SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data

Integrating CNN and Bi-LSTM for protein succinylation sites prediction based on Natural Language Processing technique

Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regulatory role in cellular processes. Dysregulated succinylation has been implicated in the onset and progression of various diseases, including liver, cardiac, pulmonary, and neurological disorders. However, identifying succinylation sites through experimental methods is often labor-intensive, costly, and technically challenging. To address this, we introduce an approach called CbiLSuccSite, that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) networks for the accurate prediction of protein succinylation sites. Our approach employs a word embedding layer to encode protein sequences, enabling the automatic learning of intricate patterns and dependencies without manual feature extraction. In 10-fold cross-validation, CBiLSuccSite achieved superior predictive performance, with an Area Under the Curve (AUC) of 0.826 and a Matthews Correlation Coefficient (MCC) of 0.502. Independent testing further validated its robustness, yielding an AUC of 0.818 and an MCC of 0.53. The integration of CNN and Bi-LSTM leverages the strengths of both architectures, establishing CBiLSuccSite as an effective tool for protein language processing and succinylation site prediction. Our model and code are publicly accessible at: https://github.com/nuinvtnu/CBiLSuccSite.

A Deep Learning and PSSM Profile Approach for Accurate SNARE Protein Prediction

SNARE proteins play a pivotal role in membrane fusion and various cellular processes. Accurate identification of SNARE proteins is crucial for elucidating their functions in both health and disease contexts. This chapter presents a novel approach employing multiscan convolutional neural networks (CNNs) combined with position-specific scoring matrix (PSSM) profiles to accurately recognize SNARE proteins. By leveraging deep learning techniques, our method significantly enhances the accuracy and efficacy of SNARE protein classification. We detail the step-by-step methodology, including dataset preparation, feature extraction using PSI-BLAST, and the design of the multiscan CNN architecture. Our results demonstrate that this approach outperforms existing methods, providing a robust and reliable tool for bioinformatics research.

Choosing an appropriate somatic embryogenesis medium of carrot (Daucus carota L.) by data mining technology

INTRODUCTION

Developing somatic embryogenesis is one of the main steps in successful in vitro propagation and gene transformation in the carrot. However, somatic embryogenesis is influenced by different intrinsic (genetics, genotype, and explant) and extrinsic (e.g., plant growth regulators (PGRs), medium composition, and gelling agent) factors which cause challenges in developing the somatic embryogenesis protocol. Therefore, optimizing somatic embryogenesis is a tedious, time-consuming, and costly process. Novel data mining approaches through a hybrid of artificial neural networks (ANNs) and optimization algorithms can facilitate modeling and optimizing in vitro culture processes and thereby reduce large experimental treatments and combinations. Carrot is a model plant in genetic engineering works and recombinant drugs, and therefore it is an important plant in research works. Also, in this research, for the first time, embryogenesis in carrot (Daucus carota L.) using Genetic algorithm (GA) and data mining technology has been reviewed and analyzed.

MATERIALS AND METHODS

In the current study, data mining approach through multilayer perceptron (MLP) and radial basis function (RBF) as two well-known ANNs were employed to model and predict embryogenic callus production in carrot based on eight input variables including carrot cultivars, agar, magnesium sulfate (MgSO4), calcium dichloride (CaCl2), manganese (II) sulfate (MnSO4), 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), and kinetin (KIN). To confirm the reliability and accuracy of the developed model, the result obtained from RBF-GA model were tested in the laboratory.

RESULTS

The results showed that RBF had better prediction efficiency than MLP. Then, the developed model was linked to a genetic algorithm (GA) to optimize the system. To confirm the reliability and accuracy of the developed model, the result of RBF-GA was experimentally tested in the lab as a validation experiment. The result showed that there was no significant difference between the predicted optimized result and the experimental result.

CONCLUTIONS

Generally, the results of this study suggest that data mining through RBF-GA can be considered as a robust approach, besides experimental methods, to model and optimize in vitro culture systems. According to the RBF-GA result, the highest somatic embryogenesis rate (62.5%) can be obtained from Nantes improved cultivar cultured on medium containing 195.23 mg/l MgSO4, 330.07 mg/l CaCl2, 18.3 mg/l MnSO4, 0.46 mg/l 2,4- D, 0.03 mg/l BAP, and 0.88 mg/l KIN. These results were also confirmed in the laboratory.

