Sequence alignment is a fundamental step in genomic data analysis. Third-generation sequencing technology facilitates the acquisition of high-quality genomic data but the explosive growth of sequencing data poses huge challenges to current sequence alignment. To reduce sequence alignment time and enhance alignment performance, a parallelization framework based on Minimap2 is proposed, called ParaSAT, aiming to expedite sequence alignment and offer insights to researchers in the field. In order to achieve load balancing among multi-nodes, We design a task pool scheduling strategy which can dynamically distribute tasks according to the status of compute nodes. To evaluate the performance of the framework, We choose 6 dataset to conduct experiments on TH-1 supercomputer. The outcomes confirm that the parallel framework ensures sequence alignment accuracy, and demonstrates a notable speedup on large datasets, approaching linearity, with parallel efficiency consistently above 80%. The framework also exhibits strong and weak scalability, effectively enhancing the efficiency of sequence alignment and offering guidance for high-performance genomic data processing.

Personality detection identifies personality traits in text. Current approaches often rely on deep learning networks for text representation but they overlook the significance of psychological language knowledge in connecting user language expression to psychological characteristics. Consequently, the accuracy of personality detection is compromised. To address this issue, this paper presents PS-GCN, a model integrating Psychological knowledge and Sentiment semantic features through Graph Convolution Networks. Firstly, the Bi-LSTM network captures local features of preprocessed sentences to accurately represent the output of sentence sentiment features. Secondly, GCNs map psycholinguistic knowledge, forming semantic networks of entities and relationships. P-GCN is designed to capture the dependency information between psycholinguistic features, while S-GCN utilises syntactic structure analysis to gather more abundant information features and enhance semantic understanding ability. Finally, attention calculation is employed to reinforce key features and weaken irrelevant information. Additionally, a sentence group model captures combined features of related sentences, effectively utilising the text structure to mine sentimental features. Experimental results on multiple datasets demonstrate that the proposed method significantly improves the classification accuracy in personality detection tasks.

Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In this paper, in order to solve the problem of multi-label and low classification accuracy of histopathology images, we propose a locally deep convolutional Swim framework (LDCSF) to classify multi-label histopathology images. In order to be able to provide local field of view diagnostic results, we propose the LDCSF model, which consists of a Swin transformer module, a local depth convolution (LDC) module, a feature reconstruction (FR) module, and a ResNet module. The Swin transformer module reduces the amount of computation generated by the attention mechanism by limiting the attention to each window. The LDC then reconstructs the attention map and performs convolution operations in multiple channels, passing the resulting feature map to the next layer. The FR module uses the corresponding weight coefficient vectors obtained from the channels to dot product with the original feature map vector matrix to generate representative feature maps. Finally, the residual network undertakes the final classification task. As a result, the classification accuracy of LDCSF for interstitial area, necrosis, non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively.

Histopathology image segmentation is the gold standard for diagnosing cancer, and can indicate cancer prognosis. However, histopathology image segmentation requires high-quality masks, so many studies now use image-level labels to achieve pixel-level segmentation to reduce the need for fine-grained annotation. To solve this problem, we propose an attention-based cross-view feature consistency end-to-end pseudo-mask generation framework named CVFC based on the attention mechanism. Specifically, CVFC is a three-branch joint framework composed of two Resnet38 and one Resnet50, and the independent branch multi-scale integrated feature map to generate a class activation map (CAM); in each branch, through down-sampling and The expansion method adjusts the size of the CAM; the middle branch projects the feature matrix to the query and key feature spaces, and generates a feature space perception matrix through the connection layer and inner product to adjust and refine the CAM of each branch; finally, through the feature consistency loss and feature cross loss to optimize the parameters of CVFC in co-training mode. After a large number of experiments, An IoU of 0.7122 and a fwIoU of 0.7018 are obtained on the WSSS4LUAD dataset, which outperforms HistoSegNet, SEAM, C-CAM, WSSS-Tissue, and OEEM, respectively.

Protein-nucleic acid interactions play a very important role in a variety of biological activities. Accurate identification of nucleic acid-binding residues is a critical step in understanding the interaction mechanisms. Although many computationally based methods have been developed to predict nucleic acid-binding residues, challenges remain. In this study, a fast and accurate sequence-based method, called ESM-NBR, is proposed. In ESM-NBR, we first use the large protein language model ESM2 to extract discriminative biological properties feature representation from protein primary sequences; then, a multi-task deep learning model composed of stacked bidirectional long short-term memory (BiLSTM) and multi-layer perceptron (MLP) networks is employed to explore common and private information of DNA- and RNA-binding residues with ESM2 feature as input. Experimental results on benchmark data sets demonstrate that the prediction performance of ESM2 feature representation comprehensively outperforms evolutionary information-based hidden Markov model (HMM) features. Meanwhile, the ESM-NBR obtains the MCC values for DNA-binding residues prediction of 0.427 and 0.391 on two independent test sets, which are 18.61 and 10.45% higher than those of the second-best methods, respectively. Moreover, by completely discarding the time-cost multiple sequence alignment process, the prediction speed of ESM-NBR far exceeds that of existing methods (5.52s for a protein sequence of length 500, which is about 16 times faster than the second-fastest method). A user-friendly standalone package and the data of ESM-NBR are freely available for academic use at: https://github.com/wwzll123/ESM-NBR.

