ZeRPI: A graph neural network model for zero-shot prediction of RNA-protein interactions

RNA-protein interactions are crucial for biological functions across multiple levels. RNA binding proteins (RBPs) intricately engage in diverse biological processes through specific RNA molecule interactions. Previous studies have revealed the indispensable role of RBPs in both health and disease development. With the increase of experimental data, machine-learning methods have been widely used to predict RNA-protein interactions. However, most current methods either train models for individual RBPs or develop multi-task models for a fixed set of multiple RBPs. These approaches are incapable of predicting interactions with previously unseen RBPs. In this study, we present ZeRPI, a zero-shot method for predicting RNA-protein interactions. Based on a graph neural network model, ZeRPI integrates RNA and protein information to generate detailed representations, using a novel loss function based on contrastive learning principles to augment the alignment between interacting pairs in feature space. ZeRPI demonstrates competitive performance in predicting RNA-protein interactions across a wide array of RBPs. Notably, our model exhibits remarkable versatility in accurately predicting interactions for unseen RBPs, demonstrating its capacity to transfer knowledge learned from known RBPs.

An Integrated TCN-CrossMHA Model for Predicting circRNA-RBP Binding Sites

Circular RNA (circRNA) has the capacity to bind with RNA binding protein (RBP), thereby exerting a substantial impact on diseases. Predicting binding sites aids in comprehending the interaction mechanism, thereby offering insights for disease treatment strategies. Here, we propose a novel approach based on temporal convolutional network (TCN) and cross multi-head attention mechanism to predict circRNA-RBP binding sites (circTCA). First, we employ two distinct encoding methodologies to obtain two raw matrices of circRNA sequences. Then, two parallel TCN blocks extract shallow and abstract features of the two matrices separately. The fusion of the two is achieved through cross multi-head attention mechanism and after this, global expectation pooling assigns weights to the concatenated feature. Finally, the task of classifying the input sequence is entrusted to a fully connected (FC) layer. We compare circTCA with other five methods and conduct ablation experiments to demonstrate its effectiveness. We also conduct feature visualization and assess the motifs extracted by circTCA with existing motifs. All in all, circTCA is effective for binding sites prediction of circRNA and RBP.

EnrichRBP: an automated and interpretable computational platform for predicting and analysing RNA-binding protein events

MOTIVATION

Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.

RESULTS

EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RBPs. The platform supports 70 deep learning algorithms, covering feature representation, selection, model training, comparison, optimization, and evaluation, all integrated within an automated pipeline. EnrichRBP is adept at providing comprehensive visualizations, enhancing model interpretability, and facilitating the discovery of functionally significant sequence regions crucial for RBP interactions. In addition, EnrichRBP supports base-level functional annotation tasks, offering explanations and graphical visualizations that confirm the reliability of the predicted RNA-binding sites. Leveraging high-performance computing, EnrichRBP provides ultra-fast predictions ranging from seconds to hours, applicable to both pre-trained and custom model scenarios, thus proving its utility in real-world applications. Case studies highlight that EnrichRBP provides robust and interpretable predictions, demonstrating the power of deep learning in the functional analysis of RBP interactions. Finally, EnrichRBP aims to enhance the reproducibility of computational method analyses for RBP sequences, as well as reduce the programming and hardware requirements for biologists, thereby offering meaningful functional insights.

AVAILABILITY AND IMPLEMENTATION

EnrichRBP is available at https://airbp.aibio-lab.com/. The source code is available at https://github.com/wangyb97/EnrichRBP, and detailed online documentation can be found at https://enrichrbp.readthedocs.io/en/latest/.

AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data

AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these genes in AD association is still a research topic. Therefore, AlzGenPred is developed to identify the AD-associated genes using machine-learning. A total of 13,504 features derived from eight sequence-encoding schemes were generated and evaluated using 16 machine learning algorithms. Network-based features significantly outperformed sequence-based features, effectively distinguishing AD-associated genes. In contrast, sequence-based features failed to classify accurately. To improve performance, we generated 24 fused features (6020 D) from sequence-based encodings, increasing accuracy by 5-7% using a two-step lightGBM-based recursive feature selection method. However, accuracy remained below 70% even after hyperparameter tuning. Therefore, network-based features were used to generate the CatBoost-based ML method AlzGenPred with 96.55% accuracy and 98.99% AUROC. The developed method is tested on the AlzGene dataset where it showed 96.43% accuracy. Then the model was validated using the transcriptomics dataset. AlzGenPred provides a reliable and user-friendly tool for identifying potential AD biomarkers, accelerating biomarker discovery, and advancing our understanding of AD. It is available at https://www.bioinfoindia.org/alzgenpred/ and https://github.com/shuklarohit815/AlzGenPred .

RiceSNP-ABST: a deep learning approach to identify abiotic stress-associated single nucleotide polymorphisms in rice

Given the adverse effects faced by rice due to abiotic stresses, the precise and rapid identification of single nucleotide polymorphisms (SNPs) associated with abiotic stress traits (ABST-SNPs) in rice is crucial for developing resistant rice varieties. The scarcity of high-quality data related to abiotic stress in rice has hindered the development of computational models and constrained research efforts aimed at rice improvement and breeding. Genome-wide association studies provide a better statistical power to consider ABST-SNPs in rice. Meanwhile, deep learning methods have shown their capability in predicting disease- or phenotype-associated loci, but have primarily focused on human species. Therefore, developing predictive models for identifying ABST-SNPs in rice is both urgent and valuable. In this paper, a model called RiceSNP-ABST is proposed for predicting ABST-SNPs in rice. Firstly, six training datasets were generated using a novel strategy for negative sample construction. Secondly, four feature encoding methods were proposed based on DNA sequence fragments, followed by feature selection. Finally, convolutional neural networks with residual connections were used to determine whether the sequences contained rice ABST-SNPs. RiceSNP-ABST outperformed traditional machine learning and state-of-the-art methods on the benchmark dataset and demonstrated consistent generalization on an independent dataset and cross-species datasets. Notably, multi-granularity causal structure learning was employed to elucidate the relationships among DNA structural features, aiming to identify key genetic variants more effectively. The web-based tool for the RiceSNP-ABST can be accessed at http://rice-snp-abst.aielab.cc.

