CrystalM: A Multi-View Fusion Approach for Protein Crystallization Prediction

Wang, Yübo; Ding, Yongsheng; Tang, Jijun; Dai, Yu; Guo, Fei

doi:10.1109/tcbb.2019.2912173

Cited by 19 publications

(6 citation statements)

References 45 publications

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“…To date, a strategy that includes deep learning and PSSM profiles has been frequently adopted to realize the identification of unknown proteins and has achieved excellent results. However, the strategy is slightly inefficient, so in this work, we used other machine learning models and adopted RPSSM ( Ding et al, 2014 ), CSP-SegPseP-SegACP ( Liang et al, 2015 ), AATP ( Zhang et al, 2012 ), DWT ( Wang et al, 2017 ; Wang, 2019 ) and SOMA ( Liang and Zhang, 2017 ) to extract features from the PSSM matrix and make a comparison. Among them, AATP and CSP-SegPseP-SegACP have the highest MCC and AUC, so they are selected as feature extraction methods.…”

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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Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…In this study, we utilized accuracy (ACC), sensitivity (SN), specificity (SP), and Matthew's correlation coefficient (MCC) to evaluate the performance of our model 39,50–66 . They are calculated as follows:

ACC = \frac{TP + TN}{TP + FP + TN + FN},

SN = \frac{TP}{TP + FN},

SP = \frac{TN}{TN + FP},

MCC = \frac{TP \times TN - FP \times FN}{\sqrt{((), TP goodbreak+ FP) ((), TP goodbreak+ FN) ((), TN goodbreak+ FP) ((), TN goodbreak+ FN)}} .

…”

Section: Metricsmentioning

confidence: 99%

Prediction of cell penetrating peptides and their uptake efficiency using random forest‐based feature selections

Liu

Ding

Rong³

et al. 2022

AIChE Journal

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Cell penetrating peptides (CPPs) are short peptides that can carry biomolecules of varying sizes across the cell membrane into the cytoplasm. Correctly identifying CPPs is the basis for studying their functions and mechanisms. Here, we propose a novel CPP predictor that is able to predict CPPs and their uptake efficiency. In our method, five feature descriptors are applied to encode the sequence and compose a hybrid feature vector. Afterward, the wrapper + random forest algorithm is employed, which combines feature selection with the prediction process to find features that are crucial for identifying CPPs. The jackknife cross validation result shows that our predictor is comparable to state-of-the-art CPP predictors, and our method reduces the feature dimension, which improves computational efficiency and avoids overfitting, allowing our predictor to be adopted to identify large-scale CPP data.

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“…Traditional antioxidant drug screening and discovery are carried out through biochemical experiments, which not only has a long time period and high cost, but also has the risk of failure in experiments ( Lv et al, 2020a ; Cheng et al, 2020 ; Cheng Y et al, 2021 ; Lv Z et al, 2021 ; Dong et al, 2021 ; Goto et al, 2021 ; Zeng et al, 2022 ). With the continuous improvement of computer technology and genome databases, methods such as data mining and machine learning are more and more widely used in biological information, drug screening and other fields ( Cheng et al, 2018 ; Wang et al, 2018 ; Ding et al, 2019 ; Wang et al, 2019 ; Zeng et al, 2020a ; Zhang CH et al, 2020 ; Zhang J et al, 2020 ; Lyu et al, 2020 ; Zhao X et al, 2021 ; Niu et al, 2021 ). In recent years, many researchers have been exploring machine learning models suitable for identifying antioxidant proteins.…”

Section: Introductionmentioning

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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CrystalM: A Multi-View Fusion Approach for Protein Crystallization Prediction

Cited by 19 publications

References 45 publications

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

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

Prediction of cell penetrating peptides and their uptake efficiency using random forest‐based feature selections

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

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