LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don’t have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/. LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. In this paper, we propose a novel computational method “SFPEL-LPI” to predict lncRNA-protein interactions. SFPEL-LPI makes use of lncRNA sequences, protein sequences and known lncRNA-protein associations to extract features and calculate similarities for lncRNAs and proteins, and then combines them with a feature projection ensemble learning frame. SFPEL-LPI can predict unobserved interactions between lncRNAs and proteins, and also can make predictions for new lncRNAs (or proteins), which have no interactions with any proteins (or lncRNAs). SFPEL-LPI produces high-accuracy performances on the benchmark dataset when evaluated by five-fold cross validation, and outperforms state-of-the-art methods. The case studies demonstrate that SFPEL-LPI can find out novel associations, which are confirmed by literature. To facilitate the lncRNA-protein interaction prediction, we develop a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.

Object detection in images captured by unmanned aerial vehicles (UAVs) holds great potential in various domains, including civilian applications, urban planning, and disaster response. However, it faces several challenges, such as multi-scale variations, dense scenes, complex backgrounds, and tiny-sized objects. In this paper, we present a novel scale-adaptive YOLO framework called SMFF-YOLO, which addresses these challenges through a multi-level feature fusion approach. To improve the detection accuracy of small objects, our framework incorporates the ELAN-SW object detection prediction head. This newly designed head effectively utilizes both global contextual information and local features, enhancing the detection accuracy of tiny objects. Additionally, the proposed bidirectional feature fusion pyramid (BFFP) module tackles the issue of scale variations in object sizes by aggregating multi-scale features. To handle complex backgrounds, we introduce the adaptive atrous spatial pyramid pooling (AASPP) module, which enables adaptive feature fusion and alleviates the negative impact of cluttered scenes. Moreover, we adopt the Wise-IoU(WIoU) bounding box regression loss to enhance the competitiveness of different quality anchor boxes, which offers the framework a more informed gradient allocation strategy. We validate the effectiveness of SMFF-YOLO using the VisDrone and UAVDT datasets. Experimental results demonstrate that our model achieves higher detection accuracy, with AP50 reaching 54.3% for VisDrone and 42.4% for UAVDT datasets. Visual comparative experiments with other YOLO-based methods further illustrate the robustness and adaptability of our approach.

Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. In this paper, we present a novel measure of drug-drug similarity named “linear neighborhood similarity”, which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method “LNSM” and its extension “LNSM-SMI” to predict side effects of new drugs, and propose the method “LNSM-MSE” to predict unobserved side effect of approved drugs. We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.

BACKGROUND. Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs representing other entities, close follow-up or sublobar resection without node dissection may be appropriate. OBJECTIVE. To develop and validate an automated deep learning model for differentiation of pGGNs on chest CT representing IAC from those representing atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), or minimally invasive adenocarcinoma (MIA). METHODS. This retrospective study included 402 patients (mean age, 53.2 years; 119 men, 283 women) with a total of 448 pGGNs on noncontrast chest CT that underwent resection from January 2019 to June 2022 and were histologically diagnosed as AAH (n=29), AIS (n=83), MIA (n=235), or IAC (n=101). We developed Lung-PNet, a 3D deep learning model for automatic segmentation and classification (probability of IAC vs other entities) of pGGNs on CT. Nodules from January 2019 to December 2021 were randomly allocated to training (n=327) and internal test (n=82) sets; nodules from January 2022 to June 22 formed a holdout test set (n=39). Segmentation performance was assessed by Dice coefficients, using radiologists' manual segmentations as reference. Classification performance was assessed by AUCROC and AUC under precision-recall curve (AUCPR), and compared with that of four readers (three radiologists, one surgeon). Code is publicly available: https://github.com/Xiaodong-Zhang-PKUFH/Lung-PNet.git. RESULTS. In the holdout test set, Dice coefficients for segmentation of IACs and of other lesions were 0.860 and 0838, and AUCROC and AUCPR for classification as IAC were 0.911 and 0.842. At threshold probability of ≥ 50.0% for prediction of IAC, Lung-PNet had sensitivity, specificity, accuracy, and F1 score of 50.0%, 92.0%, 76.9%, and 60.9% in the holdout test set. Accuracy and F1 score (with p values vs Lung-PNet) were, in the holdout test set: reader 1, 51.3% (p=.02) and 48.7% (p=.008); reader 2, 79.5% (p=.75) and 75.0% (p=.10); reader 3, 66.7% (p=.35) and 68.3% (p<.001); reader 4, 71.8% (p=.48) and 42.1% (p=.18). CONCLUSION. Lung-PNet exhibited robust performance for segmenting and classifying (IAC vs other entities) pGGNs on chest CT. CLINICAL IMPACT. This automated deep learning tool may help guide selection of surgical strategies for pGNN management.

Unmanned aerial vehicle (UAV) scanning and mobile laser scanning provide detailed three-dimensional observations for forest inventory and structural monitoring for which point-wise separation of ground, understory, woody components, and foliage is fundamental. Modern segmentation pipelines can produce multiple predictions from different feature configurations, training objectives, random seeds, or network architectures. However, equal and fixed-weight fusion implicitly assume stable source quality, although the relative reliability of these predictions can vary across semantic classes and sensing conditions, leading to error propagation and negative transfer. We therefore formulate multi-source prediction integration as a calibration-supported class-wise reliability decision problem and propose the unified class-wise reliability gate (UCRG). The UCRG measures global and class-wise source reliability from labeled calibration data, uses shrinkage to stabilize class-specific estimates when labels are scarce, and conditionally performs soft fusion or hard source selection according to the reliability gap. An optional calibration-guided confusion-pair refiner further targets residual ambiguity between semantically similar classes. All the reliability estimation and parameter selection processes are performed without query labels. Experiments on FOR-instance, EvoMLS, and Lin3D cover UAV and mobile laser scanning together with cross-dataset transfer. Using one shared parameterization across the three primary protocols, the UCRG obtains 70.68% mIoU and records two negative-transfer episodes across the six evaluated episodes compared with six for fixed-weight fusion. When the gate parameters are selected solely from the available calibration support in each deployment, the UCRG reaches 59.20% mean mIoU in the PTv3-augmented Lin3D candidate pool. These results show that reliability-guided decisions can exploit class-dependent complementarity across heterogeneous frozen predictors and sensing conditions.