CRIECNN: Ensemble convolutional neural network and advanced feature extraction methods for the precise forecasting of circRNA-RBP binding sites

Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is crucial in circRNA research. Existing prediction models suffer from limited availability and accuracy, necessitating advanced approaches. In this study, we propose CRIECNN (Circular RNA-RBP Interaction predictor using an Ensemble Convolutional Neural Network), a novel ensemble deep learning model that enhances circRNA-RBP binding site prediction accuracy. CRIECNN employs advanced feature extraction methods and evaluates four distinct sequence datasets and encoding techniques (BERT, Doc2Vec, KNF, EIIP). The model consists of an ensemble convolutional neural network, a BiLSTM, and a self-attention mechanism for feature refinement. Our results demonstrate that CRIECNN outperforms state-of-the-art methods in accuracy and performance, effectively predicting circRNA-RBP interactions from both full-length sequences and fragments. This novel strategy makes an enormous advancement in the prediction of circRNA-RBP interactions, improving our understanding of circRNAs and their regulatory roles.

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides

Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint's GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework

Autophagy is a primary mechanism for maintaining cellular homeostasis. The synergistic actions of autophagy-related (ATG) proteins strictly regulate the whole autophagic process. Therefore, accurate identification of ATGs is a first and critical step to reveal the molecular mechanism underlying the regulation of autophagy. Current computational methods can predict ATGs from primary protein sequences, but owing to the limitations of algorithms, significant room for improvement still exists. In this research, we propose EnsembleDL-ATG, an ensemble deep learning framework that aggregates multiple deep learning models to predict ATGs from protein sequence and evolutionary information. We first evaluated the performance of individual networks for various feature descriptors to identify the most promising models. Then, we explored all possible combinations of independent models to select the most effective ensemble architecture. The final framework was built and maintained by an organization of four different deep learning models. Experimental results show that our proposed method achieves a prediction accuracy of 94.5 % and MCC of 0.890, which are nearly 4 % and 0.08 higher than ATGPred-FL, respectively. Overall, EnsembleDL-ATG is the first ATG machine learning predictor based on ensemble deep learning. The benchmark data and code utilized in this study can be accessed for free at https://github.com/jingry/autoBioSeqpy/tree/2.0/examples/EnsembleDL-ATG.

STL-decomposition ensemble deep learning models for daily reservoir inflow forecast for hydroelectricity production

Accurate reservoir inflow forecasting is crucial for efficient water management. In this study, different deep learning models, including Dense, Long short-term memory (LSTM), and one-dimensional convolutional neural networks (Conv1D), were used to build ensembles. Seasonal-trend decomposition using loess (STL) was applied to decompose reservoir inflows and precipitations into random, seasonal, and trend components. Seven ensemble models, namely STL-Dense, STL-Conv1D, STL-LSTM, STL-Dense-LSTM-Conv1D, STL-Dense multivariate, STL-LSTM multivariate, and STL-Conv1D multivariate, were proposed and evaluated using daily inflows and precipitation decomposed data from the Lom Pangar reservoir from 2015 to 2020. Evaluation metrics, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Nash Sutcliff Efficiency (NSE), were applied to assess model performance. Results showed that the STL-Dense multivariate model was the best ensemble among the thirteen models with MAE of 14.636 m³/s, RMSE of 20.841 m³/s, MAPE of 6.622%, and NSE of 0.988. These findings stress the importance of considering multiple inputs and models for accurate reservoir inflow forecasting and optimal water management. Not all ensemble models were good for Lom pangar inflow forecast as the Dense, Conv1D, and LSTM models performed better than their proposed STL monovariate ensemble models.

PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

Background and Objective

In the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures.

Methods

Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several transfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes.

Results

PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases.

Conclusion

The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.

SENSDeep: An Ensemble Deep Learning Method for Protein-Protein Interaction Sites Prediction

PURPOSE

The determination of which amino acid in a protein interacts with other proteins is important in understanding the functional mechanism of that protein. Although there are experimental methods to detect protein-protein interaction sites (PPISs), these are costly, time-consuming, and require expertise. Therefore, many computational methods have been proposed to accelerate this type of research, but they are generally insufficient to predict PPISs accurately. There is a need for development in this field.

METHODS

In this study, we introduce a new PPISs prediction method. This method is a sequence-based Stacking ENSemble Deep (SENSDeep) learning method that has an ensemble learning model including the models of RNN, CNN, GRU sequence to sequence (GRUs2s), GRU sequence to sequence with an attention layer (GRUs2satt) and a multilayer perceptron. Two embedded features, secondary structure, and protein sequence information are added to the training data set in addition to twelve existing features to improve the prediction performance of the method.

RESULTS

SENSDeep trained on the training data set without two extra features obtains a better performance on some of the independent testing data sets than that of the other methods in the literature, especially on scoring metrics of sensitivity, F1, MCC, and AUPRC, having increments up to 63.5%, 19.3%, 18.5%, 11.4%, respectively. It is shown that the added extra features improve the performance of the method by having almost the same performance with less data as the method trained on the data set without these added features. On the other hand, different sizes of the sliding window are tried on the data sets and an optimal sliding window size for SENSDeep is found. Moreover, SENSDeep has also been compared to structure-based methods. Some of these methods have been found to perform better. Using SENSDeep obtained by training with both training data sets, PPISs prediction examples of various proteins that are not in these training data sets are also presented. Furthermore, execution times for SENSDeep and its submodels are shown.

AVAILABILITY AND IMPLEMENTATION

https://github.com/enginaybey/SENSDeep.

