Precise Prediction Research Articles

A novel high applicability link prediction based on biased random walks

Complex systems are prevalent in nature, and link prediction is crucial for analyzing these systems. This method has gained attention for its ability to uncover various irregular movements with underlying similarities. While individual particle behavior is unpredictable, the collective behavior of large groups can be forecasted more accurately. The variability at lower scales vs universal similarities at higher scales is often overlooked. To address this, we investigate the random walk process on directed networks at a global scale. We propose an improved link prediction method using biased random walks to enhance accuracy and applicability. We first define out-degree neighbors as valid transmission options to prevent conflicts with in-degree paths. We then analyze how out-degrees and in-degrees affect particle transition probabilities, guiding particles toward high out-degrees and low in-degrees for a sustainable process. Finally, we use particle stabilization probabilities at different nodes as a similarity measure for predicting potential connections. Our method improves prediction precision and practicality, as validated by experiments on nine real-world networks, showing significant gains in accuracy and AUC scores.

Prediction of the potentially suitable areas of Eucommia ulmoides Oliver in China under climate change based on optimized Biomod2 and MaxEnt models

Eucommia ulmoides Oliver is a medicinal plant of significant economic importance. Its cortex has been employed for centuries to alleviate various conditions such as lumbar pain, knee pain, and osteoporosis. Additionally, E. ulmoides possesses substantial industrial value. With the growing demand for this medicinal herb, ensuring its sustainable supply has become imperative. Climate change has caused habitat restrictions or migration of medicinal plants. Therefore, predicting the impact of climate change on the distribution of E. ulmoides is crucial for its conservation and sustainable use. This study evaluated the potential distribution of E. ulmoides across China under various climate change scenarios since the last interglacial period by modeling suitable areas based on 257 distribution records and 19 major environmental factors related to E. ulmoides. The model selection process initially identified the MaxEnt model as the most suitable. The optimized MaxEnt model, with RM = 2.0 and FC = LQHPT settings, generated the most precise predictions. Results indicate that the minimum temperature of the coldest month, annual mean temperature, and annual precipitation significantly affect the distribution of E. ulmoides. Under current environmental conditions, highly suitable areas for E. ulmoides are found in Southwest and Southeast China, with a total suitable habitat area of 23.12 × 104 km2. However, the range of suitable habitat has shifted due to global warming’s negative impact. Under different climate scenarios, suitable areas for E. ulmoides have either increased or decreased, with expansions primarily in high-latitude regions. Future climate scenarios predict shifts in the centroid of suitable E. ulmoides habitat towards Yichang City in Hubei Province. The findings of this study support the development, artificial cultivation, and conservation of E. ulmoides resources.

Nonlinear Deep Learning Framework for Precise Sentiment Classification in Textual Data

The study presents the Nonlinear Deep Learning Framework for Precise Sentiment Classification (NPSC), focusing on improving sentiment analysis in text data. The model addresses the challenge of capturing complex and hidden sentiment patterns using a combination of BERT embeddings, Bidirectional LSTM (BiLSTM), Convolutional Neural Networks (CNN), and an attention mechanism. This setup helps in identifying subtle relationships within text, enabling more precise sentiment predictions. NPSC tackles common issues faced by existing models that struggle with detailed text analysis. Using the IMDB movie review dataset, the approach relies on BERT to convert words into dense vectors, BiLSTM for capturing context from both directions, and CNN to extract key local features. An attention layer highlights important words, refining the sentiment detection process. The integration of these components in a nonlinear fusion layer allows for enhanced feature interaction, leading to accurate classification through a final softmax layer. The findings show that NPSC performs better across metrics like accuracy, precision, recall, and F1-score. Ablation studies reveal the impact of each component, demonstrating their role in enhancing the model’s effectiveness. Error analysis shows how NPSC manages mixed sentiments and challenging text patterns. The study highlights NPSC’s potential as a reliable method for sentiment analysis, contributing to better handling of complex textual data.

Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation study.