S. Early detection of abiotic stress in plants through SNARE proteins using hybrid feature fusion model

Agriculture is the main source of livelihood for most of the population across the globe. Plants are often considered life savers for humanity, having evolved complex adaptations to cope with adverse environmental conditions. Protecting agricultural produce from devastating conditions such as stress is essential for the sustainable development of the nation. Plants respond to various environmental stressors such as drought, salinity, heat, cold, etc. Abiotic stress can significantly impact crop yield and development posing a major threat to agriculture. SNARE proteins play a major role in pathological processes as they are vital proteins in the life sciences. These proteins act as key players in stress responses. Feature extraction is essential for visualizing the underlying structure of the SNARE proteins in analyzing the root cause of abiotic stress in plants. To address this issue, we developed a hybrid model to capture the hidden structures of the SNAREs. A feature fusion technique has been devised by combining the potential strengths of convolutional neural networks (CNN) with a high dimensional radial basis function (RBF) network. Additionally, we employ a bi-directional long short-term memory (Bi-LSTM) network to classify the presence of SNARE proteins. Our feature fusion model successfully identified abiotic stress in plants with an accuracy of 74.6%. When compared with various existing frameworks, our model demonstrates superior classification results.

Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding

Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.

A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins

There is a great deal of importance to SNARE proteins, and their absence from function can lead to a variety of diseases. The SNARE protein is known as a membrane fusion protein, and it is crucial for mediating vesicle fusion. The identification of SNARE proteins must therefore be conducted with an accurate method. Through extensive experiments, we have developed a model based on graph-regularized k-local hyperplane distance nearest neighbor model (GHKNN) binary classification. In this, the model uses the physicochemical property extraction method to extract protein sequence features and the SMOTE method to upsample protein sequence features. The combination achieves the most accurate performance for identifying all protein sequences. Finally, we compare the model based on GHKNN binary classification with other classifiers and measure them using four different metrics: SN, SP, ACC, and MCC. In experiments, the model performs significantly better than other classifiers.

IAV-CNN: A 2D Convolutional Neural Network Model to Predict Antigenic Variants of Influenza A Virus

The rapid evolution of influenza viruses constantly leads to the emergence of novel influenza strains that are capable of escaping from population immunity. The timely determination of antigenic variants is critical to vaccine design. Empirical experimental methods like hemagglutination inhibition (HI) assays are time-consuming and labor-intensive, requiring live viruses. Recently, many computational models have been developed to predict the antigenic variants without considerations of explicitly modeling the interdependencies between the channels of feature maps. Moreover, the influenza sequences consisting of similar distribution of residues will have high degrees of similarity and will affect the prediction outcome. Consequently, it is challenging but vital to determine the importance of different residue sites and enhance the predictive performance of influenza antigenicity. We have proposed a 2D convolutional neural network (CNN) model to infer influenza antigenic variants (IAV-CNN). Specifically, we apply a new distributed representation of amino acids, named ProtVec that can be applied to a variety of downstream proteomic machine learning tasks. After splittings and embeddings of influenza strains, a 2D squeeze-and-excitation CNN architecture is constructed that enables networks to focus on informative residue features by fusing both spatial and channel-wise information with local receptive fields at each layer. Experimental results on three influenza datasets show IAV-CNN achieves state-of-the-art performance combining the new distributed representation with our proposed architecture. It outperforms both traditional machine algorithms with the same feature representations and the majority of existing models in the independent test data. Therefore we believe that our model can be served as a reliable and robust tool for the prediction of antigenic variants.

Identifying SNARE Proteins Using an Alignment-Free Method Based on Multiscan Convolutional Neural Network and PSSM Profiles

Background: SNARE proteins play a vital role in membrane fusion and cellular physiology and pathological processes. Many potential therapeutics for mental diseases or even cancer based on SNAREs are also developed. Therefore, there is a dire need to predict the SNAREs for further manipulation of these essential proteins, which demands new and efficient approaches. Methods: Some computational frameworks were proposed to tackle the hurdles of biological methods, which take plenty of time and budget to conduct the identification of SNAREs. However, the performances of existing frameworks were insufficiently satisfied, as they failed to retain the SNARE sequence order and capture the mass hidden features from SNAREs. This paper proposed a novel model constructed on the multiscan convolutional neural network (CNN) and position-specific scoring matrix (PSSM) profiles to address these limitations. We employed and trained our model on the benchmark dataset with fivefold cross-validation and two different independent datasets. Results: Overall, the multiscan CNN was cross-validated on the training set and excelled in the SNARE classification reaching 0.963 in AUC and 0.955 in AUPRC. On top of that, with the sensitivity, specificity, accuracy, and MCC of 0.842, 0.968, 0.955, and 0.767, respectively, our proposed framework outperformed previous models in the SNARE recognition task. Conclusions: It is truly believed that our model can contribute to the discrimination of SNARE proteins and general proteins.

