A structured view on pattern mining-based biclustering

Henriques, Rui; Antunes, Cláudia; Madeira, Sara C.

doi:10.1016/j.patcog.2015.06.018

Cited by 60 publications

(66 citation statements)

References 117 publications

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“…In stochastic approaches, a set of parameters that describe the biclustering solution are learned by optimizing a merit (likelihood) function. The pursued homogeneity determines the coherence, quality and structure of a biclustering solution [13]. The coherence of a bicluster is determined by the observed form of correlation among its elements (coherence assumption) and by the allowed value deviations from perfect correlation (coherence strength).…”

Section: Biclusteringmentioning

confidence: 99%

“…Hundreds of biclustering algorithms were proposed in the last decade, as shown by recent surveys [10,26]. In recent years, a clearer understanding of the synergies between biclustering and pattern mining paved the rise for a new class of algorithms, referred to as pattern-based biclustering algorithms [13]. Pattern-based biclustering algorithms are inherently prepared to efficiently find exhaustive solutions of biclusters and offer the unprecedented possibility to affect their structure, coherency and quality [12,14].…”

Section: Biclustering Digital Collections Following the Taxonomy Of mentioning

confidence: 99%

“…Pattern-based biclustering algorithms are inherently prepared to efficiently find exhaustive solutions of biclusters and offer the unprecedented possibility to affect their structure, coherency and quality [12,14]. This behavior explains why this class of biclustering algorithms are receiving an increasing attention in recent years [13,18]. BicPAMS [14] consistently combines such state-of-the-art contributions on pattern-based biclustering.…”

Section: Biclustering Digital Collections Following the Taxonomy Of mentioning

confidence: 99%

“…Depending on the goal, one or more coherence assumptions (Definitions 1 and 2) can be pursued [13,18]. The classic constant coherence can be placed to find groups of documents and topics, where each document has a similar probability to be described by a specific topic.…”

Section: On Whymentioning

confidence: 99%

“…Despite its well-recognized relevance to derive ontologies for content categorization, FCA is hampered by major drawbacks. First, it imposes the selection of binarization thresholds to decide whether a topic is represented in a given document, making it vulnerable to subjective choices and to the item-boundaries problem [13]. As a result, FCA is unable to retain concepts sensitive to the varying predominance of topics in a given document, neglecting the rich nature of vector space models.…”

Section: Introductionmentioning

confidence: 99%

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Moving from Formal Towards Coherent Concept Analysis: Why, When and How

Kovalchuk

Proença

Borbinha

et al. 2020

Lecture Notes in Computer Science

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Formal concept analysis has been largely applied to explore taxonomic relationships and derive ontologies from text collections. Despite its recognized relevance, it generally misses relevant concept associations and suffers from the need to learn from Boolean space models. Biclustering, the discovery of coherent concept associations (subsets of documents correlated on subsets of terms and topics), is here suggested to address the aforementioned problems. This work proposes a structured view on why, when and how to apply biclustering for concept analysis, a subject remaining largely unexplored up to date. Gathered results from a large text collection confirm the relevance of biclustering to find lesstrivial, yet actionable and statistically significant concept associations.

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Section: Biclusteringmentioning

confidence: 99%

Section: Biclustering Digital Collections Following the Taxonomy Of mentioning

confidence: 99%

Section: Biclustering Digital Collections Following the Taxonomy Of mentioning

confidence: 99%

Section: On Whymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Moving from Formal Towards Coherent Concept Analysis: Why, When and How

Kovalchuk

Proença

Borbinha

et al. 2020

Lecture Notes in Computer Science

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Pattern‐centric transformation of omics data grounded on discriminative gene associations aids predictive tasks in TCGA while ensuring interpretability

Patrício,

Costa,

Henriques

2024

Biotech & Bioengineering

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The increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of molecular features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom‐built algorithms that can extract relevant information from these sources of data. We propose a transformation that moves data centered on molecules (e.g., transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant co‐expression patterns. To this end, the proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and increase predictive performance of various classifiers across multiple omics layers. Results suggest that omics data transformations from gene‐centric to pattern‐centric data supports both prediction tasks and human interpretation, notably contributing to precision medicine applications.

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