A novel transformer-based aggregation model for predicting gene mutations in lung adenocarcinoma.

In recent years, predicting gene mutations on whole slide imaging (WSI) has gained prominence. The primary challenge is extracting global information and achieving unbiased semantic aggregation. To address this challenge, we propose a novel Transformer-based aggregation model, employing a self-learning weight aggregation mechanism to mitigate semantic bias caused by the abundance of features in WSI. Additionally, we adopt a random patch training method, which enhances model learning richness by randomly extracting feature vectors from WSI, thus addressing the issue of limited data. To demonstrate the model's effectiveness in predicting gene mutations, we leverage the lung adenocarcinoma dataset from Shandong Provincial Hospital for prior knowledge learning. Subsequently, we assess TP53, CSMD3, LRP1B, and TTN gene mutations using lung adenocarcinoma tissue pathology images and clinical data from The Cancer Genome Atlas (TCGA). The results indicate a notable increase in the AUC (Area Under the ROC Curve) value, averaging 4%, attesting to the model's performance improvement. Our research offers an efficient model to explore the correlation between pathological image features and molecular characteristics in lung adenocarcinoma patients. This model introduces a novel approach to clinical genetic testing, expected to enhance the efficiency of identifying molecular features and genetic testing in lung adenocarcinoma patients, ultimately providing more accurate and reliable results for related studies.

Semi-supervised breast cancer pathology image segmentation based on fine-grained classification guidance.

Breast cancer pathological image segmentation (BCPIS) holds significant value in assisting physicians with quantifying tumor regions and providing treatment guidance. However, achieving fine-grained semantic segmentation remains a major challenge for this technology. The complex and diverse morphologies of breast cancer tissue structures result in high costs for manual annotation, thereby limiting the sample size and annotation quality of the dataset. These practical issues have a significant impact on the segmentation performance. To overcome these challenges, this study proposes a semi-supervised learning model based on classification-guided segmentation. The model first utilizes a multi-scale convolutional network to extract rich semantic information and then employs a multi-expert cross-layer joint learning strategy, integrating a small number of labeled samples to iteratively provide the model with class-generated multi-cue pseudo-labels and real labels. Given the complexity of the breast cancer samples and the limited sample quantity, an innovative approach of augmenting additional unlabeled data was adopted to overcome this limitation. Experimental results demonstrate that, although the proposed model falls slightly behind supervised segmentation models, it still exhibits significant progress and innovation. The semi-supervised model in this study achieves outstanding performance, with an IoU (Intersection over Union) value of 71.53%. Compared to other semi-supervised methods, the model developed in this study demonstrates a performance advantage of approximately 3%. Furthermore, the research findings indicate a significant correlation between the classification and segmentation tasks in breast cancer pathological images, and the guidance of a multi-expert system can significantly enhance the fine-grained effects of semi-supervised semantic segmentation.

Improving Alzheimer Diagnoses With An Interpretable Deep Learning Framework: Including Neuropsychiatric Symptoms.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by the progressive cognitive decline. Among the various clinical symptoms, neuropsychiatric symptoms (NPS) commonly occur during the course of AD. Previous researches have demonstrated a strong association between NPS and severity of AD, while the research methods are not sufficiently intuitive. Here, we report a hybrid deep learning framework for AD diagnosis using multimodal inputs such as structural MRI, behavioral scores, age, and gender information. The framework uses a 3D convolutional neural network to automatically extract features from MRI. The imaging features are passed to the Principal Component Analysis for dimensionality reduction, which fuse with non-imaging information to improve the diagnosis of AD. According to the experimental results, our model achieves an accuracy of 0.91 and an area under the curve of 0.97 in the task of classifying AD and cognitively normal individuals. SHapley Additive exPlanations are used to visually exhibit the contribution of specific NPS in the proposed model. Among all behavioral symptoms, apathy plays a particularly important role in the diagnosis of AD, which can be considered a valuable factor in further studies, as well as clinical trials.

FCOS-LSC: A Novel Model for Green Fruit Detection in a Complex Orchard Environment.

