羟氨苄青霉素引起大疱性类天疱疮样疹

Background
Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction.
Methods
Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins).
Findings
24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The 78 highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity
79 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm.
Conclusions
Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

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Can machine-learning improve cardiovascular risk prediction using routine clinical data?

N6-Methyladenosine (m6A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N6-methyladenosine sites are limited by the small amount of data available. Taking advantage of the thousands of m6A sites detected by high-throughput sequencing, it is now possible to discover the characteristics of m6A sequences using deep learning techniques. To the best of our knowledge, our work is the first attempt to use word embedding and deep neural networks for m6A prediction from mRNA sequences. Using four deep neural networks, we developed a model inferred from a larger sequence shifting window that can predict m6A accurately and robustly. Four prediction schemes were built with various RNA sequence representations and optimized convolutional neural networks. The soft voting results from the four deep networks were shown to outperform all of the state-of-the-art methods. We evaluated these predictors mentioned above on a rigorous independent test data set and proved that our proposed method outperforms the state-of-the-art predictors. The training, independent, and cross-species testing data sets are much larger than in previous studies, which could help to avoid the problem of overfitting. Furthermore, an online prediction web server implementing the four proposed predictors has been built and is available at http://server.malab.cn/Gene2vec/.

/pdf/gene2vec-gene-subsequence-embedding-for-prediction-of-1ey1tpv7xj.pdf

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA.

// Wei Chen 1, 4 , Hui Ding 2 , Pengmian Feng 3 , Hao Lin 2, 4 , Kuo-Chen Chou 4, 5 1 Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, China 2 Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China 3 School of Public Health, North China University of Science and Technology, Tangshan, China 4 Gordon Life Science Institute, Belmont, Massachusetts, United States of America 5 Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia Correspondence to: Wei Chen, e-mail: wchen@gordonlifescience.org , chenweiimu@gmail.com Hao Lin, e-mail: hlin@uestc.edu.cn Kuo-Chen Chou, e-mail: kcchou@gordonlifescience.org Keywords: anticancer peptides, PseAAC, g-gap dipeptide mode, incremental feature selection, iACP webserver Received: January 06, 2016      Accepted: February 11, 2016      Published: March 01, 2016 ABSTRACT Cancer remains a major killer worldwide. Traditional methods of cancer treatment are expensive and have some deleterious side effects on normal cells. Fortunately, the discovery of anticancer peptides (ACPs) has paved a new way for cancer treatment. With the explosive growth of peptide sequences generated in the post genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying ACPs, so as to speed up their application in treating cancer. Here we report a sequence-based predictor called iACP developed by the approach of optimizing the g-gap dipeptide components. It was demonstrated by rigorous cross-validations that the new predictor remarkably outperformed the existing predictors for the same purpose in both overall accuracy and stability. For the convenience of most experimental scientists, a publicly accessible web-server for iACP has been established at http://lin.uestc.edu.cn/server/iACP , by which users can easily obtain their desired results.

https://www.kau.edu.sa/Files/117/Researches/68715_41960.pdf

iACP: a sequence-based tool for identifying anticancer peptides

Motivation Enhancers are of short regulatory DNA elements. They can be bound with proteins (activators) to activate transcription of a gene, and hence play a critical role in promoting gene transcription in eukaryotes. With the avalanche of DNA sequences generated in the post-genomic age, it is a challenging task to develop computational methods for timely identifying enhancers from extremely complicated DNA sequences. Although some efforts have been made in this regard, they were limited at only identifying whether a query DNA element being of an enhancer or not. According to the distinct levels of biological activities and regulatory effects on target genes, however, enhancers should be further classified into strong and weak ones in strength. Results In view of this, a two-layer predictor called ' IENHANCER-2L: ' was proposed by formulating DNA elements with the 'pseudo k-tuple nucleotide composition', into which the six DNA local parameters were incorporated. To the best of our knowledge, it is the first computational predictor ever established for identifying not only enhancers, but also their strength. Rigorous cross-validation tests have indicated that IENHANCER-2L: holds very high potential to become a useful tool for genome analysis. Availability and implementation For the convenience of most experimental scientists, a web server for the two-layer predictor was established at http://bioinformatics.hitsz.edu.cn/iEnhancer-2L/, by which users can easily get their desired results without the need to go through the mathematical details. Contact bliu@gordonlifescience.org, bliu@insun.hit.edu.cn, xlan@stanford.edu, kcchou@gordonlifescience.org Supplementary information Supplementary data are available at Bioinformatics online.

