Single-cell RNA-sequencing (scRNA-seq) can excavate cellular heterogeneity and distinguish different types of cells. Clustering cells into subpopulations is essential in analyzing scRNA-seq data as it can help subsequent downstream analysis. However, scRNA-seq data are high-dimensional, sparse, and contain erroneous zero counts, which poses a great challenge for clustering. Although various methods have emerged in recent years, they cannot fully grasp the information of cells by characterizing scRNA-seq data from a single perspective, resulting in poor learned embedding representation and poor clustering performance. In this paper, we propose a multi-view clustering framework scMVAF for scRNA-seq data, which can learn more discriminative embedding representations by integrating feature information from multiple cell views. First, to comprehensively capture the data information, we generate multiple diverse views by down-sampling features, and then scMVAF learns a strong embedding representation for each cell view using an autoencoder based on a denoising zero-inflated negative binomial model. Next, to explore the correlation between cells in different views, a multi-view fusion module is introduced to fuse the embeddings from different views into a unified feature space. Concurrently, the fused embeddings are clustered to generate pseudo labels to improve the embedding process, and finally updating the embedding features and pseudo-labels in turn to obtain better clustering performance. Experiments are implemented on 16 real datasets and verify that scMVAF is superior to the other eight advanced technologies. Our code script can be obtained at https://github.com/LQXLE/scMVAF/.

Genomic prediction (GP) has made significant progress in the field of breeding. Traditional linear models perform well in handling simple traits but have limitations in extracting nonlinear features for complex traits. The introduction of deep learning (DL) techniques has provided a new approach to GP, especially suited for high-dimensional data processing and complex trait prediction. However, traditional DL models require manual design of the network architecture, which necessitates continuous experimentation and modification. In this paper, we propose a new framework, MDNN, that utilizes the memetic algorithm for neural architecture search and automatically optimizes the network architecture. Compared with the DNNGP, MDNN achieved a 36.49% improvement in the average Pearson correlation coefficient on the wheat599 dataset and a 12.28% improvement on the wheat2000 dataset.

Predicting long non-coding RNA (lncRNA)-protein interactions is essential for understanding biological processes and discovering new therapeutic targets. In this study, we propose a novel model based on inter-view contrastive learning and miRNA fusion for lncRNA-protein interaction (LPI) prediction, called ICMF-LPI, which utilizes a heterogeneous information network to enhance LPI prediction. The model integrates miRNA as a mediator, constructing an lncRNA-miRNA-protein network, and employs metapath to extract diverse relationships from heterogeneous graphs. By fusing miRNA-related information and leveraging contrastive learning across inter-views, ICMF-LPI effectively captures potential interactions. Experimental results, including five-fold cross-validation, demonstrate the model's superior performance compared to several state-of-the-art methods, with significant improvements in the area under the receiver operating characteristic curve and the area under the precision-recall curve metrics. Notably, even when direct LPI connections are excluded, ICMF-LPI still achieves competitive predictive accuracy, performing comparably or better than some existing models. This demonstrates that the proposed model is effective in scenarios where direct interaction data are unavailable. This approach offers a promising direction for developing predictive models in bioinformatics, particularly in challenging conditions.

Accurately predicting the functions of multi-functional therapeutic peptides is crucial for the development of related drugs. However, existing peptide function prediction methods largely rely on either a single type of feature or a single model architecture, limiting prediction accuracy and applicability. Additionally, training better-performing models on datasets with class imbalance issues remains a significant challenge. In this study, we propose the multi-functional therapeutic peptide of multi-feature fusion prediction (MFTP_MFFP) model, a novel method for predicting the functionality of multi-functional therapeutic peptides. This approach uses various encoding techniques to process peptide sequence data, generating multiple features that help the model learn hidden information within the sequences. To maximize the effectiveness of these features, we propose a gated feature fusion module that efficiently integrates them. The module assigns learnable gating weights to each feature, optimizing integration and enhancing fusion efficiency. The fused features are then passed into a neural network model for feature extraction. Additionally, we propose a marginal focal dice loss function (MFDL) to address the class imbalance and improve the model's prediction performance. Experimental results show that the MFTP_MFFP model outperforms existing models in all evaluation metrics, demonstrating its robustness and effectiveness in multi-functional therapeutic peptide prediction tasks.