To better address the difficulties in designing green fruit recognition techniques in machine vision systems, a new fruit detection model is proposed. This model is an optimization of the FCOS (full convolution one-stage object detection) algorithm, incorporating LSC (level scales, spaces, channels) attention blocks in the network structure, and named FCOS-LSC. The method achieves efficient recognition and localization of green fruit images affected by overlapping occlusions, lighting conditions, and capture angles. Specifically, the improved feature extraction network ResNet50 with added deformable convolution is used to fully extract green fruit feature information. The feature pyramid network (FPN) is employed to fully fuse low-level detail information and high-level semantic information in a cross-connected and top-down connected way. Next, the attention mechanisms are added to each of the 3 dimensions of scale, space (including the height and width of the feature map), and channel of the generated multiscale feature map to improve the feature perception capability of the network. Finally, the classification and regression subnetworks of the model are applied to predict the fruit category and bounding box. In the classification branch, a new positive and negative sample selection strategy is applied to better distinguish supervised signals by designing weights in the loss function to achieve more accurate fruit detection. The proposed FCOS-LSC model has 38.65M parameters, 38.72G floating point operations, and mean average precision of 63.0% and 75.2% for detecting green apples and green persimmons, respectively. In summary, FCOS-LSC outperforms the state-of-the-art models in terms of precision and complexity to meet the accurate and efficient requirements of green fruit recognition using intelligent agricultural equipment. Correspondingly, FCOS-LSC can be used to improve the robustness and generalization of the green fruit detection models.

An accurate green fruits detection method based on optimized YOLOX-m.

Fruit detection and recognition has an important impact on fruit and vegetable harvesting, yield prediction and growth information monitoring in the automation process of modern agriculture, and the actual complex environment of orchards poses some challenges for accurate fruit detection. In order to achieve accurate detection of green fruits in complex orchard environments, this paper proposes an accurate object detection method for green fruits based on optimized YOLOX_m. First, the model extracts features from the input image using the CSPDarkNet backbone network to obtain three effective feature layers at different scales. Then, these effective feature layers are fed into the feature fusion pyramid network for enhanced feature extraction, which combines feature information from different scales, and in this process, the Atrous spatial pyramid pooling (ASPP) module is used to increase the receptive field and enhance the network's ability to obtain multi-scale contextual information. Finally, the fused features are fed into the head prediction network for classification prediction and regression prediction. In addition, Varifocal loss is used to mitigate the negative impact of unbalanced distribution of positive and negative samples to obtain higher precision. The experimental results show that the model in this paper has improved on both apple and persimmon datasets, with the average precision (AP) reaching 64.3% and 74.7%, respectively. Compared with other models commonly used for detection, the model approach in this study has a higher average precision and has improved in other performance metrics, which can provide a reference for the detection of other fruits and vegetables.

Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network.

Accurate identification of driver's drowsiness state through Electroencephalogram (EEG) signals can effectively reduce traffic accidents, but EEG signals are usually stored in various clients in the form of small samples. This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver's drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver's privacy and improves communication efficiency.

Long-Range Dependence Involutional Network for Logo Detection.

Logo detection is one of the crucial branches in computer vision due to various real-world applications, such as automatic logo detection and recognition, intelligent transportation, and trademark infringement detection. Compared with traditional handcrafted-feature-based methods, deep learning-based convolutional neural networks (CNNs) can learn both low-level and high-level image features. Recent decades have witnessed the great feature representation capabilities of deep CNNs and their variants, which have been very good at discovering intricate structures in high-dimensional data and are thereby applicable to many domains including logo detection. However, logo detection remains challenging, as existing detection methods cannot solve well the problems of a multiscale and large aspect ratios. In this paper, we tackle these challenges by developing a novel long-range dependence involutional network (LDI-Net). Specifically, we designed a strategy that combines a new operator and a self-attention mechanism via rethinking the intrinsic principle of convolution called long-range dependence involution (LD involution) to alleviate the detection difficulties caused by large aspect ratios. We also introduce a multilevel representation neural architecture search (MRNAS) to detect multiscale logo objects by constructing a novel multipath topology. In addition, we implemented an adaptive RoI pooling module (ARM) to improve detection efficiency by addressing the problem of logo deformation. Comprehensive experiments on four benchmark logo datasets demonstrate the effectiveness and efficiency of the proposed approach.