CRBPSA: CircRNA-RBP interaction sites identification using sequence structural attention model

BACKGROUND

Due to the ability of circRNA to bind with corresponding RBPs and play a critical role in gene regulation and disease prevention, numerous identification algorithms have been developed. Nevertheless, most of the current mainstream methods primarily capture one-dimensional sequence features through various descriptors, while neglecting the effective extraction of secondary structure features. Moreover, as the number of introduced descriptors increases, the issues of sparsity and ineffective representation also rise, causing a significant burden on computational models and leaving room for improvement in predictive performance.

RESULTS

Based on this, we focused on capturing the features of secondary structure in sequences and developed a new architecture called CRBPSA, which is based on a sequence-structure attention mechanism. Firstly, a base-pairing matrix is generated by calculating the matching probability between each base, with a Gaussian function introduced as a weight to construct the secondary structure. Then, a Structure_Transformer is employed to extract base-pairing information and spatial positional dependencies, enabling the identification of binding sites through deeper feature extraction. Experimental results using the same set of hyperparameters on 37 circRNA datasets, totaling 671,952 samples, show that the CRBPSA algorithm achieves an average AUC of 99.93%, surpassing all existing prediction methods.

CONCLUSIONS

CRBPSA is a lightweight and efficient prediction tool for circRNA-RBP, which can capture structural features of sequences with minimal computational resources and accurately predict protein-binding sites. This tool facilitates a deeper understanding of the biological processes and mechanisms underlying circRNA and protein interactions.

A Representation Learning Approach for Predicting circRNA Back-Splicing Event via Sequence-Interaction-Aware Dual Encoder

Circular RNAs (circRNAs) play a crucial role in gene regulation and association with diseases because of their unique closed continuous loop structure, which is more stable and conserved than ordinary linear RNAs. As fundamental work to clarify their functions, a large number of computational approaches for identifying circRNA formation have been proposed. However, these methods fail to fully utilize the important characteristics of back-splicing events, i.e., the positional information of the splice sites and the interaction features of its flanking sequences, for predicting circRNAs. To this end, we hereby propose a novel approach called SIDE for predicting circRNA back-splicing events using only raw RNA sequences. Technically, SIDE employs a dual encoder to capture global and interactive features of the RNA sequence, and then a decoder designed by the contrastive learning to fuse out discriminative features improving the prediction of circRNAs formation. Empirical results on three real-world datasets show the effectiveness of SIDE. Further analysis also reveals that the effectiveness of SIDE.

Predicting circRNA-RBP Binding Sites Using a Hybrid Deep Neural Network

Circular RNAs (circRNAs) are non-coding RNAs generated by reverse splicing. They are involved in biological process and human diseases by interacting with specific RNA-binding proteins (RBPs). Due to traditional biological experiments being costly, computational methods have been proposed to predict the circRNA-RBP interaction. However, these methods have problems of single feature extraction. Therefore, we propose a novel model called circ-FHN, which utilizes only circRNA sequences to predict circRNA-RBP interactions. The circ-FHN approach involves feature coding and a hybrid deep learning model. Feature coding takes into account the physicochemical properties of circRNA sequences and employs four coding methods to extract sequence features. The hybrid deep structure comprises a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU). The CNN learns high-level abstract features, while the BiGRU captures long-term dependencies in the sequence. To assess the effectiveness of circ-FHN, we compared it to other computational methods on 16 datasets and conducted ablation experiments. Additionally, we conducted motif analysis. The results demonstrate that circ-FHN exhibits exceptional performance and surpasses other methods. circ-FHN is freely available at https://github.com/zhaoqi106/circ-FHN .

Research progress on prediction of RNA-protein binding sites in the past five years

Accurately predicting RNA-protein binding sites is essential to gain a deeper comprehension of the protein-RNA interactions and their regulatory mechanisms, which are fundamental in gene expression and regulation. However, conventional biological approaches to detect these sites are often costly and time-consuming. In contrast, computational methods for predicting RNA protein binding sites are both cost-effective and expeditious. This review synthesizes already existing computational methods, summarizing commonly used databases for predicting RNA protein binding sites. In addition, applications and innovations of computational methods using traditional machine learning and deep learning for RNA protein binding site prediction during 2018-2023 are presented. These methods cover a wide range of aspects such as effective database utilization, feature selection and encoding, innovative classification algorithms, and evaluation strategies. Exploring the limitations of existing computational methods, this paper delves into the potential directions for future development. DeepRKE, RDense, and DeepDW all employ convolutional neural networks and long and short-term memory networks to construct prediction models, yet their algorithm design and feature encoding differ, resulting in diverse prediction performances.

iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites

Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step in molecular and cell biology. Deep learning (DL)-based methods have been proposed to predict circRNA-RBP interaction sites and achieved impressive identification performance. However, those methods cannot effectively capture long-distance dependencies, and cannot effectively utilize the interaction information of multiple features. To overcome those limitations, we propose a DL-based model iCRBP-LKHA using deep hybrid networks for identifying circRNA-RBP interaction sites. iCRBP-LKHA adopts five encoding schemes. Meanwhile, the neural network architecture, which consists of large kernel convolutional neural network (LKCNN), convolutional block attention module with one-dimensional convolution (CBAM-1D) and bidirectional gating recurrent unit (BiGRU), can explore local information, global context information and multiple features interaction information automatically. To verify the effectiveness of iCRBP-LKHA, we compared its performance with shallow learning algorithms on 37 circRNAs datasets and 37 circRNAs stringent datasets. And we compared its performance with state-of-the-art DL-based methods on 37 circRNAs datasets, 37 circRNAs stringent datasets and 31 linear RNAs datasets. The experimental results not only show that iCRBP-LKHA outperforms other competing methods, but also demonstrate the potential of this model in identifying other RNA-RBP interaction sites.

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.

Decoding protein binding landscape on circular RNAs with base-resolution transformer models

Circular RNAs (circRNAs), a class of endogenous RNA with a covalent loop structure, can regulate gene expression by serving as sponges for microRNAs and RNA-binding proteins (RBPs). To date, most computational methods for predicting RBP binding sites on circRNAs focus on circRNA fragments instead of circRNAs. These methods detect whether a circRNA fragment contains binding sites, but cannot determine where are the binding sites and how many binding sites are on the circRNA transcript. We report a hybrid deep learning-based tool, CircSite, to predict RBP binding sites at single-nucleotide resolution and detect key contributed nucleotides on circRNA transcripts. CircSite takes advantage of convolutional neural networks (CNNs) and Transformer for learning local and global representations of circRNAs binding to RBPs, respectively. We construct 37 datasets of circRNAs interacting with proteins for benchmarking and the experimental results show that CircSite offers accurate predictions of RBP binding nucleotides and detects key subsequences aligning well with known binding motifs. CircSite is an easy-to-use online webserver for predicting RBP binding sites on circRNA transcripts and freely available at http://www.csbio.sjtu.edu.cn/bioinf/CircSite/.

