A computational platform to identify origins of replication sites in eukaryotes

Dao, Fanny; Lv, Hao; Zulfiqar, Hasan; Yang, Hui; Su, Wei; Gao, Hui; Ding, Hui; Lin, Hao

doi:10.1093/bib/bbaa017

Cited by 77 publications

(45 citation statements)

References 44 publications

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“…In order to evaluate the performance of related predictive methods and the reliability of the algorithm, the sensitivity ( S n ), specificity ( S p ), accuracy (ACC), Matthew's correlation coefficient (MCC), and overall accuracy (OA) [ 54 – 59 ] were used and defined by

where N is the total number of protein sequences in the dataset, TP represents the numbers of the correctly recognized positives, FN is the numbers of the positives recognized as negatives, FP means the numbers of the negatives recognized as positives, while TN is the numbers of correctly recognized negatives.…”

Section: Resultsmentioning

confidence: 99%

Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features

Gao

2020

BioMed Research International

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There are a lot of bacteria in the environment, and Gram-positive bacteria are the most common ones. Some Gram-positive bacteria are very harmful to the human body, so it is significant to predict Gram-positive bacterial protein subcellular location. And identification of Gram-positive bacterial protein subcellular location is important for developing effective drugs. In this paper, a new Gram-positive bacterial protein subcellular location dataset was established. The amino acid composition, the gene ontology annotation information, the hydropathy dipeptide composition information, the amino acid dipeptide composition information, and the autocovariance average chemical shift information were selected as characteristic parameters, then these parameters were combined. The locations of Gram-positive bacterial proteins were predicted by the Support Vector Machine (SVM) algorithm, and the overall accuracy (OA) reached 86.1% under the Jackknife test. The overall accuracy (OA) in our predictive model was higher than those in existing methods. This improved method may be helpful for protein function prediction.

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Section: Resultsmentioning

confidence: 99%

Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features

Gao

2020

BioMed Research International

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“…Whereas, it couldn't be used for exposing the causal effect. With the increase of GWAS data and abundance of molecular characterize (Cheng, 2019;Cheng et al, 2019a;Dao et al, 2020;Zhang et al, 2020), more and more researchers choose MR analysis for this purpose. MR analysis is an IV-based framework, which requires summarized GWAS data.…”

Section: Discussionmentioning

confidence: 99%

A Mendelian Randomization Analysis to Expose the Causal Effect of IL-18 on Osteoporosis Based on Genome-Wide Association Study Data

Kou

Zhou

et al. 2020

Front. Bioeng. Biotechnol.

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Accumulating evidence showed that Interleukin (IL) level is associated with Osteoporosis. Whereas, most of these associations are based on observational studies. Thus, their causality was still unclear. Mendelian randomization (MR) is a widely used statistical framework that uses genetic instrumental variables (IVs) to explore the causality of intermediate phenotype with disease. To classify their causality, we conducted a MR analysis to investigate the effect of IL-18 level on the risk of Osteoporosis. First, based on summarized genome-wide association study (GWAS) data, 8 independent IL-18 SNPs reaching genome-wide significance were deemed as IVs. Next, Simple median method was used to calculate the pooled odds ratio (OR) of these 8 SNPs for the assessment of IL-8 on the risk of Osteoporosis. Then, MR-Egger regression was utilized to detect potential bias due to the horizontal pleiotropy of these IVs. As a result of simple median method, we get the SE (−0.001; 95% CI −0.002 to 0; P = 0.042), which means low IL-18 level could increases the risk of the development of Osteoporosis. The low intercept (0; 95% CI −0.001 to 0; P = 0.59) shows there is no bias due to the horizontal pleiotropy of the IVs.

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“…Each enhancer in the dataset is a 200 bp nucleotide sequence, which consists of four nucleotides, namely, adenine (A), guanine (G), cytosine (C) and thymine (T). Each nucleotide is represented by a set of vectors ( Table 1 ) [ 37 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network

et al. 2020

Computational and Mathematical Methods in Medicine

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Enhancers are noncoding fragments in DNA sequences, which play an important role in gene transcription and translation. However, due to their high free scattering and positional variability, the identification and classification of enhancers have a higher level of complexity than those of coding genes. In order to solve this problem, many computer studies have been carried out in this field, but there are still some deficiencies in these prediction models. In this paper, we use various feature extraction strategies, dimension reduction technology, and a comprehensive application of machine model and recurrent neural network model to achieve an accurate prediction of enhancer identification and classification with the accuracy of was 76.7% and 84.9%, respectively. The model proposed in this paper is superior to the previous methods in performance index or feature dimension, which provides inspiration for the prediction of enhancers by computer technology in the future.

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A computational platform to identify origins of replication sites in eukaryotes

Cited by 77 publications

References 44 publications

Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features

Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features

A Mendelian Randomization Analysis to Expose the Causal Effect of IL-18 on Osteoporosis Based on Genome-Wide Association Study Data

Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network

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