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BackgroundPolygenic risk scores (PRSs) are personalized assessments based on the cumulative effect of a larger number of genetic variants, and the PRS has been utilized to estimate the risk of a disease or disease-related trait for an individual. However, the capability of PRS can be interfered with by multiple factors spanning from the generating genome-wide association study (GWAS) summary to the PRS calculation. In this study, based on a large cohort of 2,781 ulcerative colitis (UC) cases and 2,876 healthy controls with available genotype array data, we systematically analyzed factors that could affect the power of PRS, including GWAS summary statistics, population stratification, impact of variants and imputation reference panels.ResultsWe eventually established the best model achieving an AUC of 0.715 (95% CI 0.702-0.729) in prediction using 2,669 UC cases and 2,846 healthy controls of genetically identified Europeans. And samples in the top 5% of the PRS distribution had more than seven times higher risk of UC than samples in the lowest 95% (OR=7.014, 95% CI 4.977-9.884).ConclusionOur analyses demonstrated that including population-specific, more associated SNPs and selecting GWAS summary statistics results from similar population structure can improve the power of PRS, strictly following the principle of focusing on one population in all aspects of generating PRS, which can be a cost-effective way to apply genotype array-derived PRS to practical risk estimation.

One of the greatest challenges in human genetics is deciphering the link between functional variants in non-coding sequences and the pathophysiology of complex diseases. To address this issue, many methods have been developed to sort functional single nucleotide variants (SNVs) for neutral SNVs in non-coding regions. In this study, we integrated well-established features and commonly used datasets, and merged them into large-scale datasets based on a random forest model, which yielded promising performance and outperformed some cutting-edge approaches. Our analyses of feature importance and data coverage also provide certain clues for future research in enhancing the prediction of functional non-coding SNVs. This article is protected by copyright. All rights reserved.

Cancer is one of the most dangerous diseases to human health. The accurate prediction of anticancer peptides (ACPs) would be valuable for the development and design of novel anticancer agents. Current deep neural network models have obtained state-of-the-art prediction accuracy for the ACP classification task. However, based on existing studies, it remains unclear which deep learning architecture achieves the best performance. Thus, in this study, we first present a systematic exploration of three important deep learning architectures: convolutional, recurrent, and convolutional-recurrent networks for distinguishing ACPs from non-ACPs. We find that the recurrent neural network with bidirectional long short-term memory cells is superior to other architectures. By utilizing the proposed model, we implement a sequence-based deep learning tool (DeepACP) to accurately predict the likelihood of a peptide exhibiting anticancer activity. The results indicate that DeepACP outperforms several existing methods and can be used as an effective tool for the prediction of anticancer peptides. Furthermore, we visualize and understand the deep learning model. We hope that our strategy can be extended to identify other types of peptides and may provide more assistance to the development of proteomics and new drugs., Graphical Abstract, Yu and colleagues exploited the deep learning technique to accurately identify anticancer peptides. A comprehensive look at peptide efficiencies and outcomes across primary sequences and model architectures yields a deep learning tool for users to predict the anticancer efficiency for a sequence of interest.

Deep learning has proven to be a powerful method with applications in various fields including image, language, and biomedical data. Thanks to the libraries and toolkits such as TensorFlow, PyTorch, and Keras, researchers can use different deep learning architectures and data sets for rapid modeling. However, the available implementations of neural networks using these toolkits are usually designed for a specific research and are difficult to transfer to other work. Here, we present autoBioSeqpy, a tool that uses deep learning for biological sequence classification. The advantage of this tool is its simplicity. Users only need to prepare the input data set and then use a command line interface. Then, autoBioSeqpy automatically executes a series of customizable steps including text reading, parameter initialization, sequence encoding, model loading, training, and evaluation. In addition, the tool provides various ready-to-apply and adapt model templates to improve the usability of these networks. We introduce the application of autoBioSeqpy on three biological sequence problems: the prediction of type III secreted proteins, protein subcellular localization, and CRISPR/Cas9 sgRNA activity. autoBioSeqpy is freely available with examples at https://github.com/jingry/autoBioSeqpy.

Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that allow a number of humans, plant and animal pathogens to inject virulence factors directly into the cytoplasm of eukaryotic cells. Export of effectors through T3SSs is critical for motility and virulence of most Gram-negative pathogens. Current computational methods can predict type III secreted effectors (T3SEs) from amino acid sequences, but due to algorithmic constraints, reliable and large-scale prediction of T3SEs in Gram-negative bacteria remains a challenge. Here, we present DeepT3 2.0 (http://advintbioinforlab.com/deept3/), a novel web server that integrates different deep learning models for genome-wide predicting T3SEs from a bacterium of interest. DeepT3 2.0 combines various deep learning architectures including convolutional, recurrent, convolutional-recurrent and multilayer neural networks to learn N-terminal representations of proteins specifically for T3SE prediction. Outcomes from the different models are processed and integrated for discriminating T3SEs and non-T3SEs. Because it leverages diverse models and an integrative deep learning framework, DeepT3 2.0 outperforms existing methods in validation datasets. In addition, the features learned from networks are analyzed and visualized to explain how models make their predictions. We propose DeepT3 2.0 as an integrated and accurate tool for the discovery of T3SEs.

Single-cell ATAC-seq (scATAC-seq), as the updating of ATAC-seq, provides a novel method for probing open chromatin sites. Currently, research of scATAC-seq is faced with the problem of high dimensionality and the inherent sparsity of the generated data. Recently, several works proposed the use of an autoencoder–decoder, a symmetry neural network architecture, and non-negative matrix factorization methods to characterize the high-dimensional data. To evaluate the performance of multiple methods, in this work, we performed a multiple comparison for characterizing scATAC-seq based on four kinds of auto-encoders known as a symmetry neural network, and two kinds of matrix factorization methods. Different sizes of latent features were used to generate the UMAP plots and for further K-means clustering. Using a gold-standard data set, we practically explored the performance among the methods and the number of latent features in a comprehensive way. Finally, we briefly discuss the underlying difficulties and future directions for scATAC-seq characterizing. As a result, the method designed for handling the sparsity outperforms other tools in the generated dataset.

Gram-negative bacteria can deliver secreted proteins (also known as secreted effectors) directly into host cells through type III secretion system (T3SS), type IV secretion system (T4SS), and type VI secretion system (T6SS) and cause various diseases. These secreted effectors are heavily involved in the interactions between bacteria and host cells, so their identification is crucial for the discovery and development of novel anti-bacterial drugs. It is currently challenging to accurately distinguish type III secreted effectors (T3SEs) and type IV secreted effectors (T4SEs) because neither T3SEs nor T4SEs contain N-terminal signal peptides, and some of these effectors have similar evolutionary conserved profiles and sequence motifs. To address this challenge, we develop a deep learning (DL) approach called DeepT3_4 to correctly classify T3SEs and T4SEs. We generate amino-acid character dictionary and sequence-based features extracted from effector proteins and subsequently implement these features into a hybrid model that integrates recurrent neural networks (RNNs) and deep neural networks (DNNs). After training the model, the hybrid neural network classifies secreted effectors into two different classes with an accuracy, F-value, and recall of over 80.0%. Our approach stands for the first DL approach for the classification of T3SEs and T4SEs, providing a promising supplementary tool for further secretome studies.

