Positive and unlabeled (PU) learning is aimed at building a binary classifier to distinguish the target from the background using only the known positive samples, which is an advanced solution for the hyperspectral target detection (HTD) task. However, when PU learning (PUL) meets complex hyperspectral scenarios, there are two main challenges: 1) How to estimate the class prior accurately? The class prior, i.e., the target proportion, is an important prior for PUL to learn the discriminant boundary, but it is difficult to estimate in hyperspectral imagery, due to the interclass spectral similarity and 2) How to remove redundancy and improve the discriminative features of the target? The diagnostic spectral feature extraction is important for the weakly supervised PUL models as it can help with separating the target from the background. In this article, to tackle these challenges, a robust PUL framework with key band selection (PU-KBS) is proposed, which is modeled as an end-to-end and class prior free PUL framework, where the accurate class prior and the most discriminative key band subset are jointly initialized and iteratively updated until reaching the optimal result by evolutionary search. Meanwhile, a deep PUL detector is introduced for guiding the subsequent search direction and discriminative deep feature extraction. The proposed PU-KBS framework was verified using different hyperspectral datasets, where accurate class prior estimation, diagnostic spectral characteristics, and robust detection results could be obtained simultaneously by the PU-KBS framework. Furthermore, the improvement in band selection interpretability and detection performance was proven experimentally.

In this big data era, interpretable machine learning models are strongly demanded for the comprehensive analytics of large-scale multiclass data. Characterizing all features from such data is a key but challenging step to understand the complexity. However, existing feature selection methods do not meet this need. In this paper, to address this problem, we propose a Bayesian multiclass nonnegative matrix factorization (MC-NMF) model with structured sparsity that is able to discover ubiquitous and class-specific features. Variational update rules were derived for efficient decomposition. In order to relieve the need of model selection and stably describe feature patterns, we further propose MC-NMF with stability selection, an ensemble method that collectively detects feature patterns from many runs of MC-NMF using different hyperparameter values and training subsets. We assessed our models on both simulated count data and multitumor ribonucleic acid-seq data. The experiments revealed that our models were able to recover predefined feature patterns from the simulated data and identify biologically meaningful patterns from the pan-cancer data.

BACKGROUND: Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space. RESULTS: We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (Plasmodium chabaudi), systemic acquired resistance in Arabidopsis thaliana, similarities and differences between inner and outer cotyledon in Brassica napus during seed development, and to Brassica napus whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples. CONCLUSIONS: Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.

Microarray technology has been used extensively for high throughput gene expression studies. Many bioinformatics tools are available for analysis of microarray data. In the data mining process, it is important to be goal oriented so that a set of proper tools can be assembled for the targeted knowledge discovery process. In this paper, we tackle this issue by using a microarray dataset from Brassica endosperm together with EST data to validate our process. We were most interested in which genes are highly expressed in Brassica endosperm and their variations and functions over various stages in embryo development. We also performed gene characterization based on gene ontology analysis. Our results indicate that designing a specific data mining workflow that considers both the log ratio and signal intensity enhances knowledge discovery process. Through this approach, we were able to find the regulatory relationship between two most important transcription factors, LEC1 and WR11 in the endosperm of Brassica napus.

MOTIVATION: A measurement of cluster quality is needed to choose potential clusters of genes that contain biologically relevant patterns of gene expression. This is strongly desirable when a large number of gene expression profiles have to be analyzed and proper clusters of genes need to be identified for further analysis, such as the search for meaningful patterns, identification of gene functions or gene response analysis. RESULTS: We propose a new cluster quality method, called stability, by which unsupervised learning of gene expression data can be performed efficiently. The method takes into account a cluster's stability on partition. We evaluate this method and demonstrate its performance using four independent, real gene expression and three simulated datasets. We demonstrate that our method outperforms other techniques listed in the literature. The method has applications in evaluating clustering validity as well as identifying stable clusters. AVAILABILITY: Please contact the first author.