Identification of the ubiquitin-proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network

The major mechanism of proteolysis in the cytosol and nucleus is the ubiquitin-proteasome pathway (UPP). The highly controlled UPP has an effect on a wide range of cellular processes and substrates, and flaws in the system can lead to the pathogenesis of a number of serious human diseases. Knowledge about UPPs provide useful hints to understand the cellular process and drug discovery. The exponential growth in next-generation sequencing wet lab approaches have accelerated the accumulation of unannotated data in online databases, making the UPP characterization/analysis task more challenging. Thus, computational methods are used as an alternative for fast and accurate identification of UPPs. Aiming this, we develop a novel deep learning-based predictor named "2DCNN-UPP" for identifying UPPs with low error rate. In the proposed method, we used proposed algorithm with a two-dimensional convolutional neural network with dipeptide deviation features. To avoid the over fitting problem, genetic algorithm is employed to select the optimal features. Finally, the optimized attribute set are fed as input to the 2D-CNN learning engine for building the model. Empirical evidence or outcomes demonstrates that the proposed predictor achieved an overall accuracy and AUC (ROC) value using 10-fold cross validation test. Superior performance compared to other state-of-the art methods for discrimination the relations UPPs classification. Both on and independent test respectively was trained on 10-fold cross validation method and then evaluated through independent test. In the case where experimentally validated ubiquitination sites emerged, we must devise a proteomics-based predictor of ubiquitination. Meanwhile, we also evaluated the generalization power of our trained modal via independent test, and obtained remarkable performance in term of 0.862 accuracy, 0.921 sensitivity, 0.803 specificity 0.803, and 0.730 Matthews correlation coefficient (MCC) respectively. Four approaches were used in the sequences, and the physical properties were calculated combined. When used a 10-fold cross-validation, 2D-CNN-UPP obtained an AUC (ROC) value of 0.862 predicted score. We analyzed the relationship between UPP protein and non-UPP protein predicted score. Last but not least, this research could effectively analyze the large scale relationship between UPP proteins and non-UPP proteins in particular and other protein problems in general and our research work might improve computational biological research. Therefore, we could utilize the latest features in our model framework and Dipeptide Deviation from Expected Mean (DDE) -based protein structure features for the prediction of protein structure, functions, and different molecules, such as DNA and RNA.

SNARER: new molecular descriptors for SNARE proteins classification

Background

SNARE proteins play an important role in different biological functions. This study aims to investigate the contribution of a new class of molecular descriptors (called SNARER) related to the chemical-physical properties of proteins in order to evaluate the performance of binary classifiers for SNARE proteins.

Results

We constructed a SNARE proteins balanced dataset, D128, and an unbalanced one, DUNI, on which we tested and compared the performance of the new descriptors presented here in combination with the feature sets (GAAC, CTDT, CKSAAP and 188D) already present in the literature. The machine learning algorithms used were Random Forest, k-Nearest Neighbors and AdaBoost and oversampling and subsampling techniques were applied to the unbalanced dataset. The addition of the SNARER descriptors increases the precision for all considered ML algorithms. In particular, on the unbalanced DUNI dataset the accuracy increases in parallel with the increase in sensitivity while on the balanced dataset D128 the accuracy increases compared to the counterpart without the addition of SNARER descriptors, with a strong improvement in specificity. Our best result is the combination of our descriptors SNARER with CKSAAP feature on the dataset D128 with 92.3% of accuracy, 90.1% for sensitivity and 95% for specificity with the RF algorithm.

Conclusions

The performed analysis has shown how the introduction of molecular descriptors linked to the chemical-physical and structural characteristics of the proteins can improve the classification performance. Additionally, it was pointed out that performance can change based on using a balanced or unbalanced dataset. The balanced nature of training can significantly improve forecast accuracy.

A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem

Machine learning has been widely used to solve complex problems in engineering applications and scientific fields, and many machine learning-based methods have achieved good results in different fields. SNAREs are key elements of membrane fusion and required for the fusion process of stable intermediates. They are also associated with the formation of some psychiatric disorders. This study processes the original sequence data with the synthetic minority oversampling technique (SMOTE) to solve the problem of data imbalance and produces the most suitable machine learning model with the iLearnPlus platform for the identification of SNARE proteins. Ultimately, a sensitivity of 66.67%, specificity of 93.63%, accuracy of 91.33%, and MCC of 0.528 were obtained in the cross-validation dataset, and a sensitivity of 66.67%, specificity of 93.63%, accuracy of 91.33%, and MCC of 0.528 were obtained in the independent dataset (the adaptive skip dipeptide composition descriptor was used for feature extraction, and LightGBM with proper parameters was used as the classifier). These results demonstrate that this combination can perform well in the classification of SNARE proteins and is superior to other methods.

VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost

Vesicular transport proteins are related to many human diseases, and they threaten human health when they undergo pathological changes. Protein function prediction has been one of the most in-depth topics in bioinformatics. In this work, we developed a useful tool to identify vesicular transport proteins. Our strategy is to extract transition probability composition, autocovariance transformation and other information from the position-specific scoring matrix as feature vectors. EditedNearesNeighbours (ENN) is used to address the imbalance of the data set, and the Max-Relevance-Max-Distance (MRMD) algorithm is adopted to reduce the dimension of the feature vector. We used 5-fold cross-validation and independent test sets to evaluate our model. On the test set, VTP-Identifier presented a higher performance compared with GRU. The accuracy, Matthew's correlation coefficient (MCC) and area under the ROC curve (AUC) were 83.6%, 0.531 and 0.873, respectively.

SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles

Soluble N-ethylmaleimide sensitive factor activating protein receptor (SNARE) proteins are a large family of transmembrane proteins located in organelles and vesicles. The important roles of SNARE proteins include initiating the vesicle fusion process and activating and fusing proteins as they undergo exocytosis activity, and SNARE proteins are also vital for the transport regulation of membrane proteins and non-regulatory vesicles. Therefore, there is great significance in establishing a method to efficiently identify SNARE proteins. However, the identification accuracy of the existing methods such as SNARE CNN is not satisfied. In our study, we developed a method based on a support vector machine (SVM) that can effectively recognize SNARE proteins. We used the position-specific scoring matrix (PSSM) method to extract features of SNARE protein sequences, used the support vector machine recursive elimination correlation bias reduction (SVM-RFE-CBR) algorithm to rank the importance of features, and then screened out the optimal subset of feature data based on the sorted results. We input the feature data into the model when building the model, used 10-fold crossing validation for training, and tested model performance by using an independent dataset. In independent tests, the ability of our method to identify SNARE proteins achieved a sensitivity of 68%, specificity of 94%, accuracy of 92%, area under the curve (AUC) of 84%, and Matthew’s correlation coefficient (MCC) of 0.48. The results of the experiment show that the common evaluation indicators of our method are excellent, indicating that our method performs better than other existing classification methods in identifying SNARE proteins.

Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network

Predicting the protein sequence information of enzymes and non-enzymes is an important but a very challenging task. Existing methods use protein geometric structures only or protein sequences alone to predict enzymatic functions. Thus, their prediction results are unsatisfactory. In this paper, we propose a novel approach for predicting the amino acid sequences of enzymes and non-enzymes via Convolutional Neural Network (CNN). In CNN, the roles of enzymes are predicted from multiple sides of biological information, including information on sequences and structures. We propose the use of two-dimensional data via 2DCNN to predict the proteins of enzymes and non-enzymes by using the same fivefold cross-validation function. We also use an independent dataset to test the performance of our model, and the results demonstrate that we are able to solve the overfitting problem. We used the CNN model proposed herein to demonstrate the superiority of our model for classifying an entire set of filters, such as 32, 64, and 128 parameters, with the fivefold validation test set as the independent classification. Via the Dipeptide Deviation from Expected Mean (DDE) matrix, mutation information is extracted from amino acid sequences and structural information with the distance and angle of amino acids is conveyed. The derived feature maps are then encoded in DDE exploitation. The independent datasets are then compared with other two methods, namely, GRU and XGBOOST. All analyses were conducted using 32, 64 and 128 filters on our proposed CNN method. The cross-validation datasets achieved an accuracy score of 0.8762%, whereas the accuracy of independent datasets was 0.7621%. Additional variables were derived on the basis of ROC AUC with fivefold cross-validation was achieved score is 0.95%. The performance of our model and that of other models in terms of sensitivity (0.9028%) and specificity (0.8497%) was compared. The overall accuracy of our model was 0.9133% compared with 0.8310% for the other model.

Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules

Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.

Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers

Since consuming gutter oil does great harm to people's health, the Food Safety Administration has always been seeking for a more effective and timely supervision. As laboratory tests consume much time, and existing field tests have excessive limitations, a more comprehensive method is in great need. This is the first time a study proposes machine learning algorithms for real-time gutter oil detection under multiple feature dimensions. Moreover, it is deployed on FPGA to be low-power and portable for actual use. Firstly, a variety of oil samples are generated by simulating the real detection environment. Next, based on previous studies, sensors are used to collect significant features that help distinguish gutter oil. Then, the acquired features are filtered and compared using a variety of classifiers. The best classification result is obtained by k-NN with an accuracy of 97.18%, and the algorithm is deployed to FPGA with no significant loss of accuracy. Power consumption is further reduced with the approximate multiplier we designed. Finally, the experimental results show that compared with all other platforms, the whole FPGA-based classification process consumes 4.77 µs and the power consumption is 65.62 mW. The dataset, source code and the 3D modeling file are all open-sourced.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

BACKGROUND AND OBJECTIVES

This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.

