With the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics. This paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications. Topic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers’ ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research.

VennPainter is a program for depicting unique and shared sets of genes lists and generating Venn diagrams, by using the Qt C++ framework. The software produces Classic Venn, Edwards’ Venn and Nested Venn diagrams and allows for eight sets in a graph mode and 31 sets in data processing mode only. In comparison, previous programs produce Classic Venn and Edwards’ Venn diagrams and allow for a maximum of six sets. The software incorporates user-friendly features and works in Windows, Linux and Mac OS. Its graphical interface does not require a user to have programing skills. Users can modify diagram content for up to eight datasets because of the Scalable Vector Graphics output. VennPainter can provide output results in vertical, horizontal and matrix formats, which facilitates sharing datasets as required for further identification of candidate genes. Users can obtain gene lists from shared sets by clicking the numbers on the diagram. Thus, VennPainter is an easy-to-use, highly efficient, cross-platform and powerful program that provides a more comprehensive tool for identifying candidate genes and visualizing the relationships among genes or gene families in comparative analysis.

Because thermal infrared (TIR) images are not affected by light and foggy environments, which are widely used in various night traffic scenarios. Especially, thermal infrared images also play an important role in autonomous vehicles. However, low contrast and lack of chromaticity have always been their problems. Image colorization is a vital technique to improve the quality of TIR images, which is beneficial to human interpretation and downstream tasks. Despite thermal infrared image colorization methods have been rapidly improved, the detail blurriness and color distortion in colorized images remain under-addressed. Mostly because these methods cannot effectively extract the ambiguous feature information of TIR images. Hence, we propose a large kernel (LK) U-Net and Attention_U-Net-Transformer (ViT-Based) based generative adversarial network. An LK_U-Net is designed to extract the feature of TIR images. Then, a branch structure composed of Attention_U-Net and ViT-Based can provide the network with semantic information from different perspectives to decode features. In addition, a composite loss function is employed to ensure the network generates a high-quality colorized image. The proposed method is evaluated on KAIST and IRVI datasets. Experimental results demonstrate the superiority of the proposed LKAT-GAN over other methods for the task of thermal infrared image colorization. The code is available at.

Computational imaging provides comprehensive and reliable information about human tissue for medical diagnosis and treatment, with medical image fusion as one of the most important technologies in the field. Empirical mode decomposition (EMD), a promising model for image processing, has been used for image fusion in some methods. However, the varying number of decomposed layers leads to problems using EMD for image fusion. In this work, we propose a fusion method for medical images incorporating L2-norm-based features, a match/salience/fuzzy-weighted measure, and the two-dimensional Littlewood-Paley empirical wavelet transform (2D LPEWT) as new version of EMD. We first decompose medical images with LPEWT to obtain the residual component (residue) and detailed sub-images (intrinsic mode functions, IMFs). Then we extract the regional features of residue with an L2-norm-based model to fuse the residue while simultaneously fusing IMFs using a method combining a fuzzy membership function with a match/salience measurement. Finally, we reconstruct the comprehensive image by applying inverse LPEWT to the fused residue and IMFs. We evaluated our method using a frequently-used dataset of brain images. The results show that our proposed method is more effective than conventional methods by fusing more information into the final images. We also show a feasible scheme for applying EMD to image fusion.

Medical image fusion combines the multiple features of human tissue from different source images, which will be conducive to clinical diagnosis. Because of the human visual perception and mechanisms of medical imaging, abundant fuzzy features exist in medical image fusion with remarkable influence. To address this issue, we introduce the similarity measure of fuzzy set theory into medical image fusion to abstract and measure these fuzzy features. In this study, we present a new similarity measure of intuitionistic fuzzy set theory to represent image features and present a lightweight medical image fusion technique on the basis of this new measure. First, the new similarity measure is proposed according to geometric modeling technique and verified by mathematical justification. Second, a set of high frequency sub-images and a low frequency sub-image are produced by Laplacian pyramid decomposition to separate different image features. Third, our proposed similarity measure is utilized to describe and extract the detailed image features. Finally, two fusion rules and inverse Laplacian pyramid decomposition are sequentially used to get the fused image. This research shows that the similarity measure of fuzzy set theory can get excellent performance in medical image fusion. Experiments reveal that our similarity measure and medical image fusion method both have the superior performance to most of the existing methods.