An overview of SNP interactions in genome-wide association studies

Li, Pei; Guo, Maozu; Wang, Qianqian; Liu, Xiaoyan; Zou, Quan

doi:10.1093/bfgp/elu036

Cited by 93 publications

(61 citation statements)

References 85 publications

(104 reference statements)

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“…It is of interest to use these neural-like computing models for optimizing culture conditions for Phellinus production. Our work would also guide for the “Precision Medicine” with personal SNP data [35] and other tasks in bioinformatics [21, 22]. …”

Section: Discussionmentioning

confidence: 99%

Optimization to the Culture Conditions forPhellinusProduction with Regression Analysis and Gene-Set Based Genetic Algorithm

Xin

Wang

et al. 2016

BioMed Research International

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Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization.

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Section: Discussionmentioning

confidence: 99%

Optimization to the Culture Conditions forPhellinusProduction with Regression Analysis and Gene-Set Based Genetic Algorithm

Xin

Wang

et al. 2016

BioMed Research International

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“…The 1000 Genomes project [2] and recent sequencing of whole human genomes [3–6] have provided a large number of single-nucleotide polymorphisms (SNPs), insertions, deletions and structural variants in humans. Among these variations, SAVs are recognized as the most common type in the human genome [7, 8], and some are often closely related to particular diseases [9–11]. According to the previous studies, SAVs may be responsible for the initiation or progression of cancer through aberrant proteins [12].…”

Section: Introductionmentioning

confidence: 99%

Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties

2017

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Single amino acid variations (SAVs) potentially alter biological functions, including causing diseases or natural differences between individuals. Identifying the relationship between a SAV and certain disease provides the starting point for understanding the underlying mechanisms of specific associations, and can help further prevention and diagnosis of inherited disease.We propose PredSAV, a computational method that can effectively predict how likely SAVs are to be associated with disease by incorporating gradient tree boosting (GTB) algorithm and optimally selected neighborhood features. A two-step feature selection approach is used to explore the most relevant and informative neighborhood properties that contribute to the prediction of disease association of SAVs across a wide range of sequence and structural features, especially some novel structural neighborhood features. In cross-validation experiments on the benchmark dataset, PredSAV achieves promising performances with an AUC score of 0.908 and a specificity of 0.838, which are significantly better than that of the other existing methods. Furthermore, we validate the capability of our proposed method by an independent test and gain a competitive advantage as a result. PredSAV, which combines gradient tree boosting with optimally selected neighborhood features, can return reliable predictions in distinguishing between disease-associated and neutral variants. Compared with existing methods, PredSAV shows improved specificity as well as increased overall performance.

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“…The optimal culture conditions are obtained and the flavones production is increased to 2200 μ g/mL. Our work would also be a guide for the “Precision Medicine” with personal SNP data [16] and other tasks in bioinformatics [17, 18]. …”

Section: Discussionmentioning

confidence: 99%

A Computational Method for Optimizing Experimental Environments forPhellinus igniariusvia Genetic Algorithm and BP Neural Network

Sun

Xin

et al. 2016

BioMed Research International

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Flavones, the secondary metabolites of Phellinus igniarius fungus, have the properties of antioxidation and anticancer. Because of the great medicinal value, there are large demands on flavones for medical use and research. Flavones abstracted from natural Phellinus can not meet the medical and research need, since Phellinus in the natural environment is very rare and is hard to be cultivated artificially. The production of flavones is mainly related to the fermentation culture of Phellinus, which made the optimization of culture conditions an important problem. Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL. In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed. Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments. Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL.

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An overview of SNP interactions in genome-wide association studies

Cited by 93 publications

References 85 publications

Optimization to the Culture Conditions forPhellinusProduction with Regression Analysis and Gene-Set Based Genetic Algorithm

Optimization to the Culture Conditions forPhellinusProduction with Regression Analysis and Gene-Set Based Genetic Algorithm

Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties

A Computational Method for Optimizing Experimental Environments forPhellinus igniariusvia Genetic Algorithm and BP Neural Network

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