METHODS

We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.

RESULTS

It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.

CONCLUSIONS

Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma

We aimed to develop a deep learning (DL) model for predicting high-grade patterns in lung adenocarcinomas (ADC) and to assess the prognostic performance of model in advanced lung cancer patients who underwent neoadjuvant or definitive concurrent chemoradiation therapy (CCRT). We included 275 patients with 290 early lung ADCs from an ongoing prospective clinical trial in the training dataset, which we split into internal-training and internal-validation datasets. We constructed a diagnostic DL model of high-grade patterns of lung ADC considering both morphologic view of the tumor and context view of the area surrounding the tumor (MC3DN; morphologic-view context-view 3D network). Validation was performed on an independent dataset of 417 patients with advanced non-small cell lung cancer who underwent neoadjuvant or definitive CCRT. The area under the curve value of the DL model was 0.8 for the prediction of high-grade histologic patterns such as micropapillary and solid patterns (MPSol). When our model was applied to the validation set, a high probability of MPSol was associated with worse overall survival (probability of MPSol >0.5 vs. <0.5; 5-year OS rate 56.1% vs. 70.7%), indicating that our model could predict the clinical outcomes of advanced lung cancer patients. The subgroup with a high probability of MPSol estimated by the DL model showed a 1.76-fold higher risk of death (HR 1.76, 95% CI 1.16-2.68). Our DL model can be useful in estimating high-grade histologic patterns in lung ADCs and predicting clinical outcomes of patients with advanced lung cancer who underwent neoadjuvant or definitive CCRT.

DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion

An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model's predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model's area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.

Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms

Medical diagnosis through the classification of biomedical attributes is one of the exponentially growing fields in bioinformatics. Although a large number of approaches have been presented in the past, wide use and superior performance of the machine learning (ML) methods in medical diagnosis necessitates significant consideration for automatic diagnostic methods. This study proposes a novel approach called concatenated resampling (CR) to increase the efficacy of traditional ML algorithms. The performance is analyzed leveraging four ML approaches like tree-based ensemble approaches, and linear machine learning approach for automatic diagnosis of inter-vertebral pathologies with increased. Besides, undersampling, over-sampling, and proposed CR techniques have been applied to unbalanced training dataset to analyze the impact of these techniques on the accuracy of each of the classification model. Extensive experiments have been conducted to make comparisons among different classification models using several metrics including accuracy, precision, recall, and F ₁ score. Comparative analysis has been performed on the experimental results to identify the best performing classifier along with the application of the re-sampling technique. The results show that the extra tree classifier achieves an accuracy of 0.99 in association with the proposed CR technique.

DBP-GAPred: An intelligent method for prediction of DNA-binding proteins types by enhanced evolutionary profile features with ensemble learning

DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Some DBPs are indispensable for understanding many types of human cancers (i.e. lung, breast, and liver cancer) and chronic diseases (i.e. AIDS/HIV, asthma), while other kinds are involved in antibiotics, steroids, and anti-inflammatory drugs designing. These crucial processes are closely related to DBPs types. DBPs are categorized into single-stranded DNA-binding proteins (ssDBPs) and double-stranded DNA-binding proteins (dsDBPs). Few computational predictors have been reported for discriminating ssDBPs and dsDBPs. However, due to the limitations of the existing methods, an intelligent computational system is still highly desirable. In this work, features from protein sequences are discovered by extending the notion of dipeptide composition (DPC), evolutionary difference formula (EDF), and K-separated bigram (KSB) into the position-specific scoring matrix (PSSM). The highly intrinsic information was encoded by a compression approach named discrete cosine transform (DCT) and the model was trained with support vector machine (SVM). The prediction performance was further boosted by the genetic algorithm (GA) ensemble strategy. The novel predictor (DBP-GAPred) acquired 1.89%, 0.28%, and 6.63% higher accuracies on jackknife, 10-fold, and independent dataset tests, respectively than the best predictor. These outcomes confirm the superiority of our method over the existing predictors.