Immune subtype identification and multi-layer perceptron classifier construction for breast cancer.

Introduction: Breast cancer is a heterogeneous tumor. Tumor microenvironment (TME) has an important effect on the proliferation, metastasis, treatment, and prognosis of breast cancer. Methods: In this study, we calculated the relative proportion of tumor infiltrating immune cells (TIICs) in the breast cancer TME, and used the consensus clustering algorithm to cluster the breast cancer subtypes. We also developed a multi-layer perceptron (MLP) classifier based on a deep learning framework to detect breast cancer subtypes, which 70% of the breast cancer research cohort was used for the model training and 30% for validation. Results: By performing the K-means clustering algorithm, the research cohort was clustered into two subtypes. The Kaplan-Meier survival estimate analysis showed significant differences in the overall survival (OS) between the two identified subtypes. Estimating the difference in the relative proportion of TIICs showed that the two subtypes had significant differences in multiple immune cells, such as CD8, CD4, and regulatory T cells. Further, the expression level of immune checkpoint molecules (PDL1, CTLA4, LAG3, TIGIT, CD27, IDO1, ICOS) and tumor mutational burden (TMB) also showed significant differences between the two subtypes, indicating the clinical value of the two subtypes. Finally, we identified a 38-gene signature and developed a multilayer perceptron (MLP) classifier that combined multi-gene signature to identify breast cancer subtypes. The results showed that the classifier had an accuracy rate of 93.56% and can be robustly used for the breast cancer subtype diagnosis. Conclusion: Identification of breast cancer subtypes based on the immune signature in the tumor microenvironment can assist clinicians to effectively and accurately assess the progression of breast cancer and formulate different treatment strategies for different subtypes.

BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment.

Despite of significant achievements made in the detection of target fruits, small fruit detection remains a great challenge, especially for immature small green fruits with a few pixels. The closeness of color between the fruit skin and the background greatly increases the difficulty of locating small target fruits in the natural orchard environment. In this paper, we propose a balanced feature pyramid network (BFP Net) for small apple detection. This network can balance information mapped to small apples from two perspectives: multiple-scale fruits on the different layers of FPN and a characteristic of a new extended feature from the output of ResNet50 conv1. Specifically, we design a weight-like feature fusion architecture on the lateral connection and top-down structure to alleviate the small-scale information imbalance on the different layers of FPN. Moreover, a new extended layer from ResNet50 conv1 is embedded into the lowest layer of standard FPN, and a decoupled-aggregated module is devised on this new extended layer of FPN to complement spatial location information and relieve the problem of locating small apple. In addition, a feature Kullback-Leibler distillation loss is introduced to transfer favorable knowledge from the teacher model to the student model. Experimental results show that APS of our method reaches 47.0%, 42.2%, and 35.6% on the benchmark of the GreenApple, MinneApple, and Pascal VOC, respectively. Overall, our method is not only slightly better than some state-of-the-art methods but also has a good generalization performance.

Rapid Non-Destructive Analysis of Food Nutrient Content Using Swin-Nutrition.

Food non-destructive detection technology (NDDT) is a powerful impetus to the development of food safety and quality. One of the essential tasks of food quality regulation is the non-destructive detection of the food's nutrient content. However, existing food nutrient NDDT performs poorly in terms of efficiency and accuracy, which hinders their widespread application in daily meals. Therefore, this paper proposed an end-to-end food nutrition non-destructive detection method, named Swin-Nutrition, which combined deep learning and NDDT to evaluate the nutrient content of food. The method aimed to fully capture the feature information from the food images and thus accurately estimate the nutrient content. Swin-Nutrition resorted to Swin Transformer, the feature fusion module (FFM), and the nutrient prediction module to evaluate nutrient content. In particular, Swin Transformer acted as the backbone network for feature extraction of food images, and FFM was used to obtain the discriminative feature representation to improve the accuracy of prediction. The experimental results on the Nutrition5k dataset demonstrated the effectiveness and efficiency of our proposed method. Specifically, the mean value of the percentage mean absolute error (PMAE) for calories, mass, fat, carbohydrate, and protein were only 15.3%, 12.5%, 22.1%, 20.8%, and 15.4%, respectively. We hope that our simple and effective method will provide a solid foundation for the research of food NDDT.

