CircRNAs are a particular single-stranded, circular structure and “non-coding” RNA molecules, with various biological functions . Existing studies have demonstrated the fundamental role of circRNAs in gene expression regulation and their significant involvement in the development of diverse complex diseases. Predicting the protein binding sites in circRNA can aid in comprehending the regulation mechanism involved in circRNA-protein binding during gene expression and facilitate the investigation of potential diagnosis and treatment strategies for complex diseases. This review begins by introducing the concept and functions of circRNAs, as well as their involvement in gene expression regulation . Then, some critical and publicly accessible databases about circRNA annotation, protein annotation, circRNA-protein binding were listed. Next, we present a brief introduction to the computational model for predicting circRNA-protein binding, followed by model performance comparison and suggestions for non-computer science experts on model selection. Finally, we examine the problems, limitations, and advantages of computational models and explore the further direction of circRNA-protein prediction, such as developing new and complex computational models, introducing complex biological sequence encoding schemes, and integrating additional biological data related to circRNA-protein binding.

Proteins are the material foundation of life, and they are organic macromolecules that make up the basic organic matter of cells. Therefore, proteins can be considered as the main bearers of life activities. Proteins are important components that make up all cells and tissues in an organism. All critical elements of an organism require the participation of proteins, and the most important thing is that they are related to life phenomena. The transportation and localization of proteins within organelles is a complex and delicate process that involves multiple steps and mechanisms. Organelle proteins are an essential element in several biological processions. In this work, we proposed TAPE_selection methods to reduce the useless information of the Tasks Assessing Protein Embed-dings (TAPE) feature in some organelle proteins, which mainly include plant vacuole proteins (PVPs) and peroxidase ones. In order to reduce the useless information, we employed some feature selection Strategies, including the Chi-Squared Test, Minimum Redundancy Maximum Relevance(mRMR), and Neighborhood Components Analysis (NCA). With the selected feature, the Proper Orthogonal Decomposition (POD) and t-distributed Stochastic Neighbor Embedding (t-SNE) were employed to reduce the reconstructed feature scale.

Pneumonia, especially corona virus disease 2019 (COVID-19), can lead to serious acute lung injury, acute respiratory distress syndrome, multiple organ failure and even death. Thus it is an urgent task for developing high-efficiency, low-toxicity and targeted drugs according to pathogenesis of coronavirus. In this paper, a novel disease-related compound identification model-based capsule network (CapsNet) is proposed. According to pneumonia-related keywords, the prescriptions and active components related to the pharmacological mechanism of disease are collected and extracted in order to construct training set. The features of each component are extracted as the input layer of capsule network. CapsNet is trained and utilized to identify the pneumonia-related compounds in Qingre Jiedu injection. The experiment results show that CapsNet can identify disease-related compounds more accurately than SVM, RF, gcForest and forgeNet.

Emerging evidence indicates that the abnormal expression of miRNAs involves in the evolution and progression of various human complex diseases. Identifying disease-related miRNAs as new biomarkers can promote the development of disease pathology and clinical medicine. However, designing biological experiments to validate disease-related miRNAs is usually time-consuming and expensive. Therefore, it is urgent to design effective computational methods for predicting potential miRNA-disease associations. Inspired by the great progress of graph neural networks in link prediction, we propose a novel graph auto-encoder model, named GAEMDA, to identify the potential miRNA-disease associations in an end-to-end manner. More specifically, the GAEMDA model applies a graph neural networks-based encoder, which contains aggregator function and multi-layer perceptron for aggregating nodes' neighborhood information, to generate the low-dimensional embeddings of miRNA and disease nodes and realize the effective fusion of heterogeneous information. Then, the embeddings of miRNA and disease nodes are fed into a bilinear decoder to identify the potential links between miRNA and disease nodes. The experimental results indicate that GAEMDA achieves the average area under the curve of $93.56\pm 0.44\%$ under 5-fold cross-validation. Besides, we further carried out case studies on colon neoplasms, esophageal neoplasms and kidney neoplasms. As a result, 48 of the top 50 predicted miRNAs associated with these diseases are confirmed by the database of differentially expressed miRNAs in human cancers and microRNA deregulation in human disease database, respectively. The satisfactory prediction performance suggests that GAEMDA model could serve as a reliable tool to guide the following researches on the regulatory role of miRNAs. Besides, the source codes are available at https://github.com/chimianbuhetang/GAEMDA.