/pdf/ienhancer-2l-a-two-layer-predictor-for-identifying-enhancers-3e00jb90ec.pdf

iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition

Motivation: Coexisting in a DNA system, meiosis and recombination are two indispensible aspects
for cell reproduction and growth. With the avalanche of genome sequences emerging in the postgenomic
age, it is an urgent challenge to acquire the information of DNA recombination spots because
it can timely provide very useful insights into the mechanism of meiotic recombination and
the process of genome evolution.
Results: To address such a challenge, we have developed a predictor, called iRSpot-EL, by fusing
different modes of pseudo K-tuple nucleotide composition and mode of dinucleotide-based autocross
covariance into an ensemble classifier of clustering approach. Five-fold cross tests on a
widely used benchmark dataset have indicated that the new predictor remarkably outperforms its
existing counterparts. Particularly, far beyond their reach, the new predictor can be easily used to
conduct the genome-wide analysis and the results obtained are quite consistent with the experimental
map.
Availability and Implementation: For the convenience of most experimental scientists, a userfriendly
web-server for iRSpot-EL has been established at http://bioinformatics.hitsz.edu.cn/iRSpot-
EL/, by which users can easily obtain their desired results without the need to go through the complicated
mathematical equations involved.

/pdf/irspot-el-identify-recombination-spots-with-an-ensemble-3er9gkqo7c.pdf

iRSpot-EL: identify recombination spots with an ensemble learning approach.

Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy.

DNA-binding proteins play an important role in most cellular processes. Therefore, it is necessary to develop an efficient predictor for identifying DNA-binding proteins only based on the sequence information of proteins. The bottleneck for constructing a useful predictor is to find suitable features capturing the characteristics of DNA binding proteins. We applied PseAAC to DNA binding protein identification, and PseAAC was further improved by incorporating the evolutionary information by using profile-based protein representation. Finally, Combined with Support Vector Machines (SVMs), a predictor called iDNAPro-PseAAC was proposed. Experimental results on an updated benchmark dataset showed that iDNAPro-PseAAC outperformed some state-of-the-art approaches, and it can achieve stable performance on an independent dataset. By using an ensemble learning approach to incorporate more negative samples (non-DNA binding proteins) in the training process, the performance of iDNAPro-PseAAC was further improved. The web server of iDNAPro-PseAAC is available at http://bioinformatics.hitsz.edu.cn/iDNAPro-PseAAC/.

/pdf/dna-binding-protein-identification-by-combining-pseudo-amino-3f2913is1g.pdf

DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation.

DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. With the rapid development of next generation of sequencing technique, the number of protein sequences is unprecedentedly increasing. Thus it is necessary to develop computational methods to identify the DNA-binding proteins only based on the protein sequence information. In this study, a novel method called iDNA-KACC is presented, which combines the support vector machine (SVM) and the auto-cross covariance transformation. The protein sequences are first converted into profile-based protein representation, and then converted into a series of fixed-length vectors by the auto-cross covariance transformation with Kmer composition. The sequence order effect can be effectively captured by this scheme. These vectors are then fed into support vector machine (SVM) to discriminate the DNA-binding proteins from the non-DNA-binding ones. iDNA-KACC achieves an overall accuracy of 75.16% and Matthew correlation coefficient of 0.5 by a rigorous jackknife test. Its performance is further improved by employing an ensemble learning approach, and the improved predictor is called iDNA-KACC-EL. Experimental results on an independent dataset shows that iDNA-KACC-EL outperforms all the other state-of-the-art predictors, indicating that it would be a useful computational tool for DNA binding protein identification.

Identification of DNA-Binding Proteins by Combining Auto-Cross Covariance Transformation and Ensemble Learning

DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. With the rapid development of next generation of sequencing technique, the number of protein sequences are unprecedentedly increasing. Thus it is necessary to develop computational methods to identify the DNA-binding protein from the protein sequence information. In this study, a novel method is presented which combines the support vector machine and the auto-cross covariance transformation. The protein sequence represented in the form of amino acids or the physical-chemical properties of amino acids are converted into a series of fixed-length vectors by Kmer composition and the auto-cross covariance transformation. The sequence order effect can be effectively capture by this scheme. These vectors are then inputted to support vector machine to discriminate the DNA-binding proteins from the non DNA-binding ones. The proposed method achieves the overall accuracy of 75.23% and Matthew correlation coefficient of 0.5 by a rigorous jackknife test. The independent test shows that the proposed method outperforms most of the existing methods. These results demonstrate that the proposed method provides the state-of-the-art performance for the prediction of DNA-binding proteins.

Shanyi Wang

Papers

iRSpot-EL: identify recombination spots with an ensemble learning approach.

Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy.

DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation.

Identification of DNA-Binding Proteins by Combining Auto-Cross Covariance Transformation and Ensemble Learning

Identification of DNA-binding proteins by auto-cross covariance transformation