CircSI-SSL: circRNA-binding site identification based on self-supervised learning

MOTIVATION

In recent years, circular RNAs (circRNAs), the particular form of RNA with a closed-loop structure, have attracted widespread attention due to their physiological significance (they can directly bind proteins), leading to the development of numerous protein site identification algorithms. Unfortunately, these studies are supervised and require the vast majority of labeled samples in training to produce superior performance. But the acquisition of sample labels requires a large number of biological experiments and is difficult to obtain.

RESULTS

To resolve this matter that a great deal of tags need to be trained in the circRNA-binding site prediction task, a self-supervised learning binding site identification algorithm named CircSI-SSL is proposed in this article. According to the survey, this is unprecedented in the research field. Specifically, CircSI-SSL initially combines multiple feature coding schemes and employs RNA_Transformer for cross-view sequence prediction (self-supervised task) to learn mutual information from the multi-view data, and then fine-tuning with only a few sample labels. Comprehensive experiments on six widely used circRNA datasets indicate that our CircSI-SSL algorithm achieves excellent performance in comparison to previous algorithms, even in the extreme case where the ratio of training data to test data is 1:9. In addition, the transplantation experiment of six linRNA datasets without network modification and hyperparameter adjustment shows that CircSI-SSL has good scalability. In summary, the prediction algorithm based on self-supervised learning proposed in this article is expected to replace previous supervised algorithms and has more extensive application value.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/cc646201081/CircSI-SSL.

Predicting RNA structures and functions by artificial intelligence

RNA functions by interacting with its intended targets structurally. However, due to the dynamic nature of RNA molecules, RNA structures are difficult to determine experimentally or predict computationally. Artificial intelligence (AI) has revolutionized many biomedical fields and has been progressively utilized to deduce RNA structures, target binding, and associated functionality. Integrating structural and target binding information could also help improve the robustness of AI-based RNA function prediction and RNA design. Given the rapid development of deep learning (DL) algorithms, AI will provide an unprecedented opportunity to elucidate the sequence-structure-function relation of RNAs.

Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network

Introduction: CircRNA-protein binding plays a critical role in complex biological activity and disease. Various deep learning-based algorithms have been proposed to identify CircRNA-protein binding sites. These methods predict whether the CircRNA sequence includes protein binding sites from the sequence level, and primarily concentrate on analysing the sequence specificity of CircRNA-protein binding. For model performance, these methods are unsatisfactory in accurately predicting motif sites that have special functions in gene expression. Methods: In this study, based on the deep learning models that implement pixel-level binary classification prediction in computer vision, we viewed the CircRNA-protein binding sites prediction as a nucleotide-level binary classification task, and use a fully convolutional neural networks to identify CircRNA-protein binding motif sites (CPBFCN). Results: CPBFCN provides a new path to predict CircRNA motifs. Based on the MEME tool, the existing CircRNA-related and protein-related database, we analysed the motif functions discovered by CPBFCN. We also investigated the correlation between CircRNA sponge and motif distribution. Furthermore, by comparing the motif distribution with different input sequence lengths, we found that some motifs in the flanking sequences of CircRNA-protein binding region may contribute to CircRNA-protein binding. Conclusion: This study contributes to identify circRNA-protein binding and provides help in understanding the role of circRNA-protein binding in gene expression regulation.

ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.

CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion

Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein (RBP), little is known about the binding sites of CircRNA. The current report is on the development of a medium-term multimodal data fusion strategy, CRBSP, to predict CircRNA-RBP binding sites. CRBSP represents the CircRNA trinucleotide semantic, location, composition and frequency information as the corresponding coding methods of Word to vector (Word2vec), Position-specific trinucleotide propensity (PSTNP), Pseudo trinucleotide composition (PseTNC) and Trinucleotide nucleotide composition (TNC), respectively. CNN (Convolution Neural Networks) was used to extract global information and BiLSTM (bidirectional Long- and Short-Term Memory network) encoder and LSTM (Long- and Short-Term Memory network) decoder for local sequence information. Enhancement of the contributions of key features by the self-attention mechanism was followed by mid-term fusion of the four enhanced features. Logistic Regression (LR) classifier showed that CRBSP gives a mean AUC value of 0.9362 through 5-fold Cross Validation of all 37 datasets, a performance which is superior to five current state-of-the-art models. Similar evaluation of linear RNA-RBP binding sites gave an AUC value of 0.7615 which is also higher than other prediction methods, demonstrating the robustness of CRBSP.

ASCRB: Multi-view based attentional feature selection for CircRNA-binding site prediction

CircRNA is a non-coding RNA with a special circular structure, which plays a key role in a variety of life activities by interacting with RNA-binding proteins through CircRNA binding sites. Therefore, accurately identifying CircRNA binding sites is of great importance for gene regulation. In previous studies, most of the methods are based on single-view or multi-view features. Considering that single-view methods provide less effective information, the current mainstream methods mainly focus on extracting rich relevant features by constructing multiple views. However, the increasing number of views leads to a large amount of redundant information, which is detrimental to the detection of CircRNA binding sites. Therefore, to solve this problem, we propose to use the channel attention mechanism to further obtain useful multi-view features by filtering out invalid information in each view. First, we use five feature encoding schemes to construct multi-view. Then, we calibrate the features by generating the global representation of each view, filtering out redundant information to retain important feature information. Finally, features obtained from multiple views are fused to detect RNA binding sites. To validate the effectiveness of the method, we compared its performance on 37 CircRNA-RBP datasets with existing methods. Experimental results show that the average AUC performance of our method is 93.85%, which is better than the current state-of-the-art methods. We also provide the source code, which can be accessed at https://github.com/dxqllp/ASCRB for access.

CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization

BACKGROUND

Circular RNAs (circRNAs) play a significant role in some diseases by acting as transcription templates. Therefore, analyzing the interaction mechanism between circRNA and RNA-binding proteins (RBPs) has far-reaching implications for the prevention and treatment of diseases. Existing models for circRNA-RBP identification usually adopt convolution neural network (CNN), recurrent neural network (RNN), or their variants as feature extractors. Most of them have drawbacks such as poor parallelism, insufficient stability, and inability to capture long-term dependencies.

