Doublets in single-cell sequencing data, caused by the simultaneous capture of two or more cells within a single reaction volume, introduce biases that compromise downstream analysis. Existing doublet detection methods primarily focus on single-modality data and exhibit limited robustness across datasets. To overcome these limitations, we developed OmniDoublet, a multimodal doublet detection method that integrates transcriptomic and epigenomic data. OmniDoublet leverages the Jaccard similarity coefficient to calculate weights that assess the reliability of neighboring cells across modalities, combining doublet scores from different modalities into a final integrated score. It further employs a Gaussian mixture model (GMM) to establish thresholds, enabling accurate binary classification of cells as singlets or doublets based on the integrated score. OmniDoublet offers a robust framework for detecting doublets across diverse scenarios. Benchmarking against state-of-the-art methods across various datasets demonstrates that OmniDoublet achieves superior accuracy, robustness, and scalability. By harnessing the comprehensive information from multimodal single-cell data, OmniDoublet enhances doublet detection, enabling researchers to gain more accurate and reliable insights into cellular processes.

Author disambiguation arises when different authors share the same name, which is a critical task in digital libraries, such as DBLP, CiteULike, CiteSeerX, etc. While the state-of-the-art methods have developed various paper embedding-based methods performing in a top-down manner, they primarily focus on the ego-network of a target name and overlook the low-quality collaborative relations existed in the ego-network. Thus, these methods can be suboptimal for disambiguating authors.In this paper, we model the author disambiguation as a collaboration network reconstruction problem, and propose an incremental and unsupervised author disambiguation method, namely IUAD, which performs in a bottom-up manner. Initially, we build a stable collaboration network based on stable collaborative relations. To further improve the recall, we build a probabilistic generative model to reconstruct the complete collaboration network. In addition, for newly published papers, we can incrementally judge who publish them via only computing the posterior probabilities. We have conducted extensive experiments on a large-scale DBLP dataset to evaluate IUAD. The experimental results demonstrate that IUAD not only achieves the promising performance, but also outperforms comparable baselines significantly. Codes are available at https://github.com/papergitgit/IUAD.

A large amount of omics data and number of bioinformatics tools has been produced. However, the methods for further exploring omics data are simple, in particular, to mine key regulatory genes, which are a priority concern in biological systems, and most of the specific functions are still unknown. First, raw data of two genotypes of melon (susceptible and resistant) were obtained by transcriptome analysis. Second, 391 transcription factors (TFs) were identified from the plant transcription factor database and cucurbit genomics database. Then, functional enrichment analysis indicated that these genes were mainly annotated in the process of transcription regulation. Third, 243 and 230 module-specific TFs were screened by weighted gene coexpression network analysis and short time series expression miner, respectively. Several TF genes, such as WRKYs and bHLHs, were regarded as key regulatory genes according to the values of significantly different modules. The coexpression network showed that these TF genes were significant correlated with resistance (R) genes, such as DRP2, RGA3, DRP1 and NB-ARC. Fourth, cis-acting element analysis illustrated that these R genes may bind to WRKY and bHLH. Finally, the expression of WRKY genes was verified by quantitative reverse transcription PCR (RT-qPCR). Phylogenetic analysis was carried out to further confirm that these TFs may play a critical role in Curcurbitaceae disease resistance. This study provides a new optimized combination strategy to explore the functions of TFs in a wide spectrum of biological processes. This strategy may also effectively predict potential relationships in the interactions of essential genes.

SUMMARY: The rapid progresses of high-throughput sequencing technology-based omics and mass spectrometry-based proteomics, such as data-independent acquisition and its penetration to clinical studies have generated increasing number of proteomic datasets containing hundreds to thousands of samples. To analyze these quantitative proteomic datasets and other omics (e.g. transcriptomics and metabolomics) datasets more efficiently and conveniently, we present a web server-based software tool ProteomeExpert implemented in Docker, which offers various analysis tools for experimental design, data mining, interpretation and visualization of quantitative proteomic datasets. ProteomeExpert can be deployed on an operating system with Docker installed or with R language environment. AVAILABILITY AND IMPLEMENTATION: The Docker image of ProteomeExpert is freely available from https://hub.docker.com/r/lifeinfo/proteomeexpert. The source code of ProteomeExpert is also openly accessible at http://www.github.com/guomics-lab/ProteomeExpert/. In addition, a demo server is provided at https://proteomic.shinyapps.io/peserver/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gabor feature has been widely viewed as a good representation method for face recognition. AdaBoost is an excellent machine learning technique. Learning Gabor feature based classifier using AdaBoost is one of the best face recognition algorithms. However, dimensionality of Gabor feature space usually is very high, which makes the training program need huge memory or else take a very long time to run. In this paper, we propose a method which not only can solve the problem but also can improve recognition accuracy. Several subspaces with moderate size are randomly generated from original high dimensional Gabor feature space. Then strong classifier is trained in every random subspace (T. Kam Ho, 1998) respectively and the outputs of multiple classifiers are combined in the final decision. Experimental results demonstrate that the method saves a great amount of training time, and achieves an exciting recognition rate of 97.91% on the FERET Fb test set

The most expressive way human display emotion is through facial expressions. Facial expression recognition is necessary for designing any practical human-machine interfaces. This paper proposes a novel framework to real-time facial expression recognition within the interactive computer environment. The two major contributions of this work are: first, we proposed a novel network structure and parameters learning algorithm for embedded HMM (Ara V. Nefian and Monson H. Hayes III, March, 1999) based on AdaBoost (Freund, Y. and Schapire, R. E., 1997). Second, we apply this optimized embedded HMM to real-time facial expression recognition. In this paper, the embedded HMM uses two-dimensional discrete cosine transform (2D-DCT) coefficients as the observation vectors opposite to previous HMM approaches which use pixel intensities to form the observation vectors. Our proposed system reduces the complexity of the training and recognition system. It offers a more flexible framework and can be used in real-time human-machine interactive applications. Experimental results demonstrate that the proposed approach is an effective method to recognize facial expression.