Biology and Data Interpretation Techniques Concepts

Sindhu Pingili

Biology and Data Interpretation Techniques Concepts

Sindhu Pingili^*
Vaageswari Institute of Pharmaceutical Sciences, Thimapur, karminagar, Telgana, India

Corresponding Author: Sindhu Pingili, E-mail: sindhureddy.pingili@gmail.com

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INTRODUCTION

The data analysis is a key factor in the biological research, where every comparison could lead to a new finding. Arranging specific data in an organized ways will be the starting step and it is known as data preparation. This data should be explained and a proper description should be provided. Every data has evaluation criteria and the interpretation would be against this evaluating data and it will be the final step. Here, the quantitative data will be statistically analysed. There are many computational software’s available like SPSS or ANOVA for analysing data. When it is quantitative data, the statistical analysis would help in measuring the degree of change in whole process and to predict the consistency of data. When compared quantitative data, qualitative data interpretation is difficult, as it require similar consensus in patterns [1]. In case of quantitative methodology, the testing theory is through the testing of a hypothesis. In qualitative research, you are either exploring the application of a theory or model in a different context or are hoping for a theory or a model to emerge from the data [2].

As stated by Marshall and Rossman (1990) "Data analysis is the process of bringing order, structure and meaning to the mass of collected data. It is a messy, ambiguous, timeconsuming, creative, and fascinating process. It does not proceed in a linear fashion; it is not neat. Qualitative data analysis is a search for general statements about relationships among categories of data" [2,3].

BIOLOGICAL DATA MINING

In process of understanding different and difficult biological processes, biologists are working hard in analysing, taking them to next levels of biological and disease causing pathways. Genomic and protein sequences, DNA microarrays, protein interactions, biomedical images has resulted in production of bulk data, leading to interpret disease causing pathways and electronic health records.

This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing computeintensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. The fundamentals of state-of-the-art data mining techniques used to solve challenges in data analysis problems. Real application would be how biologists and clinical scientists can employ data mining. These studies finally results in making meaningful observations and discoveries. Sequence Analysis, Biological Network Mining, Classification, Trend Analysis and 3D Medical Images, Text Mining and Its Biomedical Applications are important applications of data mining.

COMPUTATIONAL MODELLING

In biological research, the support of computers and algorithms has a major role. When the quantity of data is high this support would be important, whiles it is difficult to analyze by humans. From the relation between two elements to its reaction levels in environment, the biology consists of high levels datasets to be analysed. Usually, models are replica of real process to make research easy and convenient [3-17].

Mathematical models are computerised as an attempt to simplify interpretations, but found to be more thoughtful idea, while many complex biological models were started to solve in an easier way. Thus, the variable data was presumably solved to obtain qualitative and quantitative results. The data interpretation became very easy and thus these models started developing in different dimensions, such as 1) Models Provide a Coherent Framework for Interpreting Data 2) Models Highlight Basic Concepts of Wide Applicability 3) Models Uncover New Phenomena or Concepts to Explore 4) Models Identify Key Factors or Components of a System 5) Models Can Link Levels of Detail (Individual to Population) etc. [17-25].

CONCLUSION

Genetic engineering, microbiology, protein synthesis, protein modelling, enzymology, enzyme kinetics etc. are complicated and the computational models play a major role. Research such as oncology and therapies, imaging is advancement. But, there are number of obstacles and difficulties to be addressed to make a successful scientific model, finally used by a life science expert. Thus, the development of these models would be required and more of this kind could make research get in to next level [3,6].