Drug response prediction in cancer cell lines is of great significance in personalized medicine. In this study, we propose GADRP, a cancer drug response prediction model based on graph convolutional networks (GCNs) and autoencoders (AEs). We first use a stacked deep AE to extract low-dimensional representations from cell line features, and then construct a sparse drug cell line pair (DCP) network incorporating drug, cell line, and DCP similarity information. Later, initial residual and layer attention-based GCN (ILGCN) that can alleviate over-smoothing problem is utilized to learn DCP features. And finally, fully connected network is employed to make prediction. Benchmarking results demonstrate that GADRP can significantly improve prediction performance on all metrics compared with baselines on five datasets. Particularly, experiments of predictions of unknown DCP responses, drug-cancer tissue associations， and drug-pathway associations illustrate the predictive power of GADRP. All results highlight the effectiveness of GADRP in predicting drug responses, and its potential value in guiding anti-cancer drug selection.

Accurate prediction of disease-gene associations is a crucial tissue in the treatment of diseases. Currently, deep learning-based methods have been proposed to determine the associations between diseases and genes. However, previous network-based models do not consider the semantic characteristics of various biomedical entities and suffer from the problems of cold-start. To this end, this study proposes a heterogeneous network-based deep learning framework (termed deepDGA) to predict disease-gene associations. First, a heterogeneous network with four kinds of biological nodes and eight kinds of edges is constructed. Second, we develop a meta path-driven deep Transformer encoder to learn node representations which contains semantic characteristics of nodes in the heterogeneous network. Finally, the inductive matrix completion algorithm that can solve problem of cold-start, is used for disease-gene association prediction. The results of 5-flod cross-validation and top-ranked predictions suggest that deepDGA is superior to other methods. In addition, we further observe that deepDGA performs the highest predictive ability for specific diseases via the literature verification, KEGG human pathway analyses, and GO enrichment analyses. In summary, deepDGA is an effective framework for predicting the diseases-gene associations.

Sentiment classification can provide the decision support of social applications such as trend judgment, public opinion monitoring, etc. However, the accuracy of sentiment classification for Chinese Weibo is still not satisfactory due to the complexity of Chinese. In addition, affected by the different organisational structure levels, the sentiment tendency of fewer Weibo Comments may be judged to be the opposite. To solve the problem above, this paper presents a Chinese sentiment classification model based on extended sentiment dictionary and organisational structure of comments. First, the sentiment dictionary can be extended by using seven dictionaries, which include the base sentiment dictionary and six additional dictionaries. Then, the sets of rules are constructed, which include inter-sentence rules and organisational structure rules. Finally, comments on three hot topics are crawled and used to make the data sets for sentiment calculation. Accordingly, based on the result of sentiment calculation, sentiment classification is completed. The effectiveness of the proposed model is verified through comparison experiments, and the experimental results are also discussed.

Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients. However, DR can easily delay the occurrence of blindness through the diagnosis of the fundus. In view of the reality, it is difficult to collect a large amount of diabetic retina data in clinical practice. This paper proposes a few-shot learning model of a deep residual network based on Earth Mover's Distance algorithm to assist in diagnosing DR. We build training and validation classification tasks for few-shot learning based on 39 categories of 1000 sample data, train deep residual networks, and obtain experience maximization pre-training models. Based on the weights of the pre-trained model, the Earth Mover's Distance algorithm calculates the distance between the images, obtains the similarity between the images, and changes the model's parameters to improve the accuracy of the training model. Finally, the experimental construction of the small sample classification task of the test set to optimize the model further, and finally, an accuracy of 93.5667% on the 3wayl0shot task of the diabetic retina test set. For the experimental code and results, please refer to: https://github.com/panliangrui/few-shot-learning-funds.

ABSTRACT In this paper, we study the delay performance in a wireless sensor network (WSN) with a cluster‐tree topology. The end‐to‐end delay in such a network can be strongly dependent on the relative location between the sensors and the sink and the resource allocations of the cluster heads (CHs). For real‐time traffic, packets transmitted with excessive delay are dropped. Given the timeline allocations of each CH for local and inter‐cluster traffic transmissions, an analytical model is developed to find the distribution of the end‐to‐end transmission delay for packets originated from different clusters. Based on this result, the packet drop rate is derived. A heuristic scheme is then proposed to jointly find the timeline allocations of all the CHs in a WSN in order to achieve the minimum and balanced packet drop rate for traffic originated from different levels of the cluster tree. Simulation results are shown to verify the analysis and to demonstrate the effectiveness of the proposed CH timeline allocation scheme. Copyright © 2012 John Wiley & Sons, Ltd.

In a wireless sensor network (WSN) with specially deployed cluster heads (CHs), association relation between sensor nodes and the CHs is important as it affects the radio resource allocations, which further determine the overall network throughput, energy consumption, and other performance. In a lot of cases, the CHs are placed in random locations, and strong overlapping may exist between their coverage areas so that sensor nodes can choose to associate to different CHs. In this paper we first formulate two optimization problems that jointly consider sensor node association and radio resource allocations, one for maximizing the network level throughput, and another for balancing the energy consumption among the CHs. For each of the optimization problems, a heuristic scheme is designed that jointly considers both the timeline allocations of the CHs and the sensor node association. Numerical results based on computer simulation demonstrate that the proposed schemes achieve close-to-optimum performance. In addition, the schemes achieve much better throughput and energy performance than the straightforward association schemes.