BACKGROUND: The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. METHODS: In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. RESULTS: The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. CONCLUSION: The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.

Machine learning based mobile traffic classification has become a popular topic in recent years. As mobile traffic data is dynamic in nature, the static model has become ineffective for the task of classifying future traffic. This is known as the concept drift problem in data streams. To this end, this paper presents an adaptive mobile traffic classification method. Specifically, a method based on the fuzzy competence model is devised to detect concept drift, and a dynamic learning method is presented to update the classification model, so as to adapt to an ever-changing environment at an appropriate time. The concept drift detection method relies on the data distribution instead of the classification error rate. Furthermore, the weights of flow samples are dynamically updated and flow samples are resampled for training a new model when a concept drift is detected. Moreover, recently trained models are saved and used for classification in weighted voting. The weight of each model is updated according to the performance it obtains on the most recent flow samples. On mobile traffic data, experimental results show that our proposed method obtains lower classification error rate with less time consumption on updating models as compared to related methods designed for handling concept drift problems.

Mobile app traffic classification aims to automatically map mobile packets into apps. It has become an active task in mobile traffic engineering, and numerous algorithms have been proposed for this task, including machine learning, deep packet inspection methods. However, existing works mainly evaluate their methods on their own collected mobile traffic traces. There is no public benchmark data. The results in existing papers cannot be directly compared. This largely limits the development of mobile app traffic classification methods. This paper describes our Mobile Traffic Data(MTD): Android app traffic flow sample sets with ground truth. The goal of MTD is to advance the state-of-arts in mobile app traffic classification. For building MTD, we collected and annotated more than ten thousands of traffic flows using Mobilegt system. The popularity used flow features were also extracted to build flow samples for mobile traffic classification using machine learning. MTD sets have been shared in public. In addition, this paper provides the performance analysis of typical machine learning techniques on MTD, which can be served as the baseline results on this benchmark data.

Class imbalance problem has attracted a great attention in the field of ELM (extreme learning machine). Cost sensitive ELM was proposed to address class imbalance but it merely handled binary class imbalance and required to predefine misclassification costs subjectively. Boosting WELM has been presented to handle multi class imbalance, and performed well on improving the classification accuracy of the minority class, but it may excessively strengthen minority class samples and degrade the performance of the majority class. This paper presents a method named OCS-BWELM (objective cost-sensitive-boosting-WELM) to handle multi class imbalance. It takes boosting WELM as the basic learning algorithm. The misclassification costs are determined by the distributions of the given data rather than being defined subjectively. More specifically, it seeks optimal costs through maximizing the mutual information between real targets and prediction outputs. A specific feature of OCS-BWELM is that its costs are objective. Experiments are carried out to compare our method against existing ELM related works on handling multi class imbalance. Results show that our method could achieve a better performance balance between minority class and majority class than boosting WELM. And it outperforms others in terms of G-mean, F-score and F-measures of minority classes in most cases.

Mobile network traffic engineering and management activities require traffic traces where each packet or flow is associated with some ground truth regarding mobile app or protocol. This paper presents a system named mobilegt that collects mobile traffic and links the ground truth to it without rooting mobile devices. It consists of two elements: mobilegt client and mobilegt server. Mobilegt client iteratively probes monitored mobile nodes' kernel to obtain socket information on active TCP/UDP sessions. Mobilegt server captures the packets generated on monitored nodes at the aid of Virtual Private Network (VPN), and labels each packet/flow by exploring the association between socket and packet. Our preliminary experimental results show that mobilegt can tag more than 98% of bytes and 93% of flows on average without significantly affecting CPU load.