| Forthcoming Papers > International Journal of Biotechnology (IJBT) Journal Homepage This page lists papers submitted for IJBT via the web that have been reviewed and accepted but not yet published. Please note that titles, authors, abstracts and keywords may change upon publication. Our TOC e-mail alerting service will notify you immediately when new issues of IJBT are published on-line. Click here to register for our TOC E-Mail Alerting. We also offer the convenience of RSS feeds which provide a means to view new content timely posted to your web site or desktop. Click here to start to use our free RSS news feeds. | International Journal of Biotechnology (2 papers in press)
- The use of performance measurements in the biotechnology sector
by Yun Dai, Gin Chong
Abstract: Despite its substantial contributions to a country’s gross domestic products (GDP), there is no known systematic study on the governance and how performance of the biotechnology sector is being measured. A questionnaire survey was conducted with the firms registered in the UK Biotechnology Handbook. With a response rate of 38% and rigorous analysis, the results show that when measuring performance, managers use both the financial and non-financial indicators, but emphasize more on the financial indicators. The financial indicators include turnover, profit margins, cash reserve and liquidity, while the non-financial indicators are meeting the customers’ needs and expectations. More specifically, the managers place attentions to the reporting of environmental issues of their firms, reflecting their readiness to excel in social corporate responsibilities. The results have implications to the biotechnology sector and regulators for public policy.
Keywords: performance measurements, governance, financial and non-financial indicators, and biotechnology firms
- Non Linear Cellular Automata with Text Clustering In Protein Coding Region Identification
by KIRAN SREE POKKULURI
Abstract: Genes carry the instructions for making proteins that are found in a cell as a specific sequence of nucleotides that are found in DNA molecules. But, the regions of these genes that code for proteins may occupy only a small region of the sequence. Identifying the coding regions play a vital role in understanding these genes This paper presents a new text clustering algorithm based on Non Linear Cellular Automata Based Local Search and K-Means (NLSKM) for identifying these protein coding regions and explains the characteristic of this algorithm theoretically. Experimental results confirm the scalability of the proposed NLSKM based classifier to any datasets irrespective of the number of classes, tuples and attributes. We note an increase in accuracy of more than 5.2%, over any existing standard algorithms for addressing this problem. This was the first algorithm to identify protein coding regions in mixed and non overlapping exon-inton boundary DNA sequences also
Keywords: non linear cellular automata, k means algorithm, dna sequence
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