METHODS

In this paper, we propose a new method completely using the self-attention mechanism to capture deep semantic features of RNA sequences. On this basis, we construct a CircSSNN model for the cirRNA-RBP identification. The proposed model constructs a feature scheme by fusing circRNA sequence representations with statistical distributions, static local contexts, and dynamic global contexts. With a stable and efficient network architecture, the distance between any two positions in a sequence is reduced to a constant, so CircSSNN can quickly capture the long-term dependencies and extract the deep semantic features.

RESULTS

Experiments on 37 circRNA datasets show that the proposed model has overall advantages in stability, parallelism, and prediction performance. Keeping the network structure and hyperparameters unchanged, we directly apply the CircSSNN to linRNA datasets. The favorable results show that CircSSNN can be transformed simply and efficiently without task-oriented tuning.

CONCLUSIONS

In conclusion, CircSSNN can serve as an appealing circRNA-RBP identification tool with good identification performance, excellent scalability, and wide application scope without the need for task-oriented fine-tuning of parameters, which is expected to reduce the professional threshold required for hyperparameter tuning in bioinformatics analysis.

MSINGB: A Novel Computational Method Based on NGBoost for Identifying Microsatellite Instability Status from Tumor Mutation Annotation Data

Microsatellite instability (MSI), a vital mutator phenotype caused by DNA mismatch repair deficiency, is frequently observed in several tumors. MSI is recognized as a critical molecular biomarker for diagnosis, prognosis, and therapeutic selection in several cancers. Identifying MSI status for current gold standard methods based on experimental analysis is laborious, time-consuming, and costly. Although several computational methods based on machine learning have been proposed to identify MSI status, we need to further understand which machine learning model would favor identification for MSI and which feature subset is strongly related to MSI. On this basis, more effective machine learning-based methods can be developed to improve the performance of MSI status identification. In this work, we present MSINGB, an NGBoost-based method for identifying MSI status from tumor somatic mutation annotation data. MSINGB first evaluates the prediction performance of 11 popular machine learning algorithms and 9 deep learning models to identify MSI. Among 20 models, NGBoost, a novel natural gradient boosting method, achieves the overall best performance. MSINGB then introduces two feature selection strategies to find the compact feature subset, which is strongly related to MSI, and employs the SHAP approach to interpreting how selected features impact the model prediction. MSINGB achieves a better prediction performance on both the tenfold cross-validation test and independent test compared with state-of-the-art methods.

CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features

Circular RNAs (circRNAs) are reverse-spliced and covalently closed RNAs. Their interactions with RNA-binding proteins (RBPs) have multiple effects on the progress of many diseases. Some computational methods are proposed to identify RBP binding sites on circRNAs but suffer from insufficient accuracy, robustness and explanation. In this study, we first take the characteristics of both RNA and RBP into consideration. We propose a method for discriminating circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features, called CRMSS. For circRNAs, we use sequence ${k}\hbox{-}{mer}$ embedding and the forming probabilities of local secondary structures as features. For RBPs, we combine sequence and structure frequencies of RNA-binding domain regions to generate features. We capture binding patterns with multi-scale residual blocks. With BiLSTM and attention mechanism, we obtain the contextual information of high-level representation for circRNA-RBP binding. To validate the effectiveness of CRMSS, we compare its predictive performance with other methods on 37 RBPs. Taking the properties of both circRNAs and RBPs into account, CRMSS achieves superior performance over state-of-the-art methods. In the case study, our model provides reliable predictions and correctly identifies experimentally verified circRNA-RBP pairs. The code of CRMSS is freely available at https://github.com/BioinformaticsCSU/CRMSS.

Characterization of circular RNAs with advanced sequencing technologies in human complex diseases

Circular RNAs (circRNAs) are one category of non-coding RNAs that do not possess 5' caps and 3' free ends. Instead, they are derived in closed circle forms from pre-mRNAs by a non-canonical splicing mechanism named "back-splicing." CircRNAs were discovered four decades ago, initially called "scrambled exons." Compared to linear RNAs, the expression levels of circRNAs are considerably lower, and it is challenging to identify circRNAs specifically. Thus, the biological relevance of circRNAs has been underappreciated until the advancement of next generation sequencing (NGS) technology. The biological insights of circRNAs, such as their tissue-specific expression patterns, biogenesis factors, and functional effects in complex diseases, namely human cancers, have been extensively explored in the last decade. With the invention of the third generation sequencing (TGS) with longer sequencing reads and newly designed strategies to characterize full-length circRNAs, the panorama of circRNAs in human complex diseases could be further unveiled. In this review, we first introduce the history of circular RNA detection. Next, we describe widely adopted NGS-based methods and the recently established TGS-based approaches capable of characterizing circRNAs in full-length. We then summarize data resources and representative circRNA functional studies related to human complex diseases. In the last section, we reviewed computational tools and discuss the potential advantages of utilizing advanced sequencing approaches to a functional interpretation of full-length circRNAs in complex diseases. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Disease.

Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition

Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied. Methods: In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location. Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.

iPromoter-CLA: Identifying promoters and their strength by deep capsule networks with bidirectional long short-term memory

BACKGROUND AND OBJECTIVE

The promoter is a fragment of DNA and a specific sequence with transcriptional regulation function in DNA. Promoters are located upstream at the transcription start site, which is used to initiate downstream gene expression. So far, promoter identification is mainly achieved by biological methods, which often require more effort. It has become a more effective classification and prediction method to identify promoter types through computational methods.

METHODS

In this study, we proposed a new capsule network and recurrent neural network hybrid model to identify promoters and predict their strength. Firstly, we used one-hot to encode DNA sequence. Secondly, we used three one-dimensional convolutional layers, a one-dimensional convolutional capsule layer and digit capsule layer to learn local features. Thirdly, a bidirectional long short-time memory was utilized to extract global features. Finally, we adopted the self-attention mechanism to improve the contribution of relatively important features, which further enhances the performance of the model.

RESULTS

Our model attains a cross-validation accuracy of 86% and 73.46% in prokaryotic promoter recognition and their strength prediction, which showcases a better performance compared with the existing approaches in both the first layer promoter identification and the second layer promoter's strength prediction.

