다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Motivation DNA N6-methyladenine (6mA) is associated with a wide range of biological processes. Since the distribution of 6mA site in the genome is non-random, accurate identification of 6mA sites is crucial for understanding its biological functions. Although experimental methods have been proposed for this regard, they are still cost-ineffective for detecting 6mA site in genome-wide scope. Therefore, it is desirable to develop computational methods to facilitate the identification of 6mA site. Results In this study, a computational method called i6mA-Pred was developed to identify 6mA sites in the rice genome, in which the optimal nucleotide chemical properties obtained by the using feature selection technique were used to encode the DNA sequences. It was observed that the i6mA-Pred yielded an accuracy of 83.13% in the jackknife test. Meanwhile, the performance of i6mA-Pred was also superior to other methods. Availability and implementation A user-friendly web-server, i6mA-Pred is freely accessible at http://lin-group.cn/server/i6mA-Pred.

i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome

Motivation DNA replication is a key step to maintain the continuity of genetic information between parental generation and offspring. The initiation site of DNA replication, also called origin of replication (ORI), plays an extremely important role in the basic biochemical process. Thus, rapidly and effectively identifying the location of ORI in genome will provide key clues for genome analysis. Although biochemical experiments could provide detailed information for ORI, it requires high experimental cost and long experimental period. As good complements to experimental techniques, computational methods could overcome these disadvantages. Results Thus, in this study, we developed a predictor called iORI-PseKNC2.0 to identify ORIs in the Saccharomyces cerevisiae genome based on sequence information. The PseKNC including 90 physicochemical properties was proposed to formulate ORI and non-ORI samples. In order to improve the accuracy, a two-step feature selection was proposed to exclude redundant and noise information. As a result, the overall success rate of 88.53% was achieved in the 5-fold cross-validation test by using support vector machine. Availability and implementation Based on the proposed model, a user-friendly webserver was established and can be freely accessed at http://lin-group.cn/server/iORI-PseKNC2.0. The webserver will provide more convenience to most of wet-experimental scholars.

Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique

DNA N4-methylcytosine (4mC) is an important genetic modification and plays crucial roles in differentiation between self and non-self DNA and in controlling DNA replication, cell cycle, and gene-expression levels. Accurate 4mC site identification is fundamental to improve the understanding of 4mC biological functions and mechanisms. Hence, it is necessary to develop in silico approaches for efficient and high-throughput 4mC site identification. Although some bioinformatic tools have been developed in this regard, their prediction accuracy and generalizability require improvement to optimize their usability in practical applications. For this purpose, we here proposed Meta-4mCpred, a meta-predictor for 4mC site prediction. In Meta-4mCpred, we employed a feature representation learning scheme and generated 56 probabilistic features based on four different machine-learning algorithms and seven feature encodings covering diverse sequence information, including compositional, physicochemical, and position-specific information. Subsequently, the probabilistic features were used as an input to support vector machine and developed a final meta-predictor. To the best of our knowledge, this is the first meta-predictor for 4mC site prediction. Cross-validation results show that Meta-4mCpred achieved an overall average accuracy of 84.2% from six different species, which is ∼2%–4% higher than those attainable using the state-of-the-art predictors. Furthermore, Meta-4mCpred achieved an overall average accuracy of 86% on independent datasets evaluation, which is over 4% higher than those yielded by the state-of-the-art predictors. The user-friendly webserver employed to implement the proposed Meta-4mCpred is freely accessible at http://thegleelab.org/Meta-4mCpred .

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Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation

The soluble carrier hormone binding protein (HBP) plays an important role in the growth of human and other animals. HBP can also selectively and non-covalently interact with hormone. Therefore, accurate identification of HBP is an important prerequisite for understanding its biological functions and molecular mechanisms. Since experimental methods are still labor intensive and cost ineffective to identify HBP, it's necessary to develop computational methods to accurately and efficiently identify HBP. In this paper, a machine learning-based method was proposed to identify HBP, in which the samples were encoded by using the optimal tripeptide composition obtained based on the binomial distribution method. In the 5-fold cross-validation test, the proposed method yielded an overall accuracy of 97.15%. For the convenience of scientific community, a user-friendly webserver called HBPred2.0 was built, which could be freely accessed at http://lin-group.cn/server/HBPred2.0/ .

Identification of hormone binding proteins based on machine learning methods

Protein structural class could provide important clues for understanding protein fold, evolution and function. However, it is still a challenging problem to accurately predict protein structural classes for low-similarity sequences. This paper was devoted to develop a powerful method to predict protein structural classes for low-similarity sequences. On the basis of a very objective and strict benchmark dataset, we firstly extracted optimal tripeptide compositions (OTC) which was picked out by using feature selection technique to formulate protein samples. And an overall accuracy of 91.1% was achieved in jackknife cross-validation. Subsequently, we investigated the accuracies of three popular features: position-specific scoring matrix (PSSM), predicted secondary structure information (PSSI) and the average chemical shift (ACS) for comparison. Finally, to further improve the prediction performance, we examined all combinations of the four kinds of features and achieved the maximum accuracy of 96.7% in jackknife cross-validation by combining OTC with ACS, demonstrating that the model is efficient and powerful. Our study will provide an important guide to extract valuable information from protein sequences.

Lin Hao

Papers

Predicting protein structural classes for low-similarity sequences by evaluating different features