Topological data analysis (TDA) is a powerful method for reducing data dimensionality, mining underlying data relationships, and intuitively representing the data structure. The Mapper algorithm is one such tool that projects high-dimensional data to 1-dimensional space by using a filter function that is subsequently used to reconstruct the data topology relationships. However, domain context information and prior knowledge have not been considered in current TDA modeling frameworks. Here, we report the development and evaluation of a semi-supervised topological analysis (STA) framework that incorporates discrete or continuously labeled data points and selects the most relevant filter functions accordingly. We validate the proposed STA framework with simulation data and then apply it to samples from Genotype-Tissue Expression data and ovarian cancer transcriptome datasets. The graphs generated by STA for these 2 datasets, based on gene expression profiles, are consistent with prior knowledge, thereby supporting the effectiveness of the proposed framework.

Gradient boosting method has been successfully used for single target prediction problems. In real world applications, however, problems involving the prediction of multiple target attributes are often of interest. In this paper, a multi-target boosting method for regression problems, named as MTBR, is proposed. Although MTBR builds one model for each target attribute separately, all the target attributes are utilized when building each model. In each boosting iteration, the base learner, the regression tree in particular, is learned by selecting the best models from all the target attributes. We also introduce a novel knowledge transfer approach. That is, the tree structure learned from one target attribute, representing a way to partition the feature space, is used to predict another target attribute. Experiments with six data sets compare MTBR to other ensemble regression methods, and prove the effectiveness of MTBR in leveraging the knowledge of multiple target attributes and improving the model accuracy.

MOTIVATION: Matched case-control analysis is widely used in biomedical studies to identify exposure variables associated with health conditions. The matching is used to improve the efficiency. Existing variable selection methods for matched case-control studies are challenged in high-dimensional settings where interactions among variables are also important. We describe a quite different method for high-dimensional matched case-control data, based on the potential outcome model, which is not only flexible regarding the number of matching and exposure variables but also able to detect interaction effects. RESULTS: We present Matched Forest (MF), an algorithm for variable selection in matched case-control data. The method preserves the case and control values in each instance but transforms the matched case-control data with added counterfactuals. A modified variable importance score from a supervised learner is used to detect important variables. The method is conceptually simple and can be applied with widely available software tools. Simulation studies show the effectiveness of MF in identifying important variables. MF is also applied to data from the biomedical domain and its performance is compared with alternative approaches. AVAILABILITY AND IMPLEMENTATION: R code for implementing MF is available at https://github.com/NooshinSh/Matched_Forest. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clustering is an important aspect of data mining, while clustering high-dimensional mixed-attribute data in a scalable fashion still remains a challenging problem. In this paper, we propose a tree-ensemble clustering algorithm for static datasets, CRAFTER, to tackle this problem. CRAFTER is able to handle categorical and numeric attributes simultaneously, and scales well with the dimensionality and the size of datasets. CRAFTER leverages the advantages of a tree-ensemble to handle mixed attributes and high dimensionality. The concept of the class probability estimates is utilized to identify the representative data points for clustering. Through a series of experiments on both synthetic and real datasets, we have demonstrated that CRAFTER is superior than Random Forest Clustering (RFC), an existing tree-based clustering method, in terms of both the clustering quality and the computational cost.

In this paper, we propose a new end-to-end deep neural network model for time-series classification (TSC) with emphasis on both the accuracy and the interpretation. The proposed model contains a convolutional network component to extract high-level features and a recurrent network component to enhance the modeling of the temporal characteristics of TS data. In addition, a feedforward fully connected network with the sparse group lasso (SGL) regularization is used to generate the final classification. The proposed architecture not only achieves satisfying classification accuracy, but also obtains good interpretability through the SGL regularization. All these networks are connected and jointly trained in an end-to-end framework, and it can be generally applied to TSC tasks across different domains without the efforts of feature engineering. Our experiments in various TS data sets show that the proposed model outperforms the traditional convolutional neural network model for the classification accuracy, and also demonstrate how the SGL contributes to a better model interpretation.