One of the most promising approaches for early and more precise disease prediction and diagnosis is through the inclusion of proteomics data augmented with clinical data. Clinical proteomics data is often characterized by its high dimensionality and extremely limited sample size, posing a significant challenge when employing machine learning techniques for extracting only the most relevant information. Although there is a wide array of statistical techniques and numerous analysis pipelines employed in proteomics data analysis, it is unclear which of these methods produce the most efficient, reproducible, and clinically meaningful results. In this study, we compared 9 unique analysis schemes comprised of different machine learning and dimensionality reduction methods for the analysis of simulated proteomics data consisting of 1317 proteins measured in 26 subjects (i.e., 13 controls and 13 cases). In scenarios where the sample size is extremely small (i.e., n < 30), all schemes resulted in an exceptionally high level of performance metrics, indicating potential overfitting. While performance metrics did not exhibit significant differences across schemes, the set of proteins selected to be discriminatory between groups demonstrated a substantial level of heterogeneity. However, despite heterogeneity in the selected proteins, their biological pathways and genetic diseases exhibited similarities. A sensitivity analysis conducted using varying sample sizes indicated that the stability of a set of selected biomarkers improves with larger sample sizes within a scheme. When the aim of the study is to identify a statistical model that best distinguishes between cohort groups using proteomics data and to uncover the biological pathways and disorders common among the selected proteins, the majority of widely used analysis pipelines perform similarly. However, if the main objective is to pinpoint a set of selected proteins that wield significant influence in discriminating cohort groups and utilize them for subsequent investigations, meticulous consideration is necessary when opting for statistical models, due to the possibility of heterogeneity in the sets of selected proteins.

Self-assembly of lamellae by elastocapillarity in the presence of gravity: experiments and modeling

Abstract Elastocapillarity describes the self-organization of elastic structures under liquid surface forces, for applications to micro- and nano scale self-assembly. It has recently become evident that precise prediction of elastocapillary self-assembly is challenged by the various phenomena arising due to fluid flow. In this study, we present experiments and a corresponding reduced-order model for the dynamic coalescence of flexible lamellae arrays. Vertical arrays of elastomeric lamellae attached to a substrate at the bottom are fabricated using 3D printed molds. The lamellae have sub-mm thickness and a few millimeters height, a size scale that cannot be achieved by traditional microfabrication. The gaps between the elastic lamellae are filled with liquid while the tips of the lamellae, which are at the top with respect to gravity, are initially fixed, then they are released which leads to their spontaneous bending and coalescence. We study the cluster size as a function of system variables both experimentally and numerically. The solid lamellae are modeled as discrete rigid elements connected by torsional springs to the substrate, while the fluid flow uses lubrication theory and capillarity. Importantly, the model incorporates the gravitational forces to effectively capture the systems' behavior ranging from the microscale with negligible gravity effect to the macroscale where gravity significantly influences the dynamics. We further derive a linearized continuum model from our nonlinear formulation, providing an analytical scaling law for the lamellae cluster size. This work is relevant for new polymorphic fins and lamellae devices, capillary self-folding and origami.

Remaining Useful Life Prediction based on Multisource Domain Transfer and Unsupervised Alignment

Transfer learning （TL） enhances remaining useful life (RUL) predictions by addressing data scarcity and operational challenges. Nonetheless, when a significant disparity in degradation data distribution exists between source and target domains, single-source domain TL may lead to misleading or negative transfer. Multisource domain TL partially mitigates these issues but fails to account for substantial discrepancies in feature-label correlations, impairing RUL prediction accuracy. To cope with this problem, we propose a multisource domain unsupervised adaptive learning method powered by a temporal convolutional network. Using a multilinear conditioning strategy, we combine degradation data and subregion labels to construct input characteristics for the domain discriminator. Additionally, we design a feature extractor that produces label-related features invariant across domains, thereby enhancing prediction precision. We evaluate our method using the publicly available C-MAPSS degradation dataset, demonstrating its effectiveness through a case study and ablation experiments.

Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas

AbstractPredicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimizing human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall implies the crucial nature of accurate rainfall prediction. Precise rainfall forecasts can facilitate early preparedness for disasters associated with heavy rains, enabling the public and government to take necessary precautions. In the North-Western Himalayas, where meteorological data are limited, the need for improved accuracy in traditional modeling methods for rainfall forecasting is pressing. To address this, our study proposes the application of advanced machine learning (ML) algorithms, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), and k-nearest neighbour (KNN) along with various deep learning (DL) algorithms such as long short-term memory (LSTM), bi-directional LSTM, deep LSTM, gated recurrent unit (GRU), and simple recurrent neural network (RNN). These advanced techniques hold the potential to significantly improve the accuracy of rainfall prediction, offering hope for more reliable forecasts. Additionally, time series techniques, including autoregressive integrated moving average (ARIMA) and trigonometric, Box-Cox transform, arma errors, trend, and seasonal components (TBATS), are proposed for predicting rainfall across the altitudinal gradients of India’s North-Western Himalayas. This approach can potentially revolutionise how we approach rainfall forecasting, ushering in a new era of accuracy and reliability. The effectiveness and accuracy of the proposed algorithms were assessed using meteorological data obtained from six weather stations at different elevations spanning from 1980 to 2021. The results indicate that DL methods exhibit the highest accuracy in predicting rainfall, as measured by the root mean squared error (RMSE) and mean absolute error (MAE), followed by ML algorithms and time series techniques. Among the DL algorithms, the accuracy order was bi-directional LSTM, LSTM, RNN, deep LSTM, and GRU. For the ML algorithms, the accuracy order was ANN, KNN, SVR, and RF. These findings suggest that altitude significantly affects the accuracy of the models, highlighting the need for additional weather stations in this mountainous region to enhance the precision of rainfall prediction.

Exosomal integrins in tumor progression, treatment and clinical prediction (Review).

Integrins are a large family of cell adhesion molecules involved in tumor cell differentiation, migration, proliferation and neovascularization. Tumor cell‑derived exosomes carry a large number of integrins, which are closely associated with tumor progression. As crucial mediators of intercellular communication, exosomal integrins have gained attention in the field of cancer biology. The present review examined the regulatory mechanisms of exosomal integrins in tumor cell proliferation, migration and invasion, and emphasized their notable roles in tumor initiation and progression. The potential of exosomal integrins as drug delivery systems in cancer treatment was explored. Additionally, the potential of exosomal integrins in clinical tumor prediction was considered, while summarizing their applications in diagnosis, prognosis assessment and treatment response prediction. Thus, the present review aimed to provide guidance and insights for future basic research and the clinical translation of exosomal integrins. The study of exosomal integrins is poised to offer new perspectives and methods for precise cancer treatment and clinical prediction.

Adadelta-CSA: Adadelta-Chameleon Swarm Algorithm for EEG-Based Epileptic Seizure Detection

Epilepsy is referred to as a neurological disorder, which is detected via examination and manual comprehension of Electroencephalogram (EEG) signals. In deep learning schemes, various enhancements have emerged to efficiently address complex issues by end-to-end learning. The major objective of this research is to propose a new seizure detection approach from EEG signals using a deep learning-based classification technique. The pre-processing is the initial stage, where denoising is performed using a Short-Time Fourier Transform (STFT). Subsequently, the statistical features, time-domain features and spectral features are extracted from the pre-processed signal. Finally, an efficient optimization approach, named Adadelta-Chameleon Swarm Algorithm (Adadelta-CSA), is proposed and employed to train Deep Neural Network (DNN) to carry out the precise seizure prediction. Here, the integration of the Adadelta concept in the Chameleon Swarm Algorithm (CSA) has resulted in Adadelta-CSA. At last, the performance of the Adadelta-CSA scheme-based DNN is compared with the existing techniques by considering accuracy, sensitivity and specificity, and it is found to produce better values of 0.951, 0.966, and 0.935, respectively.