CONCLUSIONS

our model not only combines convolutional neural network and capsule layer but also uses a self-attention mechanism to better capture hidden information features from the perspective of sequence. Thus, we hope that our model can be widely applied to other components.

A pseudo-Siamese framework for circRNA-RBP binding sites prediction integrating BiLSTM and soft attention mechanism

Circular RNAs (circRNAs) are widely expressed in tissues and play a key role in diseases through interacting with RNA binding proteins (RBPs). Since the high cost of traditional technology, computational methods are developed to identify the binding sites between circRNAs and RBPs. Unfortunately, these methods suffer from the insufficient learning of features and the single classification of output. To address these limitations, we propose a novel method named circ-pSBLA which constructs a pseudo-Siamese framework integrating Bi-directional long short-term memory (BiLSTM) network and soft attention mechanism for circRNA-RBP binding sites prediction. Softmax function and CatBoost are adopted to classify, respectively, and then a pseudo-Siamese framework is constructed. circ-pSBLA combines them to get final output. To validate the effectiveness of circ-pSBLA, we compare it with other state-of-the-art methods and carry out an ablation experiment on 17 sub-datasets. Moreover, we do motif analysis on 3 sub-datasets. The results show that circ-pSBLA achieves superior performance and outperforms other methods. All supporting source codes can be downloaded from https://github.com/gyj9811/circ-pSBLA.

PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest

Promoters short DNA sequences play vital roles in initiating gene transcription. However, it remains a challenge to identify promoters using conventional experiment techniques in a high-throughput manner. To this end, several computational predictors based on machine learning models have been developed, while their performance is unsatisfactory. In this study, we proposed a novel two-layer predictor, called PredPromoter-MF(2L), based on multi-source feature fusion and ensemble learning. PredPromoter-MF(2L) was developed based on various deep features learned by a pre-trained deep learning network model and sequence-derived features. Feature selection based on XGBoost was applied to reduce fused features dimensions, and a cascade deep forest model was trained on the selected feature subset for promoter prediction. The results both fivefold cross-validation and independent test demonstrated that PredPromoter-MF(2L) outperformed state-of-the-art methods.

Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks

BACKGROUND

Circular RNAs (CircRNAs) play critical roles in gene expression regulation and disease development. Understanding the regulation mechanism of CircRNAs formation can help reveal the role of CircRNAs in various biological processes mentioned above. Back-splicing is important for CircRNAs formation. Back-splicing sites prediction helps uncover the mysteries of CircRNAs formation. Several methods were proposed for back-splicing sites prediction or circRNA-realted prediction tasks. Model performance was constrained by poor feature learning and using ability.

RESULTS

In this study, CircCNN was proposed to predict pre-mRNA back-splicing sites. Convolution neural network and batch normalization are the main parts of CircCNN. Experimental results on three datasets show that CircCNN outperforms other baseline models. Moreover, PPM (Position Probability Matrix) features extract by CircCNN were converted as motifs. Further analysis reveals that some of motifs found by CircCNN match known motifs involved in gene expression regulation, the distribution of motif and special short sequence is important for pre-mRNA back-splicing.

CONCLUSIONS

In general, the findings in this study provide a new direction for exploring CircRNA-related gene expression regulatory mechanism and identifying potential targets for complex malignant diseases. The datasets and source code of this study are freely available at: https://github.com/szhh521/CircCNN .

A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia

Immune thrombocytopenia (ITP) is an autoimmune disease with the typical symptom of a low platelet count in blood. ITP demonstrated age and sex biases in both occurrences and prognosis, and adult ITP was mainly induced by the living environments. The current diagnosis guideline lacks the integration of molecular heterogenicity. This study recruited the largest cohort of platelet transcriptome samples. A comprehensive procedure of feature selection, feature engineering, and stacking classification was carried out to detect the ITP biomarkers using RNA sequencing (RNA-seq) transcriptomes. The 40 detected biomarkers were loaded to train the final ITP detection model, with an overall accuracy 0.974. The biomarkers suggested that ITP onset may be associated with various transcribed components, including protein-coding genes, long intergenic non-coding RNA (lincRNA) genes, and pseudogenes with apparent transcriptions. The delivered ITP detection model may also be utilized as a complementary ITP diagnosis tool. The code and the example dataset is freely available on http://www.healthinformaticslab.org/supp/resources.php

Construction and Simulation of Music Style Prediction Model under Improved Sparse Neural Network

This paper designs and implements a music style prediction system using an improved sparse neural network, aiming to provide users with personalized music lists that match their interests. This paper firstly introduces how to combine the restricted Boltzmann machine model and recommendation algorithm and proposes a method to extract data features—setting a threshold to extract data features, and then, based on this, this paper introduces an improved K-Item RBM by weighted fusion of RBM recommendation algorithm and Item’s recommendation algorithm. Finally, the algorithm model is trained and predicted by the extracted features, and the experimental comparison analysis shows that the K-Item RBM algorithm can reduce the error between the predicted data and the real data and improve the performance of the recommendation system; in addition, to improve the accuracy of the recommendation, this paper introduces an improved CNN-CF neural network recommendation algorithm, which uses a convolutional neural network (CNN) to extract. The algorithm uses a convolutional neural network (CNN) to extract text features from the dataset, then trains the algorithm model, and finally makes personalized recommendations to users. The system can crawl user and music data and complete preprocessing of data such as deduplication, word separation, and keyword extraction. In this paper, we define the prediction evaluation criteria with the evaluation index F as the core and compare and analyse the prediction effect of four models longitudinally. The experimental results show that the music style prediction model based on the improved sparse neural network has a higher evaluation index value F and better prediction performance than the two-time series prediction models; compared with the general sparse neural network music style prediction model, the improved sparse neural network music style prediction model has an increased evaluation index value F for prediction ability, and the overall prediction effect is better and the prediction ability is significantly improved. The system can judge the appropriate recommendation algorithm according to the actual situation of the user and music data information and realize the continuously personalized music list recommendation for users to meet their music needs.

HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional network

Identifying genome-wide binding events between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) can greatly facilitate our understanding of functional mechanisms within circRNAs. Thanks to the development of cross-linked immunoprecipitation sequencing technology, large amounts of genome-wide circRNA binding event data have accumulated, providing opportunities for designing high-performance computational models to discriminate RBP interaction sites and thus to interpret the biological significance of circRNAs. Unfortunately, there are still no computational models sufficiently flexible to accommodate circRNAs from different data scales and with various degrees of feature representation. Here, we present HCRNet, a novel end-to-end framework for identification of circRNA-RBP binding events. To capture the hierarchical relationships, the multi-source biological information is fused to represent circRNAs, including various natural language sequence features. Furthermore, a deep temporal convolutional network incorporating global expectation pooling was developed to exploit the latent nucleotide dependencies in an exhaustive manner. We benchmarked HCRNet on 37 circRNA datasets and 31 linear RNA datasets to demonstrate the effectiveness of our proposed method. To evaluate further the model's robustness, we performed HCRNet on a full-length dataset containing 740 circRNAs. Results indicate that HCRNet generally outperforms existing methods. In addition, motif analyses were conducted to exhibit the interpretability of HCRNet on circRNAs. All supporting source code and data can be downloaded from https://github.com/yangyn533/HCRNet and https://doi.org/10.6084/m9.figshare.16943722.v1. And the web server of HCRNet is publicly accessible at http://39.104.118.143:5001/.

A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning

Drug-drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug-drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git.

More and more evidences show that circular RNA can directly bind to proteins and participate in countless different biological processes. The calculation method can quickly and accurately predict the binding site of circular RNA and RBP. In order to identify the interaction of circRNA with 37 different types of circRNA binding proteins, we developed an integrated deep learning network based on hierarchical network, called CRBPDL. It can effectively learn high-level feature representations. The performance of the model was verified through comparative experiments of different feature extraction algorithms, different deep learning models and classifier models. Moreover, the CRBPDL model was applied to 31 linear RNAs, and the effectiveness of our method was proved by comparison with the results of current excellent algorithms. It is expected that the CRBPDL model can effectively predict the binding site of circular RNA-RBP and provide reliable candidates for further biological experiments.

Staem5: A novel computational approachfor accurate prediction of m5C site

5-Methylcytosine (m5C) is an important post-transcriptional modification that has been extensively found in multiple types of RNAs. Many studies have shown that m5C plays vital roles in many biological functions, such as RNA structure stability and metabolism. Computational approaches act as an efficient way to identify m5C sites from high-throughput RNA sequence data and help interpret the functional mechanism of this important modification. This study proposed a novel species-specific computational approach, Staem5, to accurately predict RNA m5C sites in Mus musculus and Arabidopsis thaliana. Staem5 was developed by employing feature fusion tactics to leverage informatic sequence profiles, and a stacking ensemble learning framework combined five popular machine learning algorithms. Extensive benchmarking tests demonstrated that Staem5 outperformed state-of-the-art approaches in both cross-validation and independent tests. We provide the source code of Staem5, which is publicly available at https://github.com/Cxd-626/Staem5.git.

Accurate identification of RNA D modification using multiple features

As one of the common post-transcriptional modifications in tRNAs, dihydrouridine (D) has prominent effects on regulating the flexibility of tRNA as well as cancerous diseases. Facing with the expensive and time-consuming sequencing techniques to detect D modification, precise computational tools can largely promote the progress of molecular mechanisms and medical developments. We proposed a novel predictor, called iRNAD_XGBoost, to identify potential D sites using multiple RNA sequence representations. In this method, by considering the imbalance problem using hybrid sampling method SMOTEEEN, the XGBoost-selected top 30 features are applied to construct model. The optimized model showed high Sn and Sp values of 97.13% and 97.38% over jackknife test, respectively. For the independent experiment, these two metrics separately achieved 91.67% and 94.74%. Compared with iRNAD method, this model illustrated high generalizability and consistent prediction efficiencies for positive and negative samples, which yielded satisfactory MCC scores of 0.94 and 0.86, respectively. It is inferred that the chemical property and nucleotide density features (CPND), electron-ion interaction pseudopotential (EIIP and PseEIIP) as well as dinucleotide composition (DNC) are crucial to the recognition of D modification. The proposed predictor is a promising tool to help experimental biologists investigate molecular functions.

Positive-unlabeled learning in bioinformatics and computational biology: a brief review

Conventional supervised binary classification algorithms have been widely applied to address significant research questions using biological and biomedical data. This classification scheme requires two fully labeled classes of data (e.g. positive and negative samples) to train a classification model. However, in many bioinformatics applications, labeling data is laborious, and the negative samples might be potentially mislabeled due to the limited sensitivity of the experimental equipment. The positive unlabeled (PU) learning scheme was therefore proposed to enable the classifier to learn directly from limited positive samples and a large number of unlabeled samples (i.e. a mixture of positive or negative samples). To date, several PU learning algorithms have been developed to address various biological questions, such as sequence identification, functional site characterization and interaction prediction. In this paper, we revisit a collection of 29 state-of-the-art PU learning bioinformatic applications to address various biological questions. Various important aspects are extensively discussed, including PU learning methodology, biological application, classifier design and evaluation strategy. We also comment on the existing issues of PU learning and offer our perspectives for the future development of PU learning applications. We anticipate that our work serves as an instrumental guideline for a better understanding of the PU learning framework in bioinformatics and further developing next-generation PU learning frameworks for critical biological applications.

circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier

Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is crucial to study the binding sites of RBPs on circRNAs. Although many methods, including traditional machine learning and deep learning, have been developed to predict the interactions between RNAs and RBPs, and most of them are focused on linear RNAs. At present, few studies have been done on the binding relationships between circRNAs and RBPs. Thus, in-depth research is urgently needed. In the existing circRNA-RBP binding site prediction methods, circRNA sequences are the main research subjects, but the relevant characteristics of circRNAs have not been fully exploited, such as the structure and composition information of circRNA sequences. Some methods have extracted different views to construct recognition models, but how to efficiently use the multi-view data to construct recognition models is still not well studied. Considering the above problems, this paper proposes a multi-view classification method called DMSK based on multi-view deep learning, subspace learning and multi-view classifier for the identification of circRNA-RBP interaction sites. In the DMSK method, first, we converted circRNA sequences into pseudo-amino acid sequences and pseudo-dipeptide components for extracting high-dimensional sequence features and component features of circRNAs, respectively. Then, the structure prediction method RNAfold was used to predict the secondary structure of the RNA sequences, and the sequence embedding model was used to extract the context-dependent features. Next, we fed the above four views' raw features to a hybrid network, which is composed of a convolutional neural network and a long short-term memory network, to obtain the deep features of circRNAs. Furthermore, we used view-weighted generalized canonical correlation analysis to extract four views' common features by subspace learning. Finally, the learned subspace common features and multi-view deep features were fed to train the downstream multi-view TSK fuzzy system to construct a fuzzy rule and fuzzy inference-based multi-view classifier. The trained classifier was used to predict the specific positions of the RBP binding sites on the circRNAs. The experiments show that the prediction performance of the proposed method DMSK has been improved compared with the existing methods. The code and dataset of this study are available at https://github.com/Rebecca3150/DMSK.

Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles

Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often time-consuming and expensive. In this study, we proposed an accurate computational model, called AOP-HMM, to predict AOPs by extracting discriminatory evolutionary features from hidden Markov model (HMM) profiles. First, auto cross-covariance (ACC) variables were applied to transform the HMM profiles into fixed-length feature vectors. Then, we performed the analysis of variance (ANOVA) method to reduce the dimensionality of the raw feature space. Finally, a support vector machine (SVM) classifier was adopted to conduct the prediction of AOPs. To comprehensively evaluate the performance of the proposed AOP-HMM model, the 10-fold cross-validation (CV), the jackknife CV, and the independent test were carried out on two widely used benchmark datasets. The experimental results demonstrated that AOP-HMM outperformed most of the existing methods and could be used to quickly annotate AOPs and guide the experimental process.

Recognizing binding sites of poorly characterized RNA-binding proteins on circular RNAs using attention Siamese network

Circular RNAs (circRNAs) interact with RNA-binding proteins (RBPs) to play crucial roles in gene regulation and disease development. Computational approaches have attracted much attention to quickly predict highly potential RBP binding sites on circRNAs using the sequence or structure statistical binding knowledge. Deep learning is one of the popular learning models in this area but usually requires a lot of labeled training data. It would perform unsatisfactorily for the less characterized RBPs with a limited number of known target circRNAs. How to improve the prediction performance for such small-size labeled characterized RBPs is a challenging task for deep learning-based models. In this study, we propose an RBP-specific method iDeepC for predicting RBP binding sites on circRNAs from sequences. It adopts a Siamese neural network consisting of a lightweight attention module and a metric module. We have found that Siamese neural network effectively enhances the network capability of capturing mutual information between circRNAs with pairwise metric learning. To further deal with the small-sample size problem, we have performed the pretraining using available labeled data from other RBPs and also demonstrate the efficacy of this transfer-learning pipeline. We comprehensively evaluated iDeepC on the benchmark datasets of RBP-binding circRNAs, and the results suggest iDeepC achieving promising results on the poorly characterized RBPs. The source code is available at https://github.com/hehew321/iDeepC.

Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification

Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evaluated in the preclinical and clinical trials. Accurate identification of ACPs has received considerable attention in recent years; as such, a number of machine learning-based methods for in silico identification of ACPs have been developed. These methods promote the research on the mechanism of ACPs therapeutics against cancer to some extent. There is a vast difference in these methods in terms of their training/testing datasets, machine learning algorithms, feature encoding schemes, feature selection methods and evaluation strategies used. Therefore, it is desirable to summarize the advantages and disadvantages of the existing methods, provide useful insights and suggestions for the development and improvement of novel computational tools to characterize and identify ACPs. With this in mind, we firstly comprehensively investigate 16 state-of-the-art predictors for ACPs in terms of their core algorithms, feature encoding schemes, performance evaluation metrics and webserver/software usability. Then, comprehensive performance assessment is conducted to evaluate the robustness and scalability of the existing predictors using a well-prepared benchmark dataset. We provide potential strategies for the model performance improvement. Moreover, we propose a novel ensemble learning framework, termed ACPredStackL, for the accurate identification of ACPs. ACPredStackL is developed based on the stacking ensemble strategy combined with SVM, Naïve Bayesian, lightGBM and KNN. Empirical benchmarking experiments against the state-of-the-art methods demonstrate that ACPredStackL achieves a comparative performance for predicting ACPs. The webserver and source code of ACPredStackL is freely available at http://bigdata.biocie.cn/ACPredStackL/ and https://github.com/liangxiaoq/ACPredStackL, respectively.

iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization

Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development of new predictive models, which depends on the availability of effective tools that support these efforts. We introduce iLearnPlus, the first machine-learning platform with graphical- and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences. iLearnPlus provides a comprehensive set of algorithms and automates sequence-based feature extraction and analysis, construction and deployment of models, assessment of predictive performance, statistical analysis, and data visualization; all without programming. iLearnPlus includes a wide range of feature sets which encode information from the input sequences and over twenty machine-learning algorithms that cover several deep-learning approaches, outnumbering the current solutions by a wide margin. Our solution caters to experienced bioinformaticians, given the broad range of options, and biologists with no programming background, given the point-and-click interface and easy-to-follow design process. We showcase iLearnPlus with two case studies concerning prediction of long noncoding RNAs (lncRNAs) from RNA transcripts and prediction of crotonylation sites in protein chains. iLearnPlus is an open-source platform available at https://github.com/Superzchen/iLearnPlus/ with the webserver at http://ilearnplus.erc.monash.edu/.

Identification of multiple RNAs using feature fusion

Detection of novel transcripts with deep sequencing has increased the demand for computational algorithms as their identification and validation using in vivo techniques is time-consuming, costly and unreliable. Most of these discovered transcripts belong to non-coding RNAs, a large group known for their diverse functional roles but lacks the common taxonomy. Thus, upon the identification of the absence of coding potential in them, it is crucial to recognize their prime functional category. To address this heterogeneity issue, we divide the ncRNAs into three classes and present RNA classifier (RNAC) that categorizes the RNAs into coding, housekeeping, small non-coding and long non-coding classes. RNAC utilizes the alignment-based genomic descriptors to extract statistical, local binary patterns and histogram features and fuse them to construct the classification models with extreme gradient boosting. The experiments are performed on four species, and the performance is assessed on multiclass and conventional binary classification (coding versus no-coding) problems. The proposed approach achieved >93% accuracy on both classification problems and also outperformed other well-known existing methods in coding potential prediction. This validates the usefulness of feature fusion for improved performance on both types of classification problems. Hence, RNAC is a valuable tool for the accurate identification of multiple RNAs .

