Using rules to analyse bio- medical data: A comparison between C4. PCL. For easy comprehensibility, rules are preferrable to non- linear kernel functions in the analysis of bio- medical data. In this paper, we describe two rule induction approaches - C4. PCL classifier - for discovering rules from both traditional clinical data and recent gene expression or proteomic profiling data. Download Matlab Toolbox Symbolic Dysfunction DiseaseC4. 5 is a widely used method, but it has two weaknesses, the single coverage constraint and the fragmentation problem, that affect its accuracy. PCL is a new rule- based classifier that overcomes these two weaknesses of decision trees by using many significant rules. We present a thorough comparison to show that our PCL method is much more accurate than C4. Bagging and Boosting in general. Emerging Pattern- Based Rules Characterizing Subtypes of Leukemia' in Dong, G. View/Download from: UTS OPUS or Publisher's site. NALi, J. 2. 01. 3, 'Protein Binding Interfaces and Their Binding Hot Spot Prediction: A Survey' in Bioinformatics for Diagnosis, Prognosis and Treatment of Complex Diseases, Springer, German, pp. View/Download from: UTS OPUS or Publisher's site. In living organisms, genes are the blueprints or library, specifying instructions for building proteins. Proteins constitute the bulk of cells. Proteins mutual binding and interactions play a vital role in numerous functions and activities, such as signal transduction, enzymatic reactions, immunoreactions and inter- cellular communications. This survey provides basic knowledge of proteins and protein binding.
First, we describe proteins fundamental elements, structures and functions. Section 5. 7 concludes this survey with a summary. Jinyan Li is a Professor of Data Sciene at the Advanced Analytics Institute and a core member at the Centre for Health Technologies, Faculty of Engineering and IT. The Library regularly arranges trials to new resources. Publishers are usually willing to provide trial access to allow us to use and evaluate a resource before. Feng, M., Li, J., Dong, G. Maintenance of frequent patterns: A survey' in Post- Mining of Association Rules: Techniques for Effective Knowledge Extraction, pp. View/Download from: Publisher's site. This chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the field of frequent pattern mining. The frequent pattern maintenance problem is summarized with a study on how the space of frequent patterns evolves in response to data updates. This chapter focuses on incremental and decremental maintenance. Four major types of maintenance algorithms are studied: Apriori- based, partition- based, prefix- tree- based, and concise- representation- based algorithms. The authors study the advantages and limitations of these algorithms from both the theoretical and experimental perspectives. Possible solutions to certain limitations are also proposed. In addition, some potential research opportunities and emerging trends in frequent pattern maintenance are also discussed. Mining conditional contrast patterns' in Post- Mining of Association Rules: Techniques for Effective Knowledge Extraction, pp. View/Download from: Publisher's site. This chapter considers the problem of . Roughly speaking, conditional contrasts capture situations where a small change in patterns is associated with a big change in the matching data of the patterns. More precisely, a conditional contrast is a triple (B, F1, F2) of three patterns; B is the condition/context pattern of the conditional contrast, and F 1 and F 2 are the contrasting factors of the conditional contrast. Such a conditional contrast is of interest if the difference between F 1 and F 2 as itemsets is relatively small, and the difference between the corresponding matching dataset of BF 1 and that of BF2 is relatively large. It offers insights on . Conditional contrast mining is related to frequent pattern mining and analysis in general, and to the mining and analysis of closed pattern and minimal generators in particular. It can also be viewed as a new direction for the analysis (and mining) of frequent patterns. After formalizing the concepts of conditional contrast, the chapter will provide some theoretical results on conditional contrast mining. ![]() These results (i) relate conditional contrasts with closed patterns and their minimal generators, (ii) provide a concise representation for conditional contrasts, and (iii) establish a so- called dominance- beam property. An efficient algorithm will be proposed based on these results, and experiment results will be reported. Related works will also be discussed. Using Constrained Information Entropy to Detect Rare Adverse Drug Reactions from Medical Forums', 3. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2. IEEE 3. 