Medium and long-term regional water demand prediction using Harris hawks optimisation-backpropagation neural network model.

Precise water demand prediction is essential for the efficient allocation and rational utilisation of regional water resources. This study addressed the challenge associated with medium and long-term water demand prediction by introducing a novel coupled model, HHO-BPNN (Harris Hawks Optimisation-Backpropagation Neural Network). Principal component analysis was employed to reduce the dimensionality of potential water demand factors. The performance of the forecasting models was compared through mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2). The findings indicated that the HHO-BPNN outperformed traditional methods, such as BPNN, support vector machines, and grey prediction model. The study utilised the sliding window method to predict water demand for the next 1, 3, and 5 years for five prefecture-level cities in northern Jiangsu Province, China. High prediction accuracy was achieved across various categories of water demand (agricultural, industrial, domestic, and ecological), with the overall accuracy being impressive at 97%. Additionally, the forecasts aligned well with local developmental plans, suggesting practical applicability for urban planning. This study elucidates the key drivers impacting water demand, providing an effective tool for regional water demand forecasting, facilitating efficient and precise water management and decision-making in the future.

Comprehensive Assessment of Soil Erosion using USLE and Sustainable Management Strategies

Continuous soil erosion from all parts of world through water and winds have become a severe concerns for soil scientists and geologists due to its adverse effect on soil productivity, water bodies siltation and deterioration of water quality. Therefore, it becomes most important to locate erosion-prone regions of any landscape and take the best measures to solve the issues of erosion. USLE and its modified models i.e. RUSLE (Revised Universal Soil Loss Equation) versions and MUSLE (Modified Universal Soil Loss Equation) are most commonly used for estimation of soil erosion. In India, soil erosion is a major serious concern which varies in its intensity depending on the country's different agro-climatic zones. USLE as well known model for evaluating soil deterioration is used in this review to provide an understanding of its parameters and how this approach is useful in soil erosion assessment. In addition, an overview of the current situation of erosion status in India which is being altered by regional variations like climate and land uses. Literature also dictates and discusses the potential advantages of better erosion control approaches, such as increased agricultural output and long-term land sustainability and looks at what might be possible in the future if the things get managed. The study also looks at crop selection and land use systems, emphasizing how crucial it is to choose crops that are suitable for the land vulnerability to erosion in order to prevent the further deterioration. Lastly, it emphasize on a thorough explanation of the USLE's parameters which are essential for precise erosion prediction including slope factors, soil erodibility, rainfall erosivity and conservation techniques. The study summarizes the knowledge on soil erosion in India and the necessity of customized, area-specific soil conservation plans to guarantee long-term agricultural outputs and sustainable agricultural goals.

NNLL-fast 2.0: Coloured sparticle production at the LHC run 3 with $\sqrt{S}$ = 13.6 TeV

We report on updated precision predictions for total cross sections of coloured supersymmetric particle production at the LHC with a centre-of-mass energy of \sqrt{S} = 13.6S=13.6 TeV, computed with the modern PDF4LHC21 set. The cross sections are calculated at an approximated NNLO accuracy in QCD and contain corrections from the threshold resummation of soft-gluon emission up to NNLL accuracy as well as Coulomb-gluon contributions including bound-state terms. The corrections are found to increase the cross sections and reduce the theoretical uncertainty as compared to the best available fixed-order calculations. These predictions constitute the state-of-the-art calculations and update the existing results for \sqrt{S} = 13S=13 TeV. We make our new results publicly available in the version 2.0 update to the code package .

