• Journal of Life Science
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• v.34 no.2
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• pp.128-137
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• 2024

This review discusses the pivotal role of vascular endothelial growth factors (VEGF) in angiogenesis and lymphangiogenesis, vital processes influencing vascular permeability, endothelial cell recruitment, and the maintenance of tumor-associated blood and lymphatic vessels. VEGF exerts its effects through tyrosine-kinase receptors, VEGFR-1, VEGFR-2, and VEGFR-3. This VEGF-VEGFR system is central not only to cancer but also to diseases arising from abnormal blood vessel and lymphatic vessel formation. In the context of cancer, VEGF and its receptors are essential for the development of tumor-associated vessels, making them attractive targets for therapeutic intervention. Various approaches, such as anti-VEGF antibodies, receptor antagonists, and VEGF receptor function inhibitors, are being explored to interfere with tumor growth. However, the clinical efficacy of anti-angiogenic agents remains uncertain and necessitates further refinement. The article also highlights the physiological role of VEGFs, emphasizing their involvement in endothelial cell functions, survival, and vascular permeability. The identification of five distinct VEGFs in humans (VEGF-A, VEGF-B, VEGF-C, VEGF-D, and PLGF) is discussed, along with the classification of VEGFRs as typical receptor tyrosine kinases with distinct signaling systems. The family includes VEGFR-1 and VEGFR-2, crucial in tumor biology and angiogenesis, and VEGFR-3, specifically involved in lymphangiogenesis. Overall, this review has provided a comprehensive overview of VEGF and VEGFR, detailing their roles in various diseases, including cancer. This is expected to further facilitate the utilization of VEGF and VEGFR as therapeutic targets.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.