[HTML][HTML] Learning prognostic models using a mixture of biclustering and triclustering: Predicting the need for non-invasive ventilation in Amyotrophic Lateral Sclerosis
Longitudinal cohort studies to study disease progression generally combine temporal
features produced under periodic assessments (clinical follow-up) with static features …
features produced under periodic assessments (clinical follow-up) with static features …
TriSig: Assessing the statistical significance of triclusters
Tensor data analysis allows researchers to uncover novel patterns and relationships that
cannot be obtained from matrix data alone. The information inferred from the patterns …
cannot be obtained from matrix data alone. The information inferred from the patterns …
[HTML][HTML] TriSig: Evaluating the statistical significance of triclusters
Tensor data analysis allows researchers to uncover novel patterns and relationships that
cannot be obtained from tabular data alone. The information inferred from multi-way patterns …
cannot be obtained from tabular data alone. The information inferred from multi-way patterns …
On the challenges of predicting treatment response in Hodgkin's Lymphoma using transcriptomic data
Background Despite the advancements in multiagent chemotherapy in the past years, up to
10% of Hodgkin's Lymphoma (HL) cases are refractory to treatment and, after remission …
10% of Hodgkin's Lymphoma (HL) cases are refractory to treatment and, after remission …
Multitask deep learning for cost-effective prediction of patient's length of stay and readmission state using multimodal physical activity sensory data
In a hospital, accurate and rapid mortality prediction of Length of Stay (LOS) is essential
since it is one of the essential measures in treating patients with severe diseases. When …
since it is one of the essential measures in treating patients with severe diseases. When …
DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes
Pattern discovery and subspace clustering play a central role in the biological domain,
supporting for instance putative regulatory module discovery from omics data for both …
supporting for instance putative regulatory module discovery from omics data for both …
Iposcore: An interactive web-based platform for postoperative surgical complications analysis and prediction in the oncology domain
H Mochão, D Gonçalves, L Alexandre, C Castro… - Computer Methods and …, 2022 - Elsevier
Background: The performance of traditional risk score systems to predict (post)-operative
outcomes is limited. This weakness reduces confidence in its use to support clinical risk …
outcomes is limited. This weakness reduces confidence in its use to support clinical risk …
Integrating Statistical Significance and Discriminative Power in Pattern Discovery
Pattern discovery plays a central role in both descriptive and predictive tasks across multiple
domains. Actionable patterns must meet rigorous statistical significance criteria and, in the …
domains. Actionable patterns must meet rigorous statistical significance criteria and, in the …
Context-situated visualization of biclusters to aid decisions: going beyond subspaces with parallel coordinates
Pattern discovery and subspace clustering are pervasive tasks across biological,
biotechnological, and biomedical domains. Parallel coordinates plots and heatmaps are …
biotechnological, and biomedical domains. Parallel coordinates plots and heatmaps are …
Scaling pattern mining through non-overlapping variable partitioning
Biclustering algorithms play a central role in the biotechnological and biomedical domains.
The knowledge extracted supports the extraction of putative regulatory modules, essential to …
The knowledge extracted supports the extraction of putative regulatory modules, essential to …