Enhancing Lung and Breast Cancer Screening with Advanced AI and Image Processing Techniques

This research investigates the application of CNNs for diagnostics improvements in lung and breast cancers based on AI image classification approaches. Using the datasets with 15,000 images describing lung cancer and 10,000 images describing cases of breast cancer, the models showed high performance: 90% for lung cancer and 99% for breast cancer classification. The descriptive analysis pointed out different features in imaging, such as dense tissue structure and irregular cell patterns; the models successfully identified these. The findings underlined the vital role that AI could play in assisting radiologists by delivering preliminary analysis, triaging high-risk cases, and leading to early cancer detection. Essential challenges were highlighted: ethical considerations concerning patients' privacy and AI algorithms' transparency. The limitation of the dataset diversity resulted in the conclusion that only broader data can ensure good generalization in various clinical settings. They recommended integrating the AI tool with clinical workflow and also called for training radiologists for effectiveness. Future research directions include real-time imaging and patient data integration for comprehensive diagnostic support and multi-modal approaches that combine imaging with genomics for more precise predictions. This leads to a more personalized cancer diagnosis and treatment plan, thus ultimately improving the results of the patients. This research, therefore, underlines the transformative capabilities of AI and image processing in modernizing cancer screening and diagnostics toward more accurate and efficient healthcare practices.

Utilising retinal phenotypes to predict cerebrovascular disease and detect related risk factors in multi-ethnic populations: a narrative review.

Cerebrovascular diseases (CBVDs) are a major cause of mortality and disability, with significant ethnic variations suggesting specific risk factors. Early detection of these risk factors is critical, and retinal imaging offers a non-invasive method to achieve this. Retinal phenotypes can serve as early markers for CBVDs. Racial differences in retinal and vascular morphometric characteristics have been described. Examining these characteristics in the context of racial differences could improve early detection and targeted interventions for CBVDs. This review discusses the role of retinal imaging in predicting CBVDs and highlights the importance of ethnicity-specific approaches. Understanding ethnic variations in retinal features can enhance the precision of CBVD prediction and enable personalised treatment strategies.

Quantum machine learning enhanced laser speckle analysis for precise speed prediction.

Laser speckle contrast imaging (LSCI) is an optical technique used to assess blood flow perfusion by modeling changes in speckle intensity, but it is generally limited to qualitative analysis due to difficulties in absolute quantification. Three-dimensional convolutional neural networks (3D CNNs) enhance the quantitative performance of LSCI by excelling at extracting spatiotemporal features from speckle data. However, excessive downsampling techniques can lead to significant information loss. To address this, we propose a hybrid quantum-classical 3D CNN framework that leverages variational quantum algorithms (VQAs) to enhance the performance of classical models. The proposed framework employs variational quantum circuits (VQCs) to replace the 3D global pooling layer, enabling the model to utilize the complete 3D information extracted by the convolutional layers for feature integration, thereby enhancing velocity prediction performance. We perform cross-validation on experimental LSCI speckle data and demonstrate the superiority of the hybrid models over their classical counterparts in terms of prediction accuracy and learning stability. Furthermore, we evaluate the models on an unseen test set and observe that the hybrid models outperform the classical models with up to 14.8% improvement in mean squared error (MSE) and up to 26.1% improvement in mean absolute percentage error (MAPE) evaluation metrics. Finally, our qualitative analysis shows that the hybrid models offer substantial improvements over classical models in predicting blood flow at both low and high velocities. These results indicate that the hybrid models possess more powerful learning and generalization capabilities.

Impact of water physicochemical properties on the survival of Oncomelania hupensis snails, the intermediate host of Schistosoma japonicum.

