THD-Tricluster: A robust triclustering technique and its application in condition specific change analysis in HIV-1 progression data

Kakati, Tulika; Ahmed, Hasin Afzal; Bhattacharyya, Dhruba K.; Kalita, Jugal

doi:10.1016/j.compbiolchem.2018.05.007

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…How actionable are the triclustering results? For instance, in biological domains [3,5,13], functional annotations and gene ontologies are used to extract meaning from the sets of genes found and to understand why they are correlated.…”

Section: Single Solution Evaluationmentioning

confidence: 99%

“…Three-way data are also capable of capturing behaviors and trends common to several individuals, being able to represent how communities function and respond together. Notable examples can be found in medical data analysis, where temporal patient data (patient-feature-time) [5] is used to describe patient profiles and disease progression patterns during patient follow-up. Alternatively, in social data [6], individuals' preferences (individual-feature-time) and interactions (individual-individual-time) are collected to improve the contents provided, recommendations, to serve communities of users sharing similar tastes.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, they are then used to compare the new algorithm with the state-of-the-art, in turn proposed and evaluated using other data, compromising the validity of some comparisons, and making them unfair, even if experimentally correct. Several authors [2,3,5,6] generated specific data to test their algorithms.…”

Section: Introductionmentioning

confidence: 99%

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G-Tric: generating three-way synthetic datasets with triclustering solutions

2021

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Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

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Section: Single Solution Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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G-Tric: generating three-way synthetic datasets with triclustering solutions

2021

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“…In clinical domains, triclustering has been successfully applied for different ends: health record data analysis, where triclusters can identify groups of patients with correlated clinical features along time; neuroimaging data analysis in which triclusters correspond to enhanced hemodynamic or electrophysiological responses and connectivity patterns between brain regions; multi-omics, where triclusters capture putative regulatory patterns within omic series data; and multivariate physiological signal data analysis, where triclusters capture coherent physiological responses for a group of individuals 1 , 3 , 4 . In spite of triclustering relevance for descriptive tasks (knowledge acquisition), its potential in predictive tasks (medical decision) remains considerably untapped 1 .…”

Section: Introductionmentioning

confidence: 99%

Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis

Soares

Henriques

Gromicho

et al. 2023

Sci Rep

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This work proposes a new class of explainable prognostic models for longitudinal data classification using triclusters. A new temporally constrained triclustering algorithm, termed TCtriCluster, is proposed to comprehensively find informative temporal patterns common to a subset of patients in a subset of features (triclusters), and use them as discriminative features within a state-of-the-art classifier with guarantees of interpretability. The proposed approach further enhances prediction with the potentialities of model explainability by revealing clinically relevant disease progression patterns underlying prognostics, describing features used for classification. The proposed methodology is used in the Amyotrophic Lateral Sclerosis (ALS) Portuguese cohort (N = 1321), providing the first comprehensive assessment of the prognostic limits of five notable clinical endpoints: need for non-invasive ventilation (NIV); need for an auxiliary communication device; need for percutaneous endoscopic gastrostomy (PEG); need for a caregiver; and need for a wheelchair. Triclustering-based predictors outperform state-of-the-art alternatives, being able to predict the need for auxiliary communication device (within 180 days) and the need for PEG (within 90 days) with an AUC above 90%. The approach was validated in clinical practice, supporting healthcare professionals in understanding the link between the highly heterogeneous patterns of ALS disease progression and the prognosis.

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Identifying condition specific key genes from basal-like breast cancer gene expression data

Maind

Raut

2019

Computational Biology and Chemistry

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THD-Tricluster: A robust triclustering technique and its application in condition specific change analysis in HIV-1 progression data

Cited by 12 publications

References 24 publications

G-Tric: generating three-way synthetic datasets with triclustering solutions

G-Tric: generating three-way synthetic datasets with triclustering solutions

Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis

Identifying condition specific key genes from basal-like breast cancer gene expression data

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