Guidelines for genetic data analysis

Data Envelopment Analysis, Genetic Algorithm, Hyperplane, Efficient Unit

Data Envelopment Analysis (DEA) is actually to obtain the efficiency using inputs and outputs, which can determine efficient and inefficient units with the help of performance calculations such that the efficiency for efficient DMUs is one and less than one for inefficient DMUs [1]. In some cases, the ranking of the decision-making units are not important for decision-makers, and they are only ...

Guidelines for RNA-Seq data analysis

New guidelines for the publication of genetic population data.

In 2000 a new policy concerning the publication of population genetic data was set up in Forensic Science International [1] with the introduction of a new section entitled ‘‘Announcement of population data’’. Subsequently in 2010 [2] a new section on ‘‘Forensic Population Genetics’’ was introduced, and recommendations were redefined. FSI: Genetics is one of the few journals still considering po...

Data envelopment analysis for imprecise data in Buyer-Seller Relationship

In the environment of business‐to‐business e‐commerce, Buyers and sellers in mature industrial markets can turn single transactions into long-term beneficial relationships by a deeper understanding of the complex connection between the two and buyers and sellers are uncertain about their roles. A “must-do” for the sellers, in particular, is to understand patterns of investment and reward,...

Fuzzy Data Envelopment Analysis for Classification of Streaming Data

The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle strea...