In clinical cancer research, it is a hot topic on how to accurately stratify patients based on genomic data. With the development of next-generation sequencing technology, more and more types of genomic features, such as mRNA expression level, can be used to distinguish cancer patients. Previous studies commonly stratified patients by using a single type of genomic features, which can only reflect one aspect of the cancer. In fact, multiscale genomic features will provide more information and may be helpful for clinical prediction. In addition, most of the conventional machine learning algorithms use a handcrafted gene set as features to construct models, which is generally selected by a statistical method with an arbitrary cut-off, e.g., p value < 0.05. The genes in the gene set are not necessarily related to the cancer and will make the model unreliable. Therefore, in our study, we thoroughly investigated the performance of different machine learning methods on stratifying breast cancer patients with a single type of genomic features. Then, we proposed a strategy, which can take into account the degree of correlation between genes and cancer patients, to identify the features from mRNAs and microRNAs, and evaluated the performance of the models with the new combined features of the multiscale genomic features. The results showed that, compared with the models constructed with a single type of features, the models with the multiscale genomic features generated by our proposed method achieved better performance on stratifying the ER status of breast cancer patients. Moreover, we found that the identified multiscale genomic features were closely related to the cancer by gene set enrichment analysis, indicating that our proposed strategy can well reflect the biological relevance of the genes to breast cancer. In conclusion, modelling with multiscale genomic features closely related to the cancer not only can guarantee the prediction performance of the models but also can effectively provide candidate genes for interpreting the mechanisms of cancer.

Cordyceps sinensis(C.sinensis) is a precious traditional Chinese herbal medicine with a wide variety of beneficial pharmacological effects. In recent years, due to the limitation of natural conditions and the over-harvesting, natural C.sinensis resources have been increasingly scarce and far from meeting the market demand. The phenomenon of counterfeit and substandard in the market is also becoming more and more serious. Thus the establishment of an identification method with good accuracy, high sensitivity and specificity is imminent. For this purpose, 3559 samples of C.sinensis were collected from different sources (part, region and phase) and we proposed the use of FTIR spectroscopy combined with chemometrics methods to construct robust and effective multi-class discriminant models. Cross-sectional comparison was performed between conventional machine learning methods (SVM, PLS-DA, LDA) and convolution neural network (CNN) under different classification tasks. Matthews correlation coefficient (MCC) was chosen as the optimized metrics for imbalanced classification problem. The best model for prediction of C.sinensis’s part was SVM combined with SNV as the pretreatment method (MCC was 0.8918 on cross-validation dataset and 0.9017 on independent testing dataset) and LDA with no pretreatment for prediction of C.sinensis’s region (MCC was 0.7721 on cross-validation dataset and 0.7332 on independent testing dataset). Classification of C.sinensis’s phase was less predictive (MCC was around 0.25) but was significantly superior to random prediction. The model performance of CNN is comparable to that of traditional machine learning methods and increasing training sample size might have the potential to further improve the model performance of CNN. Our results demonstrated that FTIR spectra combined with an optimized multivariable discriminant model could be used as an effective and sensitive method for rapid quality control of C.sinensis.

Post-translational modifications of histone play a key role in controlling gene expression, as they are involved in gene transcription regulation through controlling DNA accessibility or recruiting specific transcription factors. Developing computational methods to predict gene expression levels from histone modification data may facilitate understanding their function in gene regulation and contribute to develop ‘epigenetic drugs’ for disease treatment. Several studies have reported that transcription termination plays a crucial role in gene regulation as it is able to premature termination and determine the cellular fate of transcripts. However, previous work mainly focused on the histone characteristics flanking transcription start site (TSS) of genes, while ignoring the histone modification features around transcription termination site (TTS). Thus, we introduced a hybrid convolutional and bi-directional long short-term memory network with attention mechanism to predict gene expression from histone modification signals in both regions. Our model achieved higher results than the state-of-the-art model in both classification and regression tasks to infer gene expression level. The predictive results of model demonstrated that histone signals of TTS can provide additional information to improve the model performance. In addition, attention weights and transcription factor binding data indicated that histone modifications in TSS might regulate transcription by recruiting TFs, but the regulation mechanisms of it in TTS need to be further explored.

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both in vivo and in vitro assays. For in vivo TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but in vitro assays, as alternatives, usually demonstrate poor correlation with real in vivo assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from in vivo assays based on gene expression levels and to further utilize in vitro data to better simulate in vivo data is needed. We proposed a strategy based on post‐modified non‐negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate in vivo data by correcting in vitro data. The similarities between real in vivo data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real in vivo data with in vitro data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using in vitro data to provide potential substitutions for in vivo TGx and to reduce the launch of live-animal tests.

