Bioinformatics software plays a very important role in making critical decisions within many areas including medicine and health care. However, most of the research is directed towards developing tools, and little time and effort is spent on testing the software to assure its quality. In testing, a test oracle is used to determine whether a test is passed or failed during testing, and unfortunately, for much of bioinformatics software, the exact expected outcomes are not well defined. Thus, the main challenge associated with conducting systematic testing on bioinformatics software is the oracle problem. Metamorphic testing (MT) is a technique used to test programs that face the oracle problem. MT uses metamorphic relations (MRs) to determine whether a test has passed or failed and specifies how the output should change according to a specific change made to the input. In this work, we use MT to test LingPipe, a tool for processing text using computational linguistics, often used in bioinformatics for bio-entity recognition from biomedical literature. First, we identify a set of MRs for testing any bio-entity recognition program. Then we develop a set of test cases that can be used to test LingPipe's bio-entity recognition functionality using these MRs. To evaluate the effectiveness of this testing process, we automatically generate a set of faulty versions of LingPipe. According to our analysis of the experimental results, we observe that our MRs can detect the majority of these faulty versions, which shows the utility of this testing technique for quality assurance of bioinformatics software.
Over the past years, there has been an enormous growth in the quantity and variety of biological data. With this growth, researchers have developed a lot of programs in bioinformatics. But, most of time and effort is spent on developing complex statistical methods, while there is a lack of effort in systematically testing these programs that is essential for the quality assurance @cite_8 .
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The field of bioinformatics shows a tremendous growth at the crossroads of biology, medicine, and informatics. Applying data mining techniques in the medical field is a very challenging undertaking due to the idiosyncrasies of the medical profession. Using conventional methods, it is very difficult to determine the exact number of microorganisms in a microscopic picture. In this paper, the emphasis is on the automatic detection of microbes using automated tools and extraction of bacterial clusters through statistical and neural network approaches. Also, Multiscan approaches with freeman chain code and contour detection for the bacterial patterns in the images have been presented. Experimental results shows that the bacterial cluster patterns obtained through neural network approach are better than the statistical approach. Background Recent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguide downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs. However, due to the complexity of many bioinformatics programs, it is often difficult to verify the correctness of the test outputs. Therefore our ability to perform systematic software testing is greatly hindered. The first comprehensive overview of preprocessing, mining, and postprocessing of biological dataMolecular biology is undergoing exponential growth in both the volume and complexity of biological dataand knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)providing in-depth fundamental and technical field information on the most important topics encountered.Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processingalso known as data miningand data postprocessing) and analyzes both verification systems and discovery systems.BIOLOGICAL DATA PREPROCESSINGPart A: Biological Data ManagementPart B: Biological Data ModelingPart C: Biological Feature ExtractionPart D Biological Feature SelectionBIOLOGICAL DATA MININGPart E: Regression Analysis of Biological DataPart F Biological Data ClusteringPart G: Biological Data ClassificationPart H: Association Rules Learning from Biological DataPart I: Text Mining and Application to Biological DataPart J: High-Performance Computing for Biological Data MiningCombining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics. The biological sciences are undergoing an explosion in the amount of available data. New data analysis methods are needed to deal with the data. We present work using KDD to analyse data from mutant phenotype growth experiments with the yeast S. cerevisiae to predict novel gene functions. The analysis of the data presented a number of challenges: multi-class labels, a large number of sparsely populated classes, the need to learn a set of accurate rules (not a complete classification), and a very large amount of missing values. We developed resampling strategies and modified the algorithm C4.5 to deal with these problems. Rules were learnt which are accurate and biologically meaningful. The rules predict function of 83 putative genes of currently unknown function at an estimated accuracy of ≥ 80 .