Full depth CNN classifier for handwritten and license plate characters recognition

Character recognition is an important research field of interest for many applications. In recent years, deep learning has made breakthroughs in image classification, especially for character recognition. However, convolutional neural networks (CNN) still deliver state-of-the-art results in this area. Motivated by the success of CNNs, this paper proposes a simple novel full depth stacked CNN architecture for Latin and Arabic handwritten alphanumeric characters that is also utilized for license plate (LP) characters recognition. The proposed architecture is constructed by four convolutional layers, two max-pooling layers, and one fully connected layer. This architecture is low-complex, fast, reliable and achieves very promising classification accuracy that may move the field forward in terms of low complexity, high accuracy and full feature extraction. The proposed approach is tested on four benchmarks for handwritten character datasets, Fashion-MNIST dataset, public LP character datasets and a newly introduced real LP isolated character dataset. The proposed approach tests report an error of only 0.28% for MNIST, 0.34% for MAHDB, 1.45% for AHCD, 3.81% for AIA9K, 5.00% for Fashion-MNIST, 0.26% for Saudi license plate character and 0.97% for Latin license plate characters datasets. The license plate characters include license plates from Turkey (TR), Europe (EU), USA, United Arab Emirates (UAE) and Kingdom of Saudi Arabia (KSA).

Introspective analysis of convolutional neural networks for improving discrimination performance and feature visualisation

Deep neural networks have been widely explored and utilised as a useful tool for feature extraction in computer vision and machine learning. It is often observed that the last fully connected (FC) layers of convolutional neural network possess higher discrimination power as compared to the convolutional and maxpooling layers whose goal is to preserve local and low-level information of the input image and down sample it to avoid overfitting. Inspired from the functionality of local binary pattern (LBP) operator, this paper proposes to induce discrimination into the mid layers of convolutional neural network by introducing a discriminatively boosted alternative to pooling (DBAP) layer that has shown to serve as a favourable replacement of early maxpooling layer in a convolutional neural network (CNN). A thorough research of the related works show that the proposed change in the neural architecture is novel and has not been proposed before to bring enhanced discrimination and feature visualisation power achieved from the mid layer features. The empirical results reveal that the introduction of DBAP layer in popular neural architectures such as AlexNet and LeNet produces competitive classification results in comparison to their baseline models as well as other ultra-deep models on several benchmark data sets. In addition, better visualisation of intermediate features can allow one to seek understanding and interpretation of black box behaviour of convolutional neural networks, used widely by the research community.

Improving protein domain classification for third-generation sequencing reads using deep learning

BACKGROUND

With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the underlying data. However, the high error rate in TGS data raises a new challenge to established domain analysis pipelines. The state-of-the-art methods are not optimized for noisy reads and have shown unsatisfactory accuracy of domain classification in TGS data. New computational methods are still needed to improve the performance of domain prediction in long noisy reads.

RESULTS

In this work, we introduce ProDOMA, a deep learning model that conducts domain classification for TGS reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In addition, we formulate our problem as an open-set problem and thus our model can reject reads not containing the targeted domains. In the experiments on simulated long reads of protein coding sequences and real TGS reads from the human genome, our model outperforms HMMER and DeepFam on protein domain classification.

CONCLUSIONS

In summary, ProDOMA is a useful end-to-end protein domain analysis tool for long noisy reads without relying on error correction.

A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images

BACKGROUND

COVID-19 pandemic imposed a lockdown situation to the world these past months. Researchers and scientists around the globe faced serious efforts from its detection to its treatment.

METHODS

Pathogenic laboratory testing is the gold standard but it is time-consuming. Lung CT-scans and X-rays are other common methods applied by researchers to detect COVID-19 positive cases. In this paper, we propose a deep learning neural network-based model as an alternative fast screening method that can be used for detecting the COVID-19 cases by analyzing CT-scans.

RESULTS

Applying the proposed method on a publicly available dataset collected of positive and negative cases showed its ability on distinguishing them by analyzing each individual CT image. The effect of different parameters on the performance of the proposed model was studied and tabulated. By selecting random train and test images, the overall accuracy and ROC-AUC of the proposed model can easily exceed 95% and 90%, respectively, without any image pre-selecting or preprocessing.

Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network

Facial landmarks represent prominent feature points on the face that can be used as anchor points in many face-related tasks. So far, a lot of research has been done with the aim of achieving efficient extraction of landmarks from facial images. Employing a large number of feature points for landmark detection and tracking usually requires excessive processing time. On the contrary, relying on too few feature points cannot accurately represent diverse landmark properties, such as shape. To extract the 68 most popular facial landmark points efficiently, in our previous study, we proposed a model called EMTCNN that extended the multi-task cascaded convolutional neural network for real-time face landmark detection. To improve the detection accuracy, in this study, we augment the EMTCNN model by using two convolution techniques—dilated convolution and CoordConv. The former makes it possible to increase the filter size without a significant increase in computation time. The latter enables the spatial coordinate information of landmarks to be reflected in the model. We demonstrate that our model can improve the detection accuracy while maintaining the processing speed.

Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation

SNAREs (soluble N-ethylmaleimide-sensitive factor activating protein receptors) are a group of proteins that are crucial for membrane fusion and exocytosis of neurotransmitters from the cell. They play an important role in a broad range of cell processes, including cell growth, cytokinesis, and synaptic transmission, to promote cell membrane integration in eukaryotes. Many studies determined that SNARE proteins have been associated with a lot of human diseases, especially in cancer. Therefore, identifying their functions is a challenging problem for scientists to better understand the cancer disease as well as design the drug targets for treatment. We described each protein sequence based on the amino acid embeddings using fastText, which is a natural language processing model performing well in its field. Because each protein sequence is similar to a sentence with different words, applying language model into protein sequence is challenging and promising. After generating, the amino acid embedding features were fed into a deep learning algorithm for prediction. Our model which combines fastText model and deep convolutional neural networks could identify SNARE proteins with an independent test accuracy of 92.8%, sensitivity of 88.5%, specificity of 97%, and Matthews correlation coefficient (MCC) of 0.86. Our performance results were superior to the state-of-the-art predictor (SNARE-CNN). We suggest this study as a reliable method for biologists for SNARE identification and it serves a basis for applying fastText word embedding model into bioinformatics, especially in protein sequencing prediction.

Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams

A promoter is a short region of DNA (100-1,000 bp) where transcription of a gene by RNA polymerase begins. It is typically located directly upstream or at the 5' end of the transcription initiation site. DNA promoter has been proven to be the primary cause of many human diseases, especially diabetes, cancer, or Huntington's disease. Therefore, classifying promoters has become an interesting problem and it has attracted the attention of a lot of researchers in the bioinformatics field. There were a variety of studies conducted to resolve this problem, however, their performance results still require further improvement. In this study, we will present an innovative approach by interpreting DNA sequences as a combination of continuous FastText N-grams, which are then fed into a deep neural network in order to classify them. Our approach is able to attain a cross-validation accuracy of 85.41 and 73.1% in the two layers, respectively. Our results outperformed the state-of-the-art methods on the same dataset, especially in the second layer (strength classification). Throughout this study, promoter regions could be identified with high accuracy and it provides analysis for further biological research as well as precision medicine. In addition, this study opens new paths for the natural language processing application in omics data in general and DNA sequences in particular.

Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture

Protein function prediction is one of the most well-studied topics, attracting attention from countless researchers in the field of computational biology. Implementing deep neural networks that help improve the prediction of protein function, however, is still a major challenge. In this research, we suggested a new strategy that includes gated recurrent units and position-specific scoring matrix profiles to predict vesicular transportation proteins, a biological function of great importance. Although it is difficult to discover its function, our model is able to achieve accuracies of 82.3% and 85.8% in the cross-validation and independent dataset, respectively. We also solve the problem of imbalance in the dataset via tuning class weight in the deep learning model. The results generated showed sensitivity, specificity, MCC, and AUC to have values of 79.2%, 82.9%, 0.52, and 0.861, respectively. Our strategy shows superiority in results on the same dataset against all other state-of-the-art algorithms. In our suggested research, we have suggested a technique for the discovery of more proteins, particularly proteins connected with vesicular transport. In addition, our accomplishment could encourage the use of gated recurrent units architecture in protein function prediction.

EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model

Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms. However, existing computational methods mainly focus on the prediction of interactions between enhancers and promoters, leaving enhancer-enhancer (E-E) interactions not well explored. In this work, we develop a novel deep learning approach, named Enhancer-enhancer contacts prediction (EnContact), to predict E-E contacts using genomic sequences as input. We statistically demonstrated the predicting ability of EnContact using training sets and testing sets derived from HiChIP data of seven cell lines. We also show that our model significantly outperforms other baseline methods. Besides, our model identifies finer-mapping E-E interactions from region-based chromatin contacts, where each region contains several enhancers. In addition, we identify a class of hub enhancers using the predicted E-E interactions and find that hub enhancers tend to be active across cell lines. We summarize that our EnContact model is capable of predicting E-E interactions using features automatically learned from genomic sequences.