We report a facile yet effective strategy of utilizing a combination of Fourier transform-infrared spectroscopy (FTIR) and multi-label algorithms, through which multi-components in polymer bonded explosives (PBXs) could be rapidly and simultaneously identified with high accuracy. The explosive components include 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclo-octane (HMX), hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), 2,4,6-triamino-1,3,5-trinitrobenzene (TATB) and 2,4,6-trinitrotoluene (TNT) involved in single-component, binary-component and ternary-component PBXs. The train set contains 354 FTIR spectra of the explosives while the independent test set contains 84. Two multi-label strategies (viz., data decomposition and algorithm adaptation) were adopted to construct the classification model with an objective of testing their efficiency in the multi-classification application. Principal component analysis (PCA) was applied to reduce the variables. Both the two algorithms exhibit excellent performance with 100% accuracy for the training and the independent test sets. However, for real PBX samples, the performance of the algorithm adaptation strategy is sharply decreased to 40% accuracy. But, it is noteworthy that the data decomposition strategy still achieves the accuracy of 100% for the real samples, exhibiting stronger robustness for the background interference and high promise in practice. The strategy proposed by the work would provide valuable information for advancing analytical methods in the explosive detection system and the other complicated samples.

Biomolecules in organisms play various functions, which are largely achieved by linking with multiple interacting partners. The comprehensive study about these links could facilitate our systematic understand on biology progress. Recently, RNA-RNA interaction analysis provides a sight in disease study by revealing the RNA regulatory mechanisms. However, many studies have focused on specific types of RNAs, and the few have been reported from the perspective of the genome wide RNA-RNA interactions. In this study, we constructed an RNA-RNA interaction network by extensively collecting reported RNA-RNA interactions and then compared it with the protein-protein interaction and the gene co-expression networks. Significant diversities were observed between these networks, involving the composition, the topological features and the pattern of the expression correlation between RNAs. When applied the RNA-RNA interaction network into the breast cancer, 15 miRNAs were identified as hub nodes and verified related to breast cancer. The topological properties of mRNAs in the network associated with their expression pattern and breast cancer correlation. The differentially expressed lncRNAs tend to interact with the differentially expressed mRNAs, indicating their importance in gene regulatory. These results suggest that RNA-RNA interaction network could be anticipated as a complement to the well-studied protein-protein interaction and co-expression network methods.

Recently, multiple researches of solvent accessibility on protein are well established. However, research on solvent accessibility of RNA faces a few challenges such as instability and diversity of RNA tertiary structure. Nowadays, no study has examined the predicting performance from different datasets built from multi-models and multi-attributes, but it is an important part of measuring the overall performance of modeling. Therefore, we performed a comprehensive comparison for predicting RNA solvent accessibility based on two datasets. 15,923 (12,229 ​+ ​3694) samples and 512(336 ​+ ​176) attributes were generated for attribute selection, finally 336 models were built for predicting. 12 modeling methods and 2 attribute selection methods were used for modeling and evaluating. This work provided a strategy for getting stable expectation when predicting RNA solvent accessibility. These results would be useful in further experimental or computational design for RNA solvent accessibility predicting and we hope this work could help the related researches which need a workflow for stable prediction.

In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in previous studies. To identify a relatively stable scheme for modeling limited samples in the gene datasets and reveal the information that they contain, the present study first evaluated the performance of a series of modeling approaches for predicting clinical endpoints of cancer and later integrated the results using various voting protocols. As a result, we proposed a relatively stable scheme that used a set of methods with an ensemble algorithm. Our findings indicated that the ensemble methodologies are more reliable for predicting cancer prognoses than single machine learning algorithms as well as for gene function evaluating. The ensemble methodologies provide a more complete coverage of relevant genes, which can facilitate the exploration of cancer mechanisms and the identification of potential drug targets.

