Conceptual Biology Research Supporting Platform: Current Design and Future Directions

Xie, Ying; Katukuri, Jayasimha; Raghavan, Vijay V.; Presti, Tony

doi:10.1007/978-3-540-78534-7_13

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Yan et al demonstrated the effectiveness of a granular information retrieval system by implementing and evaluating a prototype system (Yan et al 2011). Xie et al applied granular computing for a conceptual biology research supporting platform (Xie et al 2008).…”

Section: Review Of Granular Computingmentioning

confidence: 99%

Granular meta-clustering based on hierarchical, network, and temporal connections

Lingras

Haider

Triff³

2016

Granul. Comput.

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In granular computing, each object is represented as an information granule and an information granule can be connected to other granules through semantic relationships. These connections can lead to a granular hierarchy or a network. Data mining of one set of objects may not be able to capture information contained in granular connections. This paper describes a concept of meta-clustering that clusters a set of granules using clustering information from another or the same set of networked granules. Cluster membership of one granule can affect another granule's cluster membership, resulting in a recursive meta-clustering process. We illustrate the usefulness of such meta-clustering for a granular hierarchy consisting of sets of businesses and reviewers, a set of networked granules representing mobile phone users, and trading patterns of financial instruments that are linked to each other through a temporal dimension. Keywords Meta-clustering Á Granular computing Á Iterative clustering Á k-means Á Fuzzy c-means Á Social networks Á Time series Á Financial markets Á Web mining

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Section: Review Of Granular Computingmentioning

confidence: 99%

Granular meta-clustering based on hierarchical, network, and temporal connections

Lingras

Haider

Triff³

2016

Granul. Comput.

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“…Swanson's hypothesizing model, the so called Swanson's ABC model, can be simply described as A relates to B, B relates to C, therefore A may relate to C, [ 2 ]. Ever since Swanson's discovery, a lot of research works have been carried out with the aim of automating and refining Swanson's ABC model [ 1 , 3 - 8 ]. Nevertheless, most of these reported approaches are based on analyzing the retrieval result set for one or two initial topics provided as query by a user, instead of being able to scale up to the whole set of literature database for the purpose of discovering real, novel and cross-silo biomedical hypotheses.…”

Section: Introductionmentioning

confidence: 99%

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

et al. 2012

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Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper.

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Supervised Link Discovery on Large-Scale Biomedical Concept Networks

Katukuri

Xiey

Raghavan

et al. 2011

2011 IEEE International Conference on Bioinformatics and Biomedicine

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Conceptual Biology Research Supporting Platform: Current Design and Future Directions

Cited by 4 publications

References 12 publications

Granular meta-clustering based on hierarchical, network, and temporal connections

Granular meta-clustering based on hierarchical, network, and temporal connections

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

Supervised Link Discovery on Large-Scale Biomedical Concept Networks

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