Bovine Albumin Serum (blood protein) on coated samples was checked. Surface of coated material and protein growth on coated material was investigated using AFM (atomic force microscope), SEM and FTIR.
Abstract: Biocompatible and corrosion resistant coatings are frequently used to protect and enhance the life time of bio-implants. TiN (titanium nitride) is being used as coating material on different surgical and orthopedic implants due to its excellent biocompatibility. TiN coatings are attractive because of low coefficient friction, high hardness, chemica...
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