Accurate segmentation of green fruit based on optimized mask RCNN application in complex orchard.

Fruit and vegetable picking robots are affected by the complex orchard environment, resulting in poor recognition and segmentation of target fruits by the vision system. The orchard environment is complex and changeable. For example, the change of light intensity will lead to the unclear surface characteristics of the target fruit; the target fruits are easy to overlap with each other and blocked by branches and leaves, which makes the shape of the fruits incomplete and difficult to accurately identify and segment one by one. Aiming at various difficulties in complex orchard environment, a two-stage instance segmentation method based on the optimized mask region convolutional neural network (mask RCNN) was proposed. The new model proposed to apply the lightweight backbone network MobileNetv3, which not only speeds up the model but also greatly improves the accuracy of the model and meets the storage resource requirements of the mobile robot. To further improve the segmentation quality of the model, the boundary patch refinement (BPR) post-processing module is added to the new model to optimize the rough mask boundaries of the model output to reduce the error pixels. The new model has a high-precision recognition rate and an efficient segmentation strategy, which improves the robustness and stability of the model. This study validates the effect of the new model using the persimmon dataset. The optimized mask RCNN achieved mean average precision (mAP) and mean average recall (mAR) of 76.3 and 81.1%, respectively, which are 3.1 and 3.7% improvement over the baseline mask RCNN, respectively. The new model is experimentally proven to bring higher accuracy and segmentation quality and can be widely deployed in smart agriculture.

YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit.

Accurate detection and segmentation of the object fruit is the key part of orchard production measurement and automated picking. Affected by light, weather, and operating angle, it brings new challenges to the efficient and accurate detection and segmentation of the green object fruit under complex orchard backgrounds. For the green fruit segmentation, an efficient YOLOF-snake segmentation model is proposed. First, the ResNet101 structure is adopted as the backbone network to achieve feature extraction of the green object fruit. Then, the C5 feature maps are expanded with receptive fields and the decoder is used for classification and regression. Besides, the center point in the regression box is employed to get a diamond-shaped structure and fed into an additional Deep-snake network, which is adjusted to the contours of the target fruit to achieve fast and accurate segmentation of green fruit. The experimental results show that YOLOF-snake is sensitive to the green fruit, and the segmentation accuracy and efficiency are significantly improved. The proposed model can effectively extend the application of agricultural equipment and provide theoretical references for other fruits and vegetable segmentation.

Fast Location and Recognition of Green Apple Based on RGB-D Image.

In the process of green apple harvesting or yield estimation, affected by the factors, such as fruit color, light, and orchard environment, the accurate recognition and fast location of the target fruit brings tremendous challenges to the vision system. In this article, we improve a density peak cluster segmentation algorithm for RGB images with the help of a gradient field of depth images to locate and recognize target fruit. Specifically, the image depth information is adopted to analyze the gradient field of the target image. The vorticity center and two-dimensional plane projection are constructed to realize the accurate center location. Next, an optimized density peak clustering algorithm is applied to segment the target image, where a kernel density estimation is utilized to optimize the segmentation algorithm, and a double sort algorithm is applied to efficiently obtain the accurate segmentation area of the target image. Finally, the segmentation area with the circle center is the target fruit area, and the maximum value method is employed to determine the radius. The above two results are merged to achieve the contour fitting of the target fruits. The novel method is designed without iteration, classifier, and several samples, which has greatly improved operating efficiency. The experimental results show that the presented method significantly improves accuracy and efficiency. Meanwhile, this new method deserves further promotion.