Schistosoma japonicum, the causative agent of schistosomiasis, heavily relies on its single intermediate host, the Oncomelania hupensis snail, for its life cycle. Controlling these snails effectively plays a pivotal role in curbing the transmission and prevalence of this disease. While prior research has extensively investigated the impact of environmental factors such as temperature and vegetation on snail survival, growth, and reproduction, the contribution of water physicochemical properties has been notably underexplored. This study presents laboratory experiments designed to comprehensively explore the influence of water physicochemical properties on snail survival, offering valuable insights into environmental factors for more precise predictions of snail distribution. We meticulously conducted laboratory snail survival experiments using water from different sources (river water/tap water), and employed a statistical approach amalgamating principal component analysis with Cox regression to preliminarily investigate the effects of different water physicochemical properties on the survival of snails. Our analysis indicates that after a 6-month laboratory snail survival experiment, the survival rate in the tap water group was significantly higher than that in the river water group for infected snails (χ2 = 7.74, p = 0.005), while the difference in survival rates for non-infected snails was not statistically significant (χ2 = 0.61, p = 0.434). The Principal Component-Cox regression analysis revealed that in the infected snail group, total phosphorus, pH value, five-day biochemical oxygen demand, conductivity, and nitrite were protective factors for snail survival, while phosphate and total nitrogen were risk factors. In the non-infected snail group, total phosphorus, pH value, five-day biochemical oxygen demand, conductivity, and nitrite were protective factors for snail survival, and total nitrogen, ammonia nitrogen, phosphate, and nitrate were risk factors. This study underscores the substantial impact of water quality's physicochemical properties on snail survival. The effects of water quality on snails are complex, and maintaining an appropriate level of organic matter content and controlling the pH value at a weak alkalescency level prove beneficial for snail survival. These findings hold significant promise for advancing our understanding of snail-borne diseases and optimizing control strategies.

ELPGPT: Large Language Models Enhancing Link Prediction in Electrical Knowledge Graph

Graphs provide essential means for organizing and analyzing complex equipment data. Although link prediction techniques have been widely applied to enhance knowledge graphs, existing methods still show room for improvement in accuracy, especially when dealing with sparse data. To address this, we introduce ELPGPT (Large Language Models Enhancing Link Prediction in Electrical Equipment Knowledge Graph), a novel approach that integrates large language models into link prediction to enhance the accuracy of relation prediction within electrical equipment knowledge graphs. The core of the ELPGPT method lies in the combination of large language models with traditional knowledge graph link prediction techniques. By leveraging the deep semantic understanding capabilities of large language models, this method effectively extracts relational features and enhances the handling of sparse data. Additionally, we employ a Retrieval-Augmented Generation (RAG) approach, which, by integrating external data sources, further enhances the precision and relevance of predictions. Experiments on the Electrical Equipment Knowledge Graph (EEKG) demonstrate that ELPGPT significantly improves performance across several metrics, including Hit@k, Mean Rank (MR), and Mean Reciprocal Rank (MRR). These results validate the effectiveness and potential applications of this method in the domain of link prediction for electrical equipment knowledge graphs.

A novel hybrid DNN-RNN framework for precise crop yield prediction

A novel hybrid DNN-RNN framework for precise crop yield prediction

Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction

This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL predictions.The model’s efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and stability.The model’s robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model’s exceptional generalizability and potential for real-world applications in electric vehicle battery management.

DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction

In recent years, numerous studies have highlighted the pivotal importance of miRNAs in personalized healthcare, showcasing broad application prospects. miRNAs hold significant potential in disease diagnosis, prognosis assessment, and therapeutic target discovery, making them an integral part of precision medicine. They are expected to enable precise disease subtyping and risk prediction, thereby advancing the development of precision medicine. GNNs, a class of deep learning architectures tailored for graph data analysis, have greatly facilitated the advancement of miRNA-disease association prediction algorithms. However, current methods often fall short in leveraging network node information, particularly in utilizing global information while neglecting the importance of local information. Effectively harnessing both local and global information remains a pressing challenge. To tackle this challenge, we propose an innovative model named DGNMDA. Initially, we constructed various miRNA and disease similarity networks based on authoritative databases. Subsequently, we creatively design a dual heterogeneous graph neural network encoder capable of efficiently learning feature information between adjacent nodes and similarity information across the entire graph. Additionally, we develop a specialized fine-grained multi-layer feature interaction gating mechanism to integrate outputs from the neural network encoders to identify novel associations connecting miRNAs with diseases. We evaluate our model using 5-fold cross-validation and real-world disease case studies, based on the HMDD V3.2 dataset. Our method demonstrates superior performance compared to existing approaches in various tasks, confirming the effectiveness and potential of DGNMDA as a robust method for predicting miRNA-disease associations.