RIFS2D: A two-dimensional version of a randomly restarted incremental feature selection algorithm with an application for detecting low-ranked biomarkers

The era of big data introduces both opportunities and challenges for biomedical researchers. One of the inherent difficulties in the biomedical research field is to recruit large cohorts of samples, while high-throughput biotechnologies may produce thousands or even millions of features for each sample. Researchers tend to evaluate the individual correlation of each feature with the class label and use the incremental feature selection (IFS) strategy to select the top-ranked features with the best prediction performance. Recent experimental data showed that a subset of continuously ranked features randomly restarted from a low-ranked feature (an RIFS block) may outperform the subset of top-ranked features. This study proposed a feature selection Algorithm RIFS2D by integrating multiple RIFS blocks. A comprehensive comparative experiment was conducted with the IFS, RIFS and existing feature selection algorithms and demonstrated that a subset of low-ranked features may also achieve promising prediction performance. This study suggested that a prediction model with promising performance may be trained by low-ranked features, even when top-ranked features did not achieve satisfying prediction performance. Further comparative experiments were conducted between RIFS2D and t-tests for the detection of early-stage breast cancer. The data showed that the RIFS2D-recommended features achieved better prediction accuracy and were targeted by more drugs than the t-test top-ranked features.

DeCban: Prediction of circRNA-RBP Interaction Sites by Using Double Embeddings and Cross-Branch Attention Networks

Circular RNAs (circRNAs), as a rising star in the RNA world, play important roles in various biological processes. Understanding the interactions between circRNAs and RNA binding proteins (RBPs) can help reveal the functions of circRNAs. For the past decade, the emergence of high-throughput experimental data, like CLIP-Seq, has made the computational identification of RNA-protein interactions (RPIs) possible based on machine learning methods. However, as the underlying mechanisms of RPIs have not been fully understood yet and the information sources of circRNAs are limited, the computational tools for predicting circRNA-RBP interactions have been very few. In this study, we propose a deep learning method to identify circRNA-RBP interactions, called DeCban, which is featured by hybrid double embeddings for representing RNA sequences and a cross-branch attention neural network for classification. To capture more information from RNA sequences, the double embeddings include pre-trained embedding vectors for both RNA segments and their converted amino acids. Meanwhile, the cross-branch attention network aims to address the learning of very long sequences by integrating features of different scales and focusing on important information. The experimental results on 37 benchmark datasets show that both double embeddings and the cross-branch attention model contribute to the improvement of performance. DeCban outperforms the mainstream deep learning-based methods on not only prediction accuracy but also computational efficiency. The data sets and source code of this study are freely available at: https://github.com/AaronYll/DECban.

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions

The interactions between miRNAs and long non-coding RNAs (lncRNAs) are subject to intensive recent studies due to its critical role in gene regulations. Computational prediction of lncRNA-miRNA interactions has become a popular alternative strategy to the experimental methods for identification of underlying interactions. It is desirable to develop the machine learning-based models for prediction of lncRNA-miRNA based on the experimentally validated interactions between lncRNAs and miRNAs. The accuracy and robustness of existing models based on machine learning techniques are subject to further improvement. Considering that the attributes of lncRNA and miRNA contribute key importance in the interaction between these two RNAs, a deep learning model, named LMI-DForest, is proposed here by combining the deep forest and autoencoder strategies. Systematic comparison on the experiment validated datasets for lncRNA-miRNA interaction datasets demonstrates that the proposed method consistently shows superior performance over the other machine learning models in the lncRNA-miRNA interaction prediction.

NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data

Non-classically secreted proteins (NCSPs) are proteins that are located in the extracellular environment, although there is a lack of known signal peptides or secretion motifs. They usually perform different biological functions in intracellular and extracellular environments, and several of their biological functions are linked to bacterial virulence and cell defence. Accurate protein localization is essential for all living organisms, however, the performance of existing methods developed for NCSP identification has been unsatisfactory and in particular suffer from data deficiency and possible overfitting problems. Further improvement is desirable, especially to address the lack of informative features and mining subset-specific features in imbalanced datasets. In the present study, a new computational predictor was developed for NCSP prediction of gram-positive bacteria. First, to address the possible prediction bias caused by the data imbalance problem, ten balanced subdatasets were generated for ensemble model construction. Then, the F-score algorithm combined with sequential forward search was used to strengthen the feature representation ability for each of the training subdatasets. Third, the subset-specific optimal feature combination process was adopted to characterize the original data from different aspects, and all subdataset-based models were integrated into a unified model, NonClasGP-Pred, which achieved an excellent performance with an accuracy of 93.23 %, a sensitivity of 100 %, a specificity of 89.01 %, a Matthew's correlation coefficient of 87.68 % and an area under the curve value of 0.9975 for ten-fold cross-validation. Based on assessment on the independent test dataset, the proposed model outperformed state-of-the-art available toolkits. For availability and implementation, see: http://lab.malab.cn/~wangchao/softwares/NonClasGP/.

Computational prediction of species-specific yeast DNA replication origin via iterative feature representation

Deoxyribonucleic acid replication is one of the most crucial tasks taking place in the cell, and it has to be precisely regulated. This process is initiated in the replication origins (ORIs), and thus it is essential to identify such sites for a deeper understanding of the cellular processes and functions related to the regulation of gene expression. Considering the important tasks performed by ORIs, several experimental and computational approaches have been developed in the prediction of such sites. However, existing computational predictors for ORIs have certain curbs, such as building only single-feature encoding models, limited systematic feature engineering efforts and failure to validate model robustness. Hence, we developed a novel species-specific yeast predictor called yORIpred that accurately identify ORIs in the yeast genomes. To develop yORIpred, we first constructed optimal 40 baseline models by exploring eight different sequence-based encodings and five different machine learning classifiers. Subsequently, the predicted probability of 40 models was considered as the novel feature vector and carried out iterative feature learning approach independently using five different classifiers. Our systematic analysis revealed that the feature representation learned by the support vector machine algorithm (yORIpred) could well discriminate the distribution characteristics between ORIs and non-ORIs when compared with the other four algorithms. Comprehensive benchmarking experiments showed that yORIpred achieved superior and stable performance when compared with the existing predictors on the same training datasets. Furthermore, independent evaluation showcased the best and accurate performance of yORIpred thus underscoring the significance of iterative feature representation. To facilitate the users in obtaining their desired results without undergoing any mathematical, statistical or computational hassles, we developed a web server for the yORIpred predictor, which is available at: http://thegleelab.org/yORIpred.