Polar-Net: Green fruit instance segmentation in complex orchard environment.

High-quality orchard picking has become a new trend, and achieving the picking of homogeneous fruit is a huge challenge for picking robots. Based on the premise of improving picking efficiency of homo-chromatic fruit in complex environments, this paper proposes a novel homo-chromatic fruit segmentation model under Polar-Net. The model uses Densely Connected Convolutional Networks (DenseNet) as the backbone network, Feature Pyramid Network (FPN) and Cross Feature Network (CFN) to achieve feature extraction and feature discrimination for images of different scales, regions of interest are drawn with the help of Region Proposal Network (RPN), and regression is performed between the features of different layers. In the result prediction part, polar coordinate modeling is performed based on the extracted image features, and the instance segmentation problem is reduced to predict the instance contour for instance center classification and dense distance regression. Experimental results demonstrate that the method effectively improves the segmentation accuracy of homo-chromatic objects and has the characteristics of simplicity and efficiency. The new method has improved the accuracy of segmentation of homo-chromatic objects for picking robots and also provides a reference for segmentation of other fruit and vegetables.

SE-COTR: A Novel Fruit Segmentation Model for Green Apples Application in Complex Orchard.

Because of the unstructured characteristics of natural orchards, the efficient detection and segmentation applications of green fruits remain an essential challenge for intelligent agriculture. Therefore, an innovative fruit segmentation method based on deep learning, termed SE-COTR (segmentation based on coordinate transformer), is proposed to achieve accurate and real-time segmentation of green apples. The lightweight network MobileNetV2 is used as the backbone, combined with the constructed coordinate attention-based coordinate transformer module to enhance the focus on effective features. In addition, joint pyramid upsampling module is optimized for integrating multiscale features, making the model suitable for the detection and segmentation of target fruits with different sizes. Finally, in combination with the outputs of the function heads, the dynamic convolution operation is applied to predict the instance mask. In complex orchard environment with variable conditions, SE-COTR achieves a mean average precision of 61.6% with low complexity for green apple fruit segmentation at severe occlusion and different fruit scales. Especially, the segmentation accuracy for small target fruits reaches 43.3%, which is obviously better than other advanced segmentation models and realizes good recognition results. The proposed method effectively solves the problem of low accuracy and overly complex fruit segmentation models with the same color as the background and can be built in portable mobile devices to undertake accurate and efficient agricultural works in complex orchard.

SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative Adversarial Networks.

There is a growing consensus in computer vision that symmetric optical flow estimation constitutes a better model than a generic asymmetric one for its independence of the selection of source/target image. Yet, convolutional neural networks (CNNs), that are considered the de facto standard vision model, deal with the asymmetric case only in most cutting-edge CNNs-based optical flow techniques. We bridge this gap by introducing a novel model named SDOF-GAN: symmetric dense optical flow with generative adversarial networks (GANs). SDOF-GAN realizes a consistency between the forward mapping (source-to-target) and the backward one (target-to-source) by ensuring that they are inverse of each other with an inverse network. In addition, SDOF-GAN leverages a GAN model for which the generator estimates symmetric optical flow fields while the discriminator differentiates the "real" ground-truth flow field from a "fake" estimation by assessing the flow warping error. Finally, SDOF-GAN is trained in a semi-supervised fashion to enable both the precious labeled data and large amounts of unlabeled data to be fully-exploited. We demonstrate significant performance benefits of SDOF-GAN on five publicly-available datasets in contrast to several representative state-of-the-art models for optical flow estimation.

EEG-Based Seizure detection using linear graph convolution network with focal loss.

