Pattern discovery plays a central role in knowledge acquisition across multiple domains. Actionable patterns must meet rigorous statistical significance criteria and, in the presence of target variables, further uphold discriminative power. This work addresses the underexplored area of guiding pattern discovery by integrating statistical significance and discriminative power criteria into state-of-the-art algorithms while preserving pattern quality. We also address how pattern quality thresholds, imposed by some algorithms, can be rectified to accommodate these additional criteria. To assess the proposed methodology, we select the triclustering task as the guiding pattern discovery case and extend well-known greedy and multi-objective optimization triclustering algorithms, δ -Trimax and TriGen, with various merit functions, such as Mean Squared Residual (MSR), Least Squared Lines (LSL), and Multi Slope Measure (MSL). Results from three case studies show the role of the proposed methodology in discovering patterns with pronouncedly higher discriminative power and statistical significance without quality deterioration, highlighting the relevance of combining these criteria to guide the underlying searches and aid knowledge discovery. Although the proposed methodology is motivated over multivariate time series data, it is straightforwardly extensible to pattern discovery tasks involving multivariate, N-way (N > 3), transactional, and sequential data structures. Availability: The code is freely available at https://github.com/JupitersMight/MOF_Triclustering • Integration of discriminative power views into pattern discovery. • Adaptable framework for pattern-centric knowledge acquisition. • Patterns with guarantees of quality, statistical significance, and discriminative power. • Extended triclustering views for pattern discovery in tensor data. • Mitigation of false positive and false negative risks during pattern discovery.

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 can offer valuable insights into disease progression, bioproduction processes, behavioral responses, weather fluctuations, or social dynamics. However, spurious patterns often hamper this process. This work aims at proposing a statistical frame to assess the probability of patterns in tensor data to deviate from null expectations, extending well-established principles for assessing the statistical significance of patterns in tabular data. A principled discussion on binomial testing to mitigate false positive discoveries is entailed at the light of: variable dependencies, temporal associations and misalignments, and multi-hypothesis correction. Results gathered from the application of triclustering algorithms over distinct real-world case studies in biotechnological domains confer validity to the proposed statistical frame while revealing vulnerabilities of reference triclustering searches. The proposed assessment can be incorporated into existing triclustering algorithms to minimize spurious occurrences, rank patterns, and further prune the search space, reducing their computational complexity.

SUMMARY: Here, we present sbml2hyb, an easy-to-use standalone Python tool that facilitates the conversion of existing mechanistic models of biological systems in Systems Biology Markup Language (SBML) into hybrid semiparametric models that combine mechanistic functions with machine learning (ML). The so-formed hybrid models can be trained and stored back in databases in SBML format. The tool supports a user-friendly export interface with an internal format validator. Two case studies illustrate the use of the sbml2hyb tool. Additionally, we describe HMOD, a new model format designed to support and facilitate hybrid models building. It aggregates the mechanistic model information with the ML information and follows as close as possible the SBML rules. We expect the sbml2hyb tool and HMOD to greatly facilitate the widespread usage of hybrid modeling techniques for biological systems analysis. AVAILABILITY AND IMPLEMENTATION: The Python interface, source code and the example models used for the case studies are accessible at: https://github.com/r-costa/sbml2hyb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumptions which are arguably insufficient to handle the diversity and heterogeneity of clinical and molecular variables within a given dataset. In addition, although an increasing number of symbolic approaches in bioinformatics are able to assign multiple items to values occurring near discretization boundaries for superior robustness, there are no reference principles on how to perform multi-item discretizations. RESULTS: In this study, an unsupervised discretization method, DI2, for variables with arbitrarily skewed distributions is proposed. Statistical tests applied to assess differences in performance confirm that DI2 generally outperforms well-established discretizations methods with statistical significance. Within classification tasks, DI2 displays either competitive or superior levels of predictive accuracy, particularly delineate for classifiers able to accommodate border values. CONCLUSIONS: This work proposes a new unsupervised method for data discretization, DI2, that takes into account the underlying data regularities, the presence of outlier values disrupting expected regularities, as well as the relevance of border values. DI2 is available at https://github.com/JupitersMight/DI2.

Understanding the individualized risks of undertaking surgical procedures is essential to personalize preparatory, intervention and post-care protocols for minimizing post-surgical complications. This knowledge is key in oncology given the nature of interventions, the fragile profile of patients with comorbidities and cytotoxic drug exposure, and the possible cancer recurrence. Despite its relevance, the discovery of discriminative patterns of post-surgical risk is hampered by major challenges: i) the unique physiological and demographic profile of individuals, as well as their differentiated post-surgical care; ii) the high-dimensionality and heterogeneous nature of available biomedical data, combining non-identically distributed risk factors, clinical and molecular variables; iii) the need to generalize tumors have significant histopathological differences and individuals undertake unique surgical procedures; iv) the need to focus on non-trivial patterns of post-surgical risk, while guaranteeing their statistical significance and discriminative power; and v) the lack of interpretability and actionability of current approaches. Biclustering, the discovery of groups of individuals correlated on subsets of variables, has unique properties of interest, being positioned to satisfy the aforementioned challenges. In this context, this work proposes a structured view on why, when and how to apply biclustering to mine discriminative patterns of post-surgical risk with guarantees of usability, a subject remaining unexplored up to date. These patterns offer a comprehensive view on how the patient profile, cancer histopathology and entailed surgical procedures determine: i) post-surgical complications, ii) survival, and iii) hospitalization needs. The gathered results confirm the role of biclustering in comprehensively finding interpretable, actionable and statistically significant patterns of post-surgical risk. The found patterns are already assisting healthcare professionals at IPO-Porto to establish specialized pre-habilitation protocols and bedside care.