BACKGROUND: The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between genes or proteins and understand the intrinsic mechanisms of biological systems have become an urgent need and study hotspot. RESULTS: In order to forecast gene expression data and identify more accurate gene regulatory network, complex-valued version of ordinary differential equation (CVODE) is proposed in this paper. In order to optimize CVODE model, a complex-valued hybrid evolutionary method based on Grammar-guided genetic programming and complex-valued firefly algorithm is presented. CONCLUSIONS: When tested on three real gene expression datasets from E. coli and Human Cell, the experiment results suggest that CVODE model could improve 20-50% prediction accuracy of gene expression data, which could also infer more true-positive regulatory relationships and less false-positive regulations than ordinary differential equation.

Although convolutional neural networks (CNN) have outperformed conventional methods in predicting the sequence specificities of protein-DNA binding in recent years, they do not take full advantage of the intrinsic weakly-supervised information of DNA sequences that a bound sequence may contain multiple TFBS(s). Here, we propose a weakly-supervised convolutional neural network architecture (WSCNN), combining multiple-instance learning (MIL) with CNN, to further boost the performance of predicting protein-DNA binding. WSCNN first divides each DNA sequence into multiple overlapping subsequences (instances) with a sliding window, and then separately models each instance using CNN, and finally fuses the predicted scores of all instances in the same bag using four fusion methods, including Max, Average, Linear Regression, and Top-Bottom Instances. The experimental results on in vivo and in vitro datasets illustrate the performance of the proposed approach. Moreover, models built on in vitro data using WSCNN can predict in vivo protein-DNA binding with good accuracy. In addition, we give a quantitative analysis of the importance of the reverse-complement mode in predicting in vivo protein-DNA binding, and explain why not directly use advanced pooling layers to combine MIL with CNN, through a series of experiments.

Post translational modification plays a significiant role in the biological processing. The potential post translational modification is composed of the center sites and the adjacent amino acid residues which are fundamental protein sequence residues. It can be helpful to perform their biological functions and contribute to understanding the molecular mechanisms that are the foundations of protein design and drug design. The existing algorithms of predicting modified sites often have some shortcomings, such as lower stability and accuracy. In this paper, a combination of physical, chemical, statistical, and biological properties of a protein have been ulitized as the features, and a novel framework is proposed to predict a protein's post translational modification sites. The multi-layer neural network and support vector machine are invoked to predict the potential modified sites with the selected features that include the compositions of amino acid residues, the E-H description of protein segments, and several properties from the AAIndex database. Being aware of the possible redundant information, the feature selection is proposed in the propocessing step in this research. The experimental results show that the proposed method has the ability to improve the accuracy in this classification issue.

The problem of object localization in image appear ubiquitously in computer vision applications including image classification, object detection and visual tracking. Recently, it is shown that multiple-instance learning (MIL) which is regarded as the fourth machine learning framework compared with supervised learning, unsupervised learning and reinforce learning has been verified that will get good effect in object localization in images. In this paper, we propose a novel method to solve the classical MIL problem, named Cross-Validated Smooth Multi-Instance learning (CVS-MIL). We treat the positiveness of instance as a continuous variable. The softmax model is used to bring a bridge between instances and bags and jointly optimize the bag label and instance label in a unified framework. The extensive experiments demonstrate that CVS-MIL consistently achieves superior performance on various MIL benchmarks. Moreover, we simply applied CVS-MIL to a challenging vision task, common object discovery. The state-of-the-art results of object discovery on Pascal VOC datasets further confirm the advantages of the proposed method.

The problem of matrix approximation appears ubiquitously in recommendation systems, computer vision and text mining. The prevailing assumption is that the partially observed matrix has a low-rank or can be well approximated by a low-rank matrix. However, this assumption is strictly that the partially observed matrix is globally low rank. In this paper, we propose a local sensitive formulation of matrix approximation which relaxes the global low-rank assumption, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We solve the problem by an efficient way based on the alternating direction method of multipliers (ADMM). Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks.

