Integrated biomarker profiling of the metabolome associated with impaired fasting glucose and type 2 diabetes mellitus in large‐scale Chinese patients

Long, Jianglan; Yang, Hui; Yang, Zhao; Jia, Qingquan; Liu, Liwei; Kong, Lingbao; Cui, Huijing; Ding, Shuzhe; Qin, Qian; Zhang, Nana; Xing-zhong, Feng; Yan, Shuxun; Tang, Jinfa; Chen, Shuo; Han, Ying; Jiang, Tao; Zhou, Wen; Qi, Ningning; Deng, Kejun; Sun, Zhi; Lin, Hao; Yan, Dan

doi:10.1002/ctm2.432

Cited by 36 publications

(26 citation statements)

References 10 publications

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“…The study of structural variations in genomes can promote research on genome evolution, significant biological phenotypic changes ( Yang et al, 2020b ; Yang et al, 2020c ; Yin et al, 2020 ), the treatment of many diseases ( Li et al, 2018 ; Yang et al, 2021b ; Long et al, 2021 ), and recommendations for therapeutic drugs ( Wei et al, 2014 ; Ding et al, 2020a ; Ding et al, 2020b ; Wang et al, 2020 ; Wei et al, 2020 ). The accurate prediction of genomic variation is of great importance to studies of many diseases, which indicates the significance of this literature review through which existing variation data were integrated and collected, and a tolerance classification model of various variations was constructed based on sequence information.…”

Section: Discussionmentioning

confidence: 99%

Genomic Variation Prediction: A Summary From Different Views

Lin

2021

Front. Cell Dev. Biol.

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Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.

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Section: Discussionmentioning

confidence: 99%

Genomic Variation Prediction: A Summary From Different Views

Lin

2021

Front. Cell Dev. Biol.

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“…XGBoost ( Chen and Guestrin, 2016 ) is a machine learning method with an excellent classification effect and high efficiency that has been widely used in recent years( Long et al, 2021 ; Yang et al, 2021 ). It stands out from many of the challenges of machine learning and data mining.…”

Section: Methodsmentioning

confidence: 99%

VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost

et al. 2022

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Vesicular transport proteins are related to many human diseases, and they threaten human health when they undergo pathological changes. Protein function prediction has been one of the most in-depth topics in bioinformatics. In this work, we developed a useful tool to identify vesicular transport proteins. Our strategy is to extract transition probability composition, autocovariance transformation and other information from the position-specific scoring matrix as feature vectors. EditedNearesNeighbours (ENN) is used to address the imbalance of the data set, and the Max-Relevance-Max-Distance (MRMD) algorithm is adopted to reduce the dimension of the feature vector. We used 5-fold cross-validation and independent test sets to evaluate our model. On the test set, VTP-Identifier presented a higher performance compared with GRU. The accuracy, Matthew’s correlation coefficient (MCC) and area under the ROC curve (AUC) were 83.6%, 0.531 and 0.873, respectively.

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“…That is, it captures a set of “small but precise” classification features with a small probability of error. While reducing the dimensionality of the feature space in this way, it also speeds up the construction of the classifier model ( Yu XP et al, 2021 ; Long et al, 2021 ; Yang et al, 2021 ). In AOPM, the Max-Relevance-Max-Distance algorithm (MRMD) was used for feature selection, which was proposed by Zou.…”

Section: Methodsmentioning

confidence: 99%

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Zhai

Zhang

et al. 2022

Front. Pharmacol.

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Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

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Integrated biomarker profiling of the metabolome associated with impaired fasting glucose and type 2 diabetes mellitus in large‐scale Chinese patients

Cited by 36 publications

References 10 publications

Genomic Variation Prediction: A Summary From Different Views

Genomic Variation Prediction: A Summary From Different Views

VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

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