Frequent sub-structure-based approaches for classifying chemical compounds

By Mukund Deshpande, Michihiro Kuramochi, Nikil Wale, George Karypis. 2005

294 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Spectroscopy

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Citations in the corpus

Text Classification using Graph Mining-based Feature Extraction (2010, background): “It has been established that structural information plays an impo rtant role in classification accuracy [14].”
Frequent pattern mining: current status and future directions (2007, methodology): “In graph classiﬁcation, Deshpande et al. (2003) used frequent subgraphs as features and built classiﬁcation models based on them.”
Comparison of descriptor spaces for chemical compound retrieval and classification (2006, methodology): “However, in recent years, research in the data mining community has generated new classes of descriptors based on frequently occurring substructures [17] and selected cycles and trees [23] that have been shown to achieve promising results.”
Sentiment Classification using Automatically Extracted Subgraph Features (2010, methodology): “With several implementations available 3 , gSpan has been commonly used for mining frequent subgraph patterns (Kudo et al., 2004; Deshpande et al., 2005).”
Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases (2013, methodology): “Subgraph isomorphism search is actively used in many applications such as protein interaction analysis [21], graph classification [4], computer-aided design of electronic circuits [10], scene analysis [1], and chemical compound search [17].”
Discriminative Frequent Pattern Analysis for Effective Classification (2007, methodology): “The idea of frequent pattern-based classification has been exploited by previous studies in different domains, including: (1) associative classification [14, 13, 25, 6, 19], where association rules are generated and analyzed for classification; and (2) graph classification [7], text categorization [”
Frequent substructure-based approaches for classifying chemical compounds (2003, background): “A short version of this paper appeared in ICDM 2003 [26].”
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting (2020, background): “This idea has since been successfully applied to study the higher organisation of transportation and neuronal networks [53], temporal networks [54], and molecules [55, 56, 57].”
GADDI: distance index based subgraph matching in biological networks (2009, unknown): “, network intrusion detection [33, 26], the semantic web [2], behavioral modeling [43, 34], VLSI reverse engineering [49], link analysis [22, 25, 36], and chemical compound classification [9, 30, 15, 10].”
A survey of frequent subgraph mining algorithms (2012, background): “, 2008) Graph classification (Kudo et al., 2004; Huan et al., 2004; Deshpande et al., 2005) Graph clustering (Flake et al.”
A statistical approach to rule learning (2006, background): “Therefore, many approaches restrict the set of subgraph features to frequently occurring, frequent closed, or class-correlated subgraphs (Deshpande et al., 2005; Bringmann et al., 2006).”
GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph (2014, background): “Mining frequent subgraphs is a central and well studied problem in graphs, and plays a critical role in many data mining tasks that include graph classification [9], modeling of user profiles [11], graph clustering [15], database design [10] and index selection [31].”
Cyclic pattern kernels for predictive graph mining (2004, background): “Many researchers have consequently resorted to represent each graph by its frequent subgraphs (see, e.g., [4, 9, 20]) and then to apply a kernel to the pattern sets corresponding to these frequent subgraphs.”
Finding Frequent Patterns in a Large Sparse Graph* (2004, unknown): “…al., 2004), VLSI reverse engineering (Yoshida and Motoda, 1995), link analysis (Jensen and Goldberg, 1998; Kleinberg et al., 1999; Kleinberg, 1999; Palmer et al., 2002), and chemical compound classification (Dehaspe et al., 1998; Kramer et al., 2001; Gonzalez et al., 2001; Deshpande et al., 2003).”