Aims: Although RNA-sequencing has been widely used to identify the differentially expressed genes (DEGs) as biomarkers to guide the therapeutic treatment, it is necessary to investigate the concordance of DEGs identified by microarray and RNA-sequencing for the clinical prognosis. Material & methods: By using The Cancer Genome Atlas data sets, we thoroughly investigated the concordance of DEGs identified from microarray and RNA-sequencing data and their molecular functions. Results: The DEGs identified by both technologies averaged ˜98.6% overlap. The cancer-related gene sets were significantly enriched with the DEGs and consistent between two technologies. Conclusions: The highly consistency of DEGs in their regulation directionality and molecular functions indicated the good reproducibility between microarray and RNA-sequencing in identifying potential oncogenes for clinical prognosis.

In clinical research, DNA microarrays are widely applied in the identification of the oncogenes, which are differentially expressed between two clinical states and considered as predictors for the cancer prognosis. Due to the heterogeneity of clinical samples, the differentially expressed genes (DEGs) discovered by current statistical methods or machine learning algorithms involve a number of genes unrelated to the phenotypic differences between the compared samples and, consequently, will impact on the reliability of the predictive models in the cancer prognosis. In our study, we proposed Bayesian nonparametric variable selection algorithm, a stochastic random and hierarchical search method, to separate out the cancer-related genes from the DEG lists. The importance of the genes in the DEG lists can be inferred from the posterior distribution of the predicted clinical endpoints, which can be simulated by the Markov Chain Monte Carlo (MCMC) algorithm. The cancer-related genes were identified according to their importance and used to construct models for the prediction of three clinical endpoints, namely the estrogen receptor status (ER status) of the breast cancer patient, the preoperative treatment response of breast cancer and the overall survival milestone outcome of acute myeloma leukemia (OS of AML). The prediction accuracies of preoperative treatment response, ER status and OS of AML were 86%, 89% and 58%, and the Mathew’s correlation coefficients were 0.42, 0.77 and 0.33, which were higher than those reported in previous studies. Furthermore, most of the genes identified by our method were reported as oncogenes in previous literatures. Our results demonstrated that the Bayesian nonparametric variable selection algorithm proposed in current study can efficiently identify the oncogenes for cancer prognosis and enhance the performance of the predictive models.

The prediction of protein domain region is an advantageous process on the study of protein structure and function. In this study, we proposed a new method, which is composed of fuzzy mean operator and region division, to predict the particular positions of domains in a target protein based on its sequence. The whole sequence is aligned and scored by using fuzzy mean operator, and the final determination of domain region position is realized by region division. A published benchmark is used for the comparison with previous researches. In addition, we generate two extra datasets to examine the stability of this method. Finally, the prediction accuracy of independent test dataset achieved by our method was up to 84.13%. We wish that this method could be useful for related researches. Proteins 2015; 83:1462–1469. © 2015 Wiley Periodicals, Inc.

The correct diagnosis and the prompt treatment of oral leukoplakia (OLK) can efficiently prevent OLK from undergoing malignant transformation to oral squamous cell carcinoma (OSCC). However, the diagnostic model for distinguishing normal mucosa from low-grade dysplasia, as well as high-grade dysplasia from OSCC was not well established in a previous study. In this study, the characteristic wavenumbers in the Raman spectra were first identified by the variable selection methods. Then, the intensities at these wavenumbers were used to classify the biopsies. As a result, the accuracies achieved by the intensities at the characteristic wavenumbers were 70.5% and 94.0% for the classification of normal vs. low-grade dysplasia and high-grade dysplasia vs. OSCC, respectively, which were greater than those (accuracy of 65.4% and 88.0%, respectively) using all the intensities in the Raman spectra. Our results suggested that constructing a diagnostic model with the intensities at the characteristic wavenumbers can improve the identification of the different lesions of oral mucosa. Moreover, most of the Raman intensities for predicting normal vs. low-grade dysplasia indicated that the transformation from normal mucosa to low-grade dysplasia was associated with the changes in the contents of lipids, while most of the intensities for predicting high-grade dysplasia vs. OSCC indicated that the transformation from high-grade dysplasia to OSCC was associated with changes in the contents of proteins and nucleic acids. Our findings can be helpful for diagnosing the various grades of OLK with dysplasia and understanding the molecular mechanisms of potential malignant transformation of oral leukoplakia.

