Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information

Dao, Fu‐Ying; Lv, Hao; Fullwood, Melissa J.; Lin, Hao

doi:10.34133/2022/9780293

Cited by 28 publications

(10 citation statements)

References 63 publications

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“…The precision ( P ), recall ( R ), accuracy (ACC), and F 1 score were computed to measure the performance of models across the prediction process. − According to the definition of these evaluation quantities, they can be expressed as follows P = TP TP + FP R = TP TP + FN ACC = TP + FP TP + FP + TN + FN F 1 − score = 2 italicPR P + R where TP, TN, FP, and FN represent the samples’ true positive, true negative, false positive, and false negative, respectively.…”

Section: Methodsmentioning

confidence: 99%

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

Zhang,

Liu,

et al. 2024

ACS Omega

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In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with positionspecific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.

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Section: Methodsmentioning

confidence: 99%

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

Zhang,

Liu,

et al. 2024

ACS Omega

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“…At present, there are three main test methods to evaluate the prediction results: the re-substitution test, the Jackknife test and the k-fold cross-validation test (Zhang et al, 2020;Zhang et al, 2021b;Deng et al, 2021;Liu et al, 2021;Tabaie et al, 2021;Ao et al, 2022a;Dai et al, 2022;Dao et al, 2022;Jin et al, 2022;Wei et al, 2022;Xiao et al, 2022;Zhou et al, 2022). Chou and Zhang have discussed in depth the classification performance estimation in bioinformatics and found the Jackknife test and k-fold cross-validation test have extrapolation ability in statistics (Malik et al, 2021;Hasan et al, 2022).…”

Section: Evaluation Methodsmentioning

confidence: 99%

Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition

Deng

et al. 2023

Front. Genet.

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Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied.Methods: In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location.Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.

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“…In addition, we also investigated the performance of SVM, 40 Random Forest (RF), 41 Logistic Regression (LR), 42 K-Nearest Neighbor (KNN), 43 AdaBoost, 44 GradientBoosting(GB), 45 and LightGBM(LGBM). 46 Because these algorithms were inferior to XGBoost in performance comparison, we will not introduce them in detail.…”

Section: Feature Selectionmentioning

confidence: 99%

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms

Liu

Shi

Gao

et al. 2023

J. Chem. Inf. Model.

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Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes. The development of artificial intelligence provides a powerful tool for accelerating the discovery of antidiabetic drugs. This work aims to establish a predictor called iPADD for discovering potential antidiabetic drugs. In the predictor, we used four kinds of molecular fingerprints and their combinations to encode the drugs and then adopted minimum-redundancy−maximum-relevance (mRMR) combined with an incremental feature selection strategy to screen optimal features. Based on the optimal feature subset, eight machine learning algorithms were applied to train models by using 5-fold cross-validation. The best model could produce an accuracy (Acc) of 0.983 with the area under the receiver operating characteristic curve (auROC) value of 0.989 on an independent test set. To further validate the performance of iPADD, we selected 65 natural products for case analysis, including 13 natural products in clinical trials as positive samples and 52 natural products as negative samples. Except for abscisic acid, our model can give correct prediction results. Molecular docking illustrated that quercetin and resveratrol stably bound with the diabetes target NR1I2. These results are consistent with the model prediction results of iPADD, indicating that the machine learning model has a strong generalization ability. The source code of iPADD is available at https:// github.com/llllxw/iPADD.

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Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information

Cited by 28 publications

References 63 publications

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms

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