Mining the biomedical literature using semantic analysis and natural language processing techniques

Feldman, Ronen; Regev, Yizhar; Hurvitz, Eyal; Finkelstein-Landau, Michal

doi:10.1016/s1478-5382(03)02330-8

Cited by 36 publications

(24 citation statements)

References 24 publications

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“…Prior to the great usages that this technology brings into many application areas such as biomedical and DNA analysis, retail industry and marketing, telecommunications, web mining and recently has also been an interesting area of research in educational domain. This information overload also exists in the biomedical field, (Feldman et al, 2003) where scientific publications and other forms of textbased data are produced at an unprecedented rate.…”

Section: Educational Institution Quality Assessment Modelmentioning

confidence: 99%

Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

Kumar¹

2011

Journal of Computer Science

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Problem statement:The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. Validation criteria focus on the quality metrics of the institution features for cluster formation and handle efficiently the arbitrary shaped clusters. Approach: The proposed work presented a fuzzy k-means cluster algorithm in the formation of student, faculty and infrastructural clusters based on the performance, skill set and facilitation availability respectively. The knowledge hidden among the educational data set is extracted through Fuzzy k-means cluster an unsupervised learning depends on certain initiation values to define the subgroups present in the data set. Results: Based on the features of the dataset and input parameters cluster formation vary, which motivates the clarification of cluster validity. The results of quality indexed fuzzy k-means shows better cluster validation compared to that of traditional k-family algorithm. Conclusion: The experimental results of cluster validation scheme confirm the reliability of validity index showing that it performs better than other k-family clusters.

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Section: Educational Institution Quality Assessment Modelmentioning

confidence: 99%

Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

Kumar¹

2011

Journal of Computer Science

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“…It can be best explained as the process of extracting useful knowledge and information including, patterns, associations, changes, anomalies and significant structures from a great deal of data stored in databases, data warehouses, or other information repositories [4,5,6]. Prior to the great usages that this technology brings into many application areas such as biomedical and DNA analysis [5,7,8], retail industry and marketing [5,9], telecommunications [5,10], web Mining [11], computer auditing [12], banking [5], fraud detection [10], financial industry [5] and medicine [13,14], it recently has also been an interesting area of research in educational domain [15].…”

Section: Decision Tree In Institution Quality Assessmentmentioning

confidence: 99%

Efficient Quality Assessment Technique with Integrated Cluster Validation and Decision Trees

Kumar¹,

Ramaswami²

2011

IJCA

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Clustering becomes a key technique in analyzing quality assessment in most of the recent research works. The partitioned clustering techniques used in previous work utilize attributes of objects to form cluster. The cluster numbers were initialized, which reduces cluster quality in terms of cluster object aggregation and appropriation. The work presented an efficient quality assessment technique comprising of two parts i.e., fuzzy k-means cluster validation scheme and decision tree model. The Fuzzy kmeans cluster validation scheme improves recall and precision measure of automatically labeling cluster objects. The decision tree model evaluates labeled cluster object and decides on the appropriation of attributes to its cluster validity index. The cluster quality index is measured in terms of number of clusters, number of objects in each cluster, cluster object cohesiveness, precision and recall values. Cluster validates focus on quality metrics of the institution data set features experimented with real and synthetic data sets. The results of quality indexed fuzzy kmeans shows better cluster validation compared to that of traditional k-family algorithm. The experimental results of cluster validation scheme and decision tree confirm the reliability of quality validity index which performs better than other traditional k-family clusters.

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“…A lot of genomic information is non-homogeneous, redundant and badly structured, since it often derives from experiments that provide only partial data and is stored in proprietary formats. Thus, biologists must query many databases and compare the partial data extracted from different sources to improve the quality of the needed data [6]. The retrieved information must be filtered to eliminate redundancy and integrate incomplete data.…”

Section: The Genomic Information Retrieval Problemmentioning

confidence: 99%

“…The information redundancy problem is partially addressed by Ponte and Croft [15], and by Feldman et al [6] who focus on retrieving information in the form of reports or papers but do not adapt to other kinds of data. Ponte and Croft propose a statistical model to estimate the probability that a document could be relevant to the query; while Feldman et al use data and text mining mechanisms to automatically select relevant literature [6].…”

Section: The Genomic Information Retrieval Problemmentioning

confidence: 99%