Motivated by timesaving when dealing with the large-scale calculation for data modeling in parallel with multiple CPU cores or machines and result comparison, PML was designed in this study. PML has the ability to do dimension reduction and grid search in parallel, support both classification and regression, and could generate HTML pages as output for results comparison. Written in PERL, PML is compatible with Windows and Linux. This open source software is free available together with the code, manual, examples and license from http://cic.scu.edu.cn/pml or https://github.com/limlcic/PML .

Genome-wide association studies (GWAS) have identified more than sixty single nucleotide polymorphisms (SNPs) associated with increased risk for type 2 diabetes (T2D). However, the identification of causal risk SNPs for T2D pathogenesis was complicated by the factor that each risk SNP is a surrogate for the hundreds of SNPs, most of which reside in non-coding regions. Here we provide a comprehensive annotation of 65 known T2D related SNPs and inspect putative functional SNPs probably causing protein dysfunction, response element disruptions of known transcription factors related to T2D genes and regulatory response element disruption of four histone marks in pancreas and pancreas islet. In new identified risk SNPs, some of them were reported as T2D related SNPs in recent studies. Further, we found that accumulation of modest effects of single sites markedly enhanced the risk prediction based on 1989 T2D samples and 3000 healthy controls. The AROC value increased from 0.58 to 0.62 by only using genotype score when putative risk SNPs were added. Besides, the net reclassification improvement is 10.03% on the addition of new risk SNPs. Taken together, functional annotation could provide a list of prioritized potential risk SNPs for the further estimation on the T2D susceptibility of individuals.

The interactions among the genes within a disease are helpful for better understanding the hierarchical structure of the complex biological system of it. Most of the current methodologies need the information of known interactions between genes or proteins to create the network connections. However, these methods meet the limitations in clinical cancer researches because different cancers not only share the common interactions among the genes but also own their specific interactions distinguished from each other. Moreover, it is still difficult to decide the boundaries of the sub-networks. Therefore, we proposed a strategy to construct a gene network by using the sparse inverse covariance matrix of gene expression data and divide it into a series of functional modules by an adaptive partition algorithm. The strategy was validated by using the microarray data of three cancers and the RNA-sequencing data of glioblastoma. The different modules in the network exhibited specific functions in cancers progression. Moreover, based on the gene expression profiles in the modules, the risk of death was well predicted in the clustering analysis and the binary classification, indicating that our strategy can be benefit for investigating the cancer mechanisms and promoting the clinical applications of network-based methodologies in cancer researches.

Signal peptides play a crucial role in various biological processes, such as localization of cell surface receptors, translocation of secreted proteins and cell-cell communication. However, the amino acid mutation in signal peptides, also called non-synonymous single nucleotide polymorphisms (nsSNPs or SAPs) may lead to the loss of their functions. In the present study, a computational method was proposed for predicting deleterious nsSNPs in signal peptides based on random forest (RF) by incorporating position specific scoring matrix (PSSM) profile, SignalP score and physicochemical properties. These features were optimized by the maximum relevance minimum redundancy (mRMR) method. Then, a cost matrix was used to minimize the effect of the imbalanced data classification problem that usually occurred in nsSNPs prediction. The method achieved an overall accuracy of 84.5% and the area under the ROC curve (AUC) of 0.822 by Jackknife test, when the optimal subset included 10 features. Furthermore, on the same dataset, we compared our predictor with other existing methods, including R-score-based method and D-score-based methods, and the result of our method was superior to those of the two methods. The satisfactory performance suggests that our method is effective in predicting the deleterious nsSNPs in signal peptides.

The prediction of protein domain region is an advantageous process on the study of protein structure and function. In this study, we proposed a new method, which is composed of fuzzy mean operator and region division, to predict the particular positions of domains in a target protein based on its sequence. The whole sequence is aligned and scored by using fuzzy mean operator, and the final determination of domain region position is realized by region division. A published benchmark is used for the comparison with previous researches. In addition, we generate two extra datasets to examine the stability of this method. Finally, the prediction accuracy of independent test dataset achieved by our method was up to 84.13%. We wish that this method could be useful for related researches.

