Molecular Computing and Bioinformatics

Liang, Xin; Zhu, Wen; Lv, Zhibin; Zou, Quan

doi:10.3390/molecules24132358

Cited by 19 publications

(13 citation statements)

References 56 publications

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“…In addition to the commonly used evaluation indicators sensitivity (SN), specificity (SP) and accuracy (ACC), AOPM also provided a Matthew’s Correlation Coefficient (MCC) and an Area Under the Curve (AUC) to evaluate the performance of the ensemble classifier, and the formulas were defined as follows ( Liang et al, 2019 ; Lv et al, 2020c ):

where

is the number of samples judged as positive for the positive class,

is the number of samples judged as positive for the negative class,

is the number of samples judged as negative for the positive class, and

is the number of samples judged as negative for the negative class. MCC is an index used in machine learning to measure the classification performance of two categories.…”

Section: Resultsmentioning

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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where

is the number of samples judged as positive for the positive class,

is the number of samples judged as positive for the negative class,

is the number of samples judged as negative for the positive class, and

is the number of samples judged as negative for the negative class. MCC is an index used in machine learning to measure the classification performance of two categories.…”

Section: Resultsmentioning

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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“…The fingerprints of structural bonds represent the drugs as Boolean substructure vectors by separating the drug molecular structure into a variety of segments. Although the molecule is sliced into individual segments, it still retains the entire structure information of the drug [ 25 , 26 ]. These printers reduce the information loss and error accumulation in the process of description and screening.…”

Section: Methodsmentioning

confidence: 99%

An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints

Zhao

Huang

Zhan

et al. 2021

BioMed Research International

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Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

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“…We assert that it is improper for series expansions not to be considered in the chemical integration of arbitrary ODEs in molecular computing [17].…”

Section: Closing Remarksmentioning

confidence: 99%

Chemical Integration of ODEs using Idealized Abstract Solutions

Lee¹

2023

Preprint

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In this work, we propose a general inversion framework to non-uniquely invert a very large class of ordinary differential equations (ODEs) into chemical reaction networks. A thorough treatment of the relevant chemical reaction network theory from the literature is given. Various simulation results are provided to augment the selection procedure for the inverse framework, where a previously known kineticization strategy is shown to be deterministically excellent but undesirable in chemical simulations. The utility of the framework is verified by simulating reaction network forms of meaningful ODE systems, and their time series are analyzed. In particular, we provide simulations of deterministic chaotic attractors whose newly discovered reaction networks are non-equivalent with any existing chemical interpretations within the literature, as well as presenting exemplary figures which may form a roadmap to the successful biochemical implementation of the integration of ODE systems.

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Molecular Computing and Bioinformatics

Cited by 19 publications

References 56 publications

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

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

An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints

Chemical Integration of ODEs using Idealized Abstract Solutions

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