Ensemble deep learning model for predicting anterior cruciate ligament tear from lateral knee radiograph

OBJECTIVE

To develop an ensemble deep learning model (DLM) predicting anterior cruciate ligament (ACL) tears from lateral knee radiographs and to evaluate its diagnostic performance.

MATERIALS AND METHODS

In this study, 1433 lateral knee radiographs (661 with ACL tear confirmed on MRI, 772 normal) from two medical centers were split into training (n = 1146) and test sets (n = 287). Three single DLMs respectively classifying radiographs with ACL tears, abnormal lateral femoral notches, and joint effusion were developed. An ensemble DLM predicting ACL tears was developed by combining the three DLMs via stacking method. The sensitivities, specificities, and area under the receiver operating characteristic curves (AUCs) of the DLMs and three radiologists were compared using McNemar test and Delong test. Subgroup analysis was performed to identify the radiologic features associated with the sensitivity.

RESULTS

The sensitivity, specificity, and AUC of the ensemble DLM were 86.8% (95% confidence interval [CI], 79.9-92.0%), 89.4% (95% CI, 83.4-93.8%), and 0.927 (95% CI, 0.891-0.954), achieving diagnostic performance comparable with that of a musculoskeletal radiologist (P = 0.193, McNemar test; P = 0.131, Delong test). The AUC of the ensemble DLM was significantly higher than those of non-musculoskeletal radiologists (P = 0.043, P < 0.001). The sensitivity of the DLM was higher than that of the radiologists in the absence of an abnormal lateral femoral notch or joint effusion.

CONCLUSION

The diagnostic performance of the ensemble DLM in predicting lateral knee radiographs with ACL tears was comparable to that of a musculoskeletal radiologist.

A predictive framework in healthcare: Case study on cardiac arrest prediction

Data-driven healthcare uses predictive analytics to enhance decision-making and personalized healthcare. Developing prognostic models is one of the applications of predictive analytics in medical environments. Various studies have used machine learning techniques for this purpose. However, there is no specific standard for choosing prediction models for different medical purposes. In this paper, the ISAF framework was proposed for choosing appropriate prediction models with regard to the properties of the classification methods. As one of the case study applications, a prognostic model for predicting cardiac arrests in sepsis patients was developed step by step through the ISAF framework. Finally, a new modified stacking model produced the best results. We predict 85 % of heart arrest cases one hour before the incidence (sensitivity> = 0.85) and 73 % of arrest cases 25 h before the occurrence (sensitivity> = 0.73). The results indicated that the proposed prognostic model has significantly improved the prediction results compared to the two standard systems of APACHE II and MEWS. Furthermore, compared to previous research, the proposed model has extended the prediction interval and improved the performance criteria.

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction

Background

Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA–protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA–protein interactions.

Results

In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA–protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA–protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA–protein networks of Mus musculus successfully.

Conclusions

In general, our proposed method EDLMFC improved the accuracy of ncRNA–protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04069-9.

Design ensemble deep learning model for pneumonia disease classification

With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).

Ensemble deep learning with multi-objective optimization for prognosis of rotating machinery

With the emerging of Internet of Things and smart sensing techniques, enormous monitoring data has been collected by prognostics and health management (PHM) systems. Predicting the Remaining useful life (RUL) of mechanical components from monitoring data has always been a challenging task in many industries, yet determining RUL accurately is identified as one of the most demanded outcomes of PHM systems. In this study, an ensemble deep learning with multi-objective optimization (EDL-MO) method is proposed for RUL prediction. A novel ensemble deep learning algorithm for RUL prediction is designed by combining accuracy and diversity. By introducing the diversity, uncorrelated error is produced in each individual iteration, and performance of prediction will be improved by evolving deep networks. The presented EDL-MO employs evolutionary optimization to optimize the two conflicting objectives, that is, diversity and accuracy. To validate the proposed algorithm, bearing run-to-failure experiments were carried out under constant load. The vibration signals are recorded and utilized to predict the RUL by using the proposed EDL-MO method, as well as other existing methods for performance comparison. The effectiveness and superiority of EDL-MO are analyzed, which outperforms the current algorithms in predicting RUL on rotation machineries.

econvRBP: Improved ensemble convolutional neural networks for RNA binding protein prediction directly from sequence

RNA binding proteins (RBPs) determine RNA process from synthesis to decay, which play a key role in RNA transport, translation and degradation. Therefore, exploring RBPs' function from the amino acid sequence using computational methods has become one of the momentous topics in genome annotation. However, there still have some challenges: (1) shallow feature: Although the sequence determines structure is self-evident, it is difficult to analyze the essential features from simple sequence. (2) Poorly understand: feature-based prediction methods mainly emphasize feature extraction, while in-depth understanding of protein mysteries limits the application of feature engineering. (3) Feature fusion: multi-feature fusion is often used, but the features are not well integrated. In view of these challenges, we propose a novel ensemble convolutional neural network (econvRBP) to predict RBPs. In order to capture the local and global features of RNA binding proteins simultaneously, first of all, One Hot and Conjoint Triad encoding methods are used to transform amino acid sequence into local and global features, respectively. After that the local and global features are combined for further high-level feature extraction using convolutional neural networks. Some experiments are constructed to evaluate our method with 10-fold cross validation and the results show that it has achieved the best performance among all the predictors so far. We correctly predicted 99% of 2875 RBPs and 99% of 6782 non-RBPs with accuracy of 0.99. In addition, the datasets provided by RBPPred are also used to validate our models with an accuracy of 0.87. These results indicate that the econvRBP is the most excellent method at present, and will provide reliable guidance for the detection of RBPs. econvRBP is available at http://47.100.203.218:3389/home.html/.