OutbreakFinder: a visualization tool for rapid detection of bacterial strain clusters based on optimized multidimensional scaling

With the evolution of next generation sequencing (NGS) technologies, whole-genome sequencing of bacterial isolates is increasingly employed to investigate epidemiology. Phylogenetic analysis is the common method for using NGS data, usually for comparing closeness between bacterial isolates to detect probable outbreaks. However, interpreting a phylogenetic tree is not easy without training in evolutionary biology. Therefore, developing an easy-to-use tool that can assist people who wish to use a phylogenetic tree to investigate epidemiological relatedness is crucial. In this paper, we present a tool called OutbreakFinder that can accept a distance matrix in csv format; alignment files from Lyve-SET, Parsnp, and ClustalOmega; and a tree file in Newick format as inputs to compute a cluster-labeled two-dimensional plot based on multidimensional-scaling dimension reduction coupled with affinity propagation clustering. OutbreakFinder can be downloaded for free at https://github.com/skypes/Newton-method-MDS.

Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles

BACKGROUND AND OBJECTIVES

Clathrin is an adaptor protein that serves as the principal element of the vesicle-coating complex and is important for the membrane cleavage to dispense the invaginated vesicle from the plasma membrane. The functional loss of clathrins has been tied to a lot of human diseases, i.e., neurodegenerative disorders, cancer, Alzheimer's diseases, and so on. Therefore, creating a precise model to identify its functions is a crucial step towards understanding human diseases and designing drug targets.

METHODS

We present a deep learning model using a two-dimensional convolutional neural network (CNN) and position-specific scoring matrix (PSSM) profiles to identify clathrin proteins from high throughput sequences. Traditionally, the 2D CNNs take images as an input so we treated the PSSM profile with a 20 × 20 matrix as an image of 20 × 20 pixels. The input PSSM profile was then connected to our 2D CNN in which we set a variety of parameters to improve the performance of the model. Based on the 10-fold cross-validation results, hyper-parameter optimization process was employed to find the best model for our dataset. Finally, an independent dataset was used to assess the predictive ability of the current model.

RESULTS

Our model could identify clathrin proteins with sensitivity of 92.2%, specificity of 91.2%, accuracy of 91.8%, and MCC of 0.83 in the independent dataset. Compared to state-of-the-art traditional neural networks, our method achieved a significant improvement in all typical measurement metrics.

CONCLUSIONS

Throughout the proposed study, we provide an effective tool for investigating clathrin proteins and our achievement could promote the use of deep learning in biomedical research. We also provide source codes and dataset freely at https://www.github.com/khanhlee/deep-clathrin/.

Improving clinical refractive results of cataract surgery by machine learning

AIM

To evaluate the potential of the Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) to improve the intraocular lens (IOL) power calculation for clinical workflow.

BACKGROUND

Current IOL power calculation methods are limited in their accuracy with the possibility of decreased accuracy especially in eyes with an unusual ocular dimension. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery there is a risk of re-operation or further refractive correction. This may create potential complications and discomfort for the patient.

METHODS

A dataset containing information about 2,194 eyes was obtained using data mining process from the Electronic Health Record (EHR) system database of the Gemini Eye Clinic. The dataset was optimized and split into the selection set (used in the design for models and training), and the verification set (used in the evaluation). The set of mean prediction errors (PEs) and the distribution of predicted refractive errors were evaluated for both models and clinical results (CR).

RESULTS

Both models performed significantly better for the majority of the evaluated parameters compared with the CR. There was no significant difference between both evaluated models. In the ±0.50 D PE category both SVM-RM and MLNN-EM were slightly better than the Barrett Universal II formula, which is often presented as the most accurate calculation formula.

CONCLUSION

In comparison to the current clinical method, both SVM-RM and MLNN-EM have achieved significantly better results in IOL calculations and therefore have a strong potential to improve clinical cataract refractive outcomes.

Prediction of Enzyme Function Based on Three Parallel Deep CNN and Amino Acid Mutation

During the past decade, due to the number of proteins in PDB database being increased gradually, traditional methods cannot better understand the function of newly discovered enzymes in chemical reactions. Computational models and protein feature representation for predicting enzymatic function are more important. Most of existing methods for predicting enzymatic function have used protein geometric structure or protein sequence alone. In this paper, the functions of enzymes are predicted from many-sided biological information including sequence information and structure information. Firstly, we extract the mutation information from amino acids sequence by the position scoring matrix and express structure information with amino acids distance and angle. Then, we use histogram to show the extracted sequence and structural features respectively. Meanwhile, we establish a network model of three parallel Deep Convolutional Neural Networks (DCNN) to learn three features of enzyme for function prediction simultaneously, and the outputs are fused through two different architectures. Finally, The proposed model was investigated on a large dataset of 43,843 enzymes from the PDB and achieved 92.34% correct classification when sequence information is considered, demonstrating an improvement compared with the previous result.