8th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), IEEE, IEEE, pp. View/Download from: Publisher's site. Adverse drug reactions (ADRs) detection is critical to avoid malpractices yet challenging due to its uncertainty in pre- marketing review and the underreporting in post- marketing surveillance. To conquer this predicament, social media based ADRs detection methods have been proposed recently. However, existing researches are mostly co- occurrence based methods and face several issues, in particularly, leaving out the rare ADRs and unable to distinguish irrelevant ADRs. In this work, we introduce a constrained information entropy (CIE) method to solve these problems. CIE first recognizes the drug- related adverse reactions using a predefined keyword dictionary and then captures high- and low- frequency (rare) ADRs by information entropy. Extensive experiments on medical forums dataset demonstrate that CIE outperforms the state- of- the- art co- occurrence based methods, especially in rare ADRs detection. Xie, J., Wang, M., Zhou, Y. Coordinating discernibility and independence scores of variables in a 2. D space for efficient and accurate feature selection', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. View/Download from: Publisher's site& copy; Springer International Publishing Switzerland 2. Feature selection is to remove redundant and irrelevant features from original ones of exemplars, so that a sparse and representative feature subset can be detected for building a more efficient and accurate classifier. This paper presents a novel definition for the discernibility and independence scores of a feature, and then constructs a two dimensional (2. D) space with the feature's independence as y- axis and discernibility as x- axis to rank features' importance. This new method is named FSDI (Feature Selection based on Discernibility and Independence of a feature). The discernibility score of a feature is to measure the distinguishability of the feature to detect instances from different classes. The independence score is to measure the redundancy of a feature. All features are plotted in the 2. D space according to their discernibility and independence coordinates. The area of the rectangular corresponding to a feature's discernibility and independence in the 2. D space is used as a criterion to rank the importance of the features. Top- k features with much higher importance than the rest ones are selected to form the sparse and representative feature subset for building an efficient and accurate classifier. Experimental results on 5 classical gene expression datasets demonstrate that our proposed FSDI algorithm can select the gene subset efficiently and has the best performance in classification. Our method provides a good solution to the bottleneck issues related to the high time complexity of the existing gene subset selection algorithms. Chen, Q., Lan, C., Li, J., Chen, B., Wang, L. Depth- first search encoding of RNA substructures', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. View/Download from: Publisher's site& copy; Springer International Publishing Switzerland 2. RNA structural motifs are important in RNA folding process. Traditional index- based and shape- based schemas are useful in modeling RNA secondary structures but ignore the structural discrepancy of individual RNA family member. Further, the in- depth analysis of underlying substructure pattern is underdeveloped owing to varied and unnormalized substructures. This prevents us from understanding RNAs functions. This article proposes a DFS (depth- first search) encoding for RNA substructures. The results show that our methods are useful in modelling complex RNA secondary structures. Ghosh, S., Zheng, Y., Lammers, T., Chen, Y. Y., Fitzmaurice, C., Johnston, S. Deriving public sector workforce insights: A case study using Australian public sector employment profiles', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. View/Download from: Publisher's site& copy; Springer International Publishing AG 2. Effective approaches for measurement of human capital in public sector and government agencies is essential for robust workforce planning against changing economic conditions. To this purpose, adopting innovative hypotheses driven workforce data analysis can help discover hidden patterns and trends about the workforce. These trends are useful for decision making and support the development of policies to reach desired employment outcomes. In this study, the data challenges and approaches to a real life workforce analytics scenario are described. Statistical results from numerous workforce data experiments are combined to derive three hypotheses that are useful to public sector organisations for human resources management and decision making. Liu, Q., Li, J., Wong, L. Efficient mining of pan- correlation patterns from time course data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. View/Download from: Publisher's site& copy; Springer International Publishing AG 2. Georges. Pompidou 1. MARSEILLE. Reservation : 0.
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