The B-factor, also called the Debye-Waller factor or the temperature factor, is a descriptor of the flexibility of protein and is commonly used in PDB (Protein Data Bank) format files. A B-factor could be measured from a protein crystal by x-ray scattering, but could not be got from the protein sequence directly. Thus, predicting the B-factor only based on the protein sequence could provide some references for the related researchers. In this study, we attempt to predict the B-factor based on the protein sequence. The information in AAindex and the predicted protein secondary structure, relative accessibility, disorder and energy changes are used to describe the amino acid residues. Four machine learning methods are used for modeling and prediction. The 5-fold cross validation is used to evaluate the modeling performance. As a result, this work provided some new methods for predicting and analyzing the B-factor based on the protein sequence, and we hope that this work could be helpful for the related researches.

Single-nucleotide polymorphisms (SNPs) are the most frequent form of genetic variations. Non-synonymous SNPs (nsSNPs) occurring in coding region result in single amino acid substitutions that associate with human hereditary diseases. Plenty of approaches were designed for distinguishing deleterious from neutral nsSNPs based on sequence level information. Novel in this work, combinations of protein–protein interaction (PPI) network topological features were introduced in predicting disease-related nsSNPs. Based on a dataset that was compiled from Swiss-Prot, a random forest model was constructed with an average accuracy value of 80.43 % and an MCC value of 0.60 in a rigorous tenfold crossvalidation test. For an independent dataset, our model achieved an accuracy of 88.05 % and an MCC of 0.67. Compared with previous studies, our approach presented superior prediction ability. Results showed that the incorporated PPI network topological features outperform conventional features. Our further analysis indicated that disease-related proteins are topologically different from other proteins. This study suggested that nsSNPs may share some topological information of proteins and the change of topological attributes could provide clues in illustrating functional shift due to nsSNPs.

The main objective of this study is to explore the contribution of complex network together with its different definitions of vertexes and edges to describe the structure of proteins. Protein folds into a specific conformation for its function depending on interactions between residues. Consequently, in many studies, a protein structure was treated as a complex system comprised of individual components residues, and edges were interactions between residues. What is the proper time for representing a protein structure as a network? To confirm the effect of different definitions of vertexes and edges in constructing the amino acid interaction networks, protein domains and the structural unit of proteins were described using this method. The identification performance of 2847 proteins with domain/domains proved that the structure of proteins was described well when R(C)(α) was around 5.0-7.5 A, and the optimal cutoff value for constructing the protein structure networks was 5.0 A (C(α) -C(α) distances) while the ideal community division method was community structure detection based on edge betweenness in this study.

Multi-family enzymes are of great importance in life, disease and other domains. However, in terms of the classification of enzymes, the information of multi-family enzymes is always removed from the dataset to account for the limitation of traditional single-label prediction methods. In order to predict multiple classes of multi-family enzymes, we adopted two multi-label learning algorithms, namely RAkEL-RF and MLKNN, and two types of protein descriptors, namely CTD and PseAAC, to generate four predictors, RAkEL-RF-CTD, RAkEL-RF-PseAAC, MLKNN-CTD and MLKNN-PseAAC. When the four predictors were tested on a training set with 10-fold cross validation, the overall success rates reached 97.99%, 96.07%, 96.01% and 95.31%, respectively. For the independent test set, the corresponding rates reached 97.57%, 95.03%, 95.9% and 93.9%, respectively. In conclusion, it proved the outstanding prediction capability and robustness of our predictors from the extremely small difference between two sets for each predictor and the relatively higher accuracy. In addition, three of seven pairs of homologous enzymes with different functions and eighteen of twenty-three distantly related enzymes with a similar family were correctly classified by the RAkEL-RF-CTD predictor. These results indicated the extensive applicability of our predictors.