BACKGROUND AND OBJECTIVES: Epilepsy is a clinical phenomenon caused by sudden abnormal and excessive discharge of brain neurons. It affects around 70 million people all over the world. Seizure detection from Electroencephalography (EEG) has achieved rapid development. However, existing methods often extract features from single channel EEG while ignoring the spatial relationship between different EEG channels. To fill this gap, a novel seizure detection model based on linear graph convolution network (LGCN) was proposed to enhance the feature embedding of raw EEG signals during seizure and non-seizure periods. METHOD: Pearson correlation matrix of raw EEG signals was calculated to build the input graph of the graph neural network where the coefficients of the matrix models the spatial relations in EEG signals. The last softmax layer makes the final decision (seizure vs. non-seizure). In addition, focal loss was utilized to redefine the loss function of LGCN to deal with the data imbalance problem during seizure detection. RESULTS: Experiments are conducted on the CHB-MIT dataset. The seizure detection accuracy, specificity, sensitivity, F1 and Auc are 99.30%, 98.82%, 99.43%, 98.73% and 98.57% respectively. CONCLUSIONS: The proposed approach yields superior performance over the-state-of-the-art in seizure detection tasks on the CHB-MIT dataset. Our method works in an end-to-end manner and it does not need manually designed features. The ability to deal with imbalanced data is also attractive in real seizure detection scenarios where the duration of seizures is much shorter than the lasting time of non-seizure events.

Symmetric Deformable Registration via Learning a Pseudomean for MR Brain Images.

Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results.

Revealing False Positive Features in Epileptic EEG Identification.

Feature selection plays a vital role in the detection and discrimination of epileptic seizures in electroencephalogram (EEG) signals. The state-of-the-art EEG classification techniques commonly entail the extraction of the multiple features that would be fed into classifiers. For some techniques, the feature selection strategies have been used to reduce the dimensionality of the entire feature space. However, most of these approaches focus on the performance of classifiers while neglecting the association between the feature and the EEG activity itself. To enhance the inner relationship between the feature subset and the epileptic EEG task with a promising classification accuracy, we propose a machine learning-based pipeline using a novel feature selection algorithm built upon a knockoff filter. First, a number of temporal, spectral, and spatial features are extracted from the raw EEG signals. Second, the proposed feature selection algorithm is exploited to obtain the optimal subgroup of features. Afterwards, three classifiers including [Formula: see text]-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) are used. The experimental results on the Bonn dataset demonstrate that the proposed approach outperforms the state-of-the-art techniques, with accuracy as high as 99.93% for normal and interictal EEG discrimination and 98.95% for interictal and ictal EEG classification. Meanwhile, it has achieved satisfactory sensitivity (95.67% in average), specificity (98.83% in average), and accuracy (98.89% in average) over the Freiburg dataset.

MAMDA: Inferring microRNA-Disease associations with manifold alignment.

Uncovering disease-related microRNAs (miRNAs) by inferring miRNA-disease associations is of critical importance for understanding the pathogenesis of disease and carrying out treatment and prevention. Recently developed computational models for inferring miRNA-disease associations assume that functionally related miRNAs are associated with phenotypically similar diseases and hence infer miRNA-disease associations by using miRNA-miRNA and disease-disease similarities, which are concretely determined by mining existing biological resources. From the perspective of manifold learning, miRNA-miRNA similarities and disease-disease similarities determine a low-dimensional manifold for miRNAs and diseases, respectively, and the basic assumption of current computational models is equivalent to consistency between the manifold structures of miRNA and disease. In this paper, we propose a novel microRNA-disease inference framework (MAMDA) that explicitly takes advantage of this consistency property and infers miRNA-disease associations by aligning the manifold structure of miRNA with that of disease together with supervision of experimentally verified miRNA-disease associations. Based on three aspects, experimental results show that the proposed framework outperforms several representative state-of-the-art techniques. First, AUC values using k-fold cross-validation indicate that our method acquires more reliable predictions than four classical techniques (HGIMDA, HDMP, RLSMDA, and NCPMDA). Second, 48/48 predicted associations between miRNAs and breast cancer are validated with the HMDD and dbDEMC to show the effectiveness of predicting isolated diseases with unknown miRNAs. Third, two case studies of colon neoplasms and lung neoplasms validate the superior accuracy of MAMDA, with 48/50 and 48/50 predicted associations in the HMDD and dbDEMC, respectively.