Discovery and disentanglement of protein aligned pattern clusters to reveal subtle functional subgroups

Wong

2020

Preprint

Background: Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest in clinical practices. We are looking for interpretability of the diagnostic/prognostic results that will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. When datasets are imbalanced in diagnostic categories, we notice that the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, we need methods that could produce explicit transparent and interpretable results in decision-making, without sacrificing accuracy, even for data with imbalanced groups. Methods: In order to interpret the clinical patterns and conduct diagnostic prediction of patients with high accuracy, we develop a novel method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), and each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even with groups small and rare.Results: Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover fewer patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches. Conclusions: In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discovers all patterns implanted in the data, displays them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel interpretable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.

Section: Analysis I -Discovery and Display Of Explicit Patterns For Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Explanation and Prediction of Clinical Data with Imbalanced Class Distribution based on Pattern Discovery and Disentanglement

Wong

2020

Preprint

“…In order to reveal the functional subgroup characteristics of conserved sequence patterns corresponding to the diverse members of a protein family, we need mathematical transformations to disentangle the intriguing functionality related to conserved functional regions to reveal subgroups not explicitly manifested from the data. To this end, we have developed a novel method, known as Aligned Residue Association Discovery and Disentanglement (ARADD) [ 4 ], based on our previous work Attribute Value Association Discovery and Disentanglement (AVADD) [ 5 ], to discover and then disentangle the statistical representation of the Aligned Residue Associations (ARAs) derived from Aligned Pattern Clusters (APCs) for revealing their subgroups and subgroup characteristics.…”

Section: Introductionmentioning

confidence: 99%

Revealing Subtle Functional Subgroups in Class A Scavenger Receptors by Pattern Discovery and Disentanglement of Aligned Pattern Clusters

Lee²,

Sze-To

et al. 2018

Proteomes

Self Cite

A protein family has similar and diverse functions locally conserved as aligned sequence segments. Further discovering their association patterns could reveal subtle family subgroup characteristics. Since aligned residues associations (ARAs) in Aligned Pattern Clusters (APCs) are complex and intertwined due to entangled function, factors, and variance in the source environment, we have recently developed a novel method: Aligned Residue Association Discovery and Disentanglement (ARADD) to solve this problem. ARADD first obtains from an APC an ARA Frequency Matrix and converts it to an adjusted statistical residual vector space (SRV). It then disentangles the SRV into Principal Components (PCs) and Re-projects their vectors to a SRV to reveal succinct orthogonal AR groups. In this study, we applied ARADD to class A scavenger receptors (SR-A), a subclass of a diverse protein family binding to modified lipoproteins with diverse biological functionalities not explicitly known. Our experimental results demonstrated that ARADD can unveil subtle subgroups in sequence segments with diverse functionality and highly variable sequence lengths. We also demonstrated that the ARAs captured in a Position Weight Matrix or an APC were entangled in biological function and domain location but disentangled by ARADD to reveal different subclasses without knowing their actual occurrence positions.

“…In Analysis I, we applied cPDD on both Synthetic and Thoracic Datasets. First, we compared the discovered patterns obtained in cPDD, Apriori [22] (a typical frequent pattern mining method) and a high-order pattern discovery method for discrete-value data (HOPD) [7] which was our early work closely resembling the PD reported in [11] [10]. Figure 4 and Fig.…”

Section: Analysis I -Discovery and Display Of Explicit Patterns For Ementioning

confidence: 99%

“…cPDD discovers patterns from a small number of AV-Clusters from a small set DS*. Hence, it not only dramatically reduces the number of pattern candidates, but also separates patterns according to their orthogonal AVAs components revealing orthogonal functional characteristic in AV clusters [10] [11] and subgroups in different DS*. Since the AV-clusters are coming from a disentangled source, the set of patterns discovered therein are relatively small with no or least overlapping and "either-or" cases among their AVs, cPDD significantly reduces the variance problem and relates more specific patterns to the targets.…”

Section: Introductionmentioning

confidence: 99%

Explanation and Prediction of Clinical Data with Imbalanced Class Distribution based on Pattern Discovery and Disentanglement

Wong

2020

Preprint

Background Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest and application in clinical practices. First, the interpretability of the diagnostic/prognostic results will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. Furthermore, from the clinical aspect, when the datasets are imbalanced in diagnostic categories, the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, it is desirable to have a method that could produce explicit transparent and interpretable results in decision-making, even for data with imbalanced groups.Methods In order to interpret the clinical patterns and conduct diagnostic prediction of patients, we present our new method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small and succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even for small and rare groups.Results Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover fewer patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches.Conclusions In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discover all patterns implanted in the data, display them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel explainable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.