BACKGROUND: Accumulating biological and clinical reports have indicated that imbalance of microbial community is closely associated with occurrence and development of various complex human diseases. Identifying potential microbe-disease associations, which could provide better understanding of disease pathology and further boost disease diagnostic and prognostic, has attracted more and more attention. However, hardly any computational models have been developed for large scale microbe-disease association prediction. RESULTS: In this article, based on the assumption that microbes with similar functions tend to share similar association or non-association patterns with similar diseases and vice versa, we proposed the model of Network Consistency Projection for Human Microbe-Disease Association prediction (NCPHMDA) by integrating known microbe-disease associations and Gaussian interaction profile kernel similarity for microbes and diseases. NCPHMDA yielded outstanding AUCs of 0.9039, 0.7953 and average AUC of 0.8918 in global leave-one-out cross validation, local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, colon cancer, asthma and type 2 diabetes were taken as independent case studies, where 9, 9 and 8 out of the top 10 predicted microbes were successfully confirmed by recent published clinical literature. CONCLUSION: NCPHMDA is a non-parametric universal network-based method which can simultaneously predict associated microbes for investigated diseases but does not require negative samples. It is anticipated that NCPHMDA would become an effective biological resource for clinical experimental guidance.

Accurate classification on protein structural is playing an important role in Bioinformatics. An increase in evidence demonstrates that a variety of classification methods have been employed in such a field. In this research, the features of amino acids composition, secondary structure's feature, and correlation coefficient of amino acid dimers and amino acid triplets have been used. Flexible neutral tree (FNT), a particular tree structure neutral network, has been employed as the classification model in the protein structures' classification framework. Considering different feature groups owing diverse roles in the model, impact factors of different groups have been put forward in this research. In order to evaluate different impact factors, Impact Factors Scaling (IFS) algorithm, which aim at reducing redundant information of the selected features in some degree, have been put forward. To examine the performance of such framework, the 640, 1189, and ASTRAL datasets are employed as the low-homology protein structure benchmark datasets. Experimental results demonstrate that the performance of the proposed method is better than the other methods in the low-homology protein tertiary structures.

Lysine succinylation is one of most important types in protein post-translational modification, which is involved in many cellular processes and serious diseases. However, effective recognition of such sites with traditional experiment methods may seem to be treated as time-consuming and laborious. Those methods can hardly meet the need of efficient identification a great deal of succinylated sites at speed. In this work, several physicochemical properties of succinylated sites have been extracted, such as the physicochemical property of the amino acids. Flexible neural tree, which is employed as the classification model, was utilized to integrate above mentioned features for generating a novel lysine succinylation prediction framework named ILSES (identification lysine succinylation-sites with ensemble features classification). Such method owns the ability to combining diverse features to predict lysine succinylation with high accuracy and real time.

Built upon the hypergeometric distribution, the Fisher Exact Test score (FETS) and its variants offer a natural way of quantifying the level of TF binding site (TFBS) motif enrichment, and have been chosen as the objective functions of several widely used discriminant motif discovery methods, such as HOMER and DREME. In spite of its popularity and efficacy, FETS is non-smooth and non-differentiable, and is thus difficult to optimize numerically. To circumvent this limitation, existing tools that learn to optimize FETS either have to rely on discrete search strategies or indirect tuning of a few external parameters, which could hurt accuracy and fail to fully utilize the potential of input sequences to generate motifs. In this paper, we propose DirectFS, which is (to our best knowledge) the first FETS-based approach that allows direct learning of the motif parameters in continuous space. We show that when the resultant loss function is optimized in a coordinate-wise manner, the cost function of each resultant sub-problem is a piece-wise constant function, whose optimal value can be found exactly and efficiently. Further, a key step in each iteration of DirectFS requires finding the most statistically significant one among tens of thousands of Fisher's exact tests, which is solved efficiently using a novel `lookahead'-style algorithm. Experimental evaluations on ENCODE ChIP-seq data illustrate the performance of the proposed approach.