Sequential pattern mining to discover relations between genes and rare diseases

Béchet, Nicolas; Cellier, Peggy; Charnois, Thierry; Crémilleux, Bruno; Jaulent, Marie-Christine

doi:10.1109/cbms.2012.6266367

Cited by 11 publications

(21 citation statements)

References 13 publications

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“…This section reports experiments on several real-life datasets [5,3] of large size having varied characteristics and representing different application domains (see Tab. 2).…”

Section: Methodsmentioning

confidence: 99%

“…To illustrate the flexibility of our approach, we selected the PubMed dataset and stated additional constraints such as minimum frequency, minimum size, and other useful constraints expressing some linguistic knowledge as membership. The goal is to extract sequential patterns which convey linguistic regularities (e.g., gene -rare disease relationships) [3]. The size constraint allows to forbid patterns that are too small w.r.t.…”

Section: Methodsmentioning

confidence: 99%

“…For example, we state that extracted patterns must contain at least the two items GENE and DISEASE. We used the gap[0, 9] constraint, which is the best setting found in [3]. As no specialized method exists for this combination of constraints, we thus compare GAP-SEQ with and without additional constraints.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Global Constraint for Mining Sequential Patterns with GAP Constraint

Kemmar¹,

Loudni²,

Lebbah³

et al. 2016

Integration of AI and OR Techniques in Constraint Programming

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Abstract. Sequential pattern mining (SPM) under gap constraint is a challenging task. Many efficient specialized methods have been developed but they are all suffering from a lack of genericity. The Constraint Programming (CP) approaches are not so effective because of the size of their encodings. In [7], we have proposed the global constraint PREFIX-PROJECTION for SPM which remedies to this drawback. However, this global constraint cannot be directly extended to support gap constraint. In this paper, we propose the global constraint GAP-SEQ enabling to handle SPM with or without gap constraint. GAP-SEQ relies on the principle of right pattern extensions. Experiments show that our approach clearly outperforms both CP approaches and the state-of-the-art cSpade method on large datasets.

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“…This section reports experiments on several real-life datasets [5,3] of large size having varied characteristics and representing different application domains (see Tab. 2).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Global Constraint for Mining Sequential Patterns with GAP Constraint

Kemmar¹,

Loudni²,

Lebbah³

et al. 2016

Integration of AI and OR Techniques in Constraint Programming

Self Cite

View full text Add to dashboard Cite

show abstract

“…The goal of this application is to discover relations between genes and diseases from biomedical texts. The details of this application is given in [26]. In this section, we focus on the extraction of sequence patterns, using our CP approach.…”

Section: A Mining Sequence Patterns From Biomedical Textsmentioning

confidence: 99%

“…Qualitative results. Our approach allowed to extract relevant linguistic patterns which are useful to extract gene -RD relationships from biomedical texts (see [26]). In addition, and unlike statistical methods (e.g.…”

Section: A Mining Sequence Patterns From Biomedical Textsmentioning

confidence: 99%

Mining Relevant Sequence Patterns with CP-Based Framework

Kemmar

Ugarte

Loudni

et al. 2014

2014 IEEE 26th International Conference on Tools With Artificial Intelligence

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International audience—Sequential pattern mining under various constraints is a challenging data mining task. The paper provides a generic framework based on constraint programming to discover sequence patterns defined by constraints on local patterns (e.g., gap, regular expressions) or constraints on patterns involving combination of local patterns such as relevant subgroups and top-k patterns. This framework enables the user to mine in a declarative way both kinds of patterns. The solving step is done by exploiting the machinery of Constraint Programming. For complex patterns involving combination of local patterns, we improve the mining step by using dynamic CSP. Finally, we present two case studies in biomedical information extraction and stylistic analysis in linguistics

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PREFIX-PROJECTION Global Constraint for Sequential Pattern Mining

Kemmar

Loudni

Lebbah

et al. 2015

Lecture Notes in Computer Science

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Sequential pattern mining under constraints is a challenging data mining task. Many efficient ad hoc methods have been developed for mining sequential patterns, but they are all suffering from a lack of genericity. Recent works have investigated Constraint Programming (CP) methods, but they are not still effective because of their encoding. In this paper, we propose a global constraint based on the projected databases principle which remedies to this drawback. Experiments show that our approach clearly outperforms CP approaches and competes well with ad hoc methods on large datasets.

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Sequential pattern mining to discover relations between genes and rare diseases

Cited by 11 publications

References 13 publications

A Global Constraint for Mining Sequential Patterns with GAP Constraint

A Global Constraint for Mining Sequential Patterns with GAP Constraint

Mining Relevant Sequence Patterns with CP-Based Framework

PREFIX-PROJECTION Global Constraint for Sequential Pattern Mining

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