Neighborhood Constraint Matrix Completion for Drug-Target Interaction Prediction

Fan, Xin; Hong, Yuxiang; Liu, Xiaohu; Zhang, Yaogong; Xie, Minzhu

doi:10.1007/978-3-319-93034-3_28

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…In formula (1), ω represents the normal vector of the hyperplane; x i represents the training sample; y i represents the sample category; b represents the threshold of sample training; C represents the penalty parameter; ε i represents the relaxation variable. For nonlinear cases, the kernel function k(x i , x j ) is introduced to map the samples from the low-dimensional space to the high-dimensional space, so that the samples can be separable in the high-dimensional space [23]. As shown in (2).…”

Section: B Support Vector Machinesmentioning

confidence: 99%

Imbalanced Data Classification Method Based on LSSASMOTE

Wang

Liu

2023

IEEE Access

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Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper. First, the kernel function parameters and penalty parameters of the support vector machine (SVM) were optimized using levy sparrow search algorithm (LSSA), and a fitness function was correspondingly designed. Then, during the optimization process, SMOTE sampling rate was combined, and LSSA iteration was used to select the best combination of SVM parameters and SMOTE sampling rate. In addition, the oversampled samples were noise processed by Tomek Link. In this case, the LSSASMOTE+SVM classification model was constructed to classify the imbalanced data.Eight of the datasets used in the experiments were obtained on UCI and KEEL, and the other three datasets were created manually. The experimental results confirm that the model can effectively improve the classification accuracy of imbalanced data and can be used as a new imbalanced data classification method.

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Section: B Support Vector Machinesmentioning

confidence: 99%

Imbalanced Data Classification Method Based on LSSASMOTE

Wang

Liu

2023

IEEE Access

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“…For the aforementioned reasons, state-of-the-art DTI techniques are based on machine learning [13][14][15][16][17]. Moreover, the increasing interest is also catalysed by the analogies between DTI and the well-studied recommendation tasks [18][19][20] Despite all the aforementioned efforts, accurate prediction of drug-target interactions still remained a challenge.…”

Section: Plos Onementioning

confidence: 99%

Modified linear regression predicts drug-target interactions accurately

2020

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State-of-the-art approaches for the prediction of drug-target interactions (DTI) are based on various techniques, such as matrix factorisation, restricted Boltzmann machines, networkbased inference and bipartite local models (BLM). In this paper, we propose the framework of Asymmetric Loss Models (ALM) which is more consistent with the underlying chemical reality compared with conventional regression techniques. Furthermore, we propose to use an asymmetric loss model with BLM to predict drug-target interactions accurately. We evaluate our approach on publicly available real-world drug-target interaction datasets. The results show that our approach outperforms state-of-the-art DTI techniques, including recent versions of BLM. OPEN ACCESSCitation: Buza K, Peška L, Koller J (2020) Modified linear regression predicts drug-target interactions accurately. PLoS ONE 15(4): e0230726. https://doi. datasets have been used in various studies, such as [17, 19, 20, 22, 28, 29].Each dataset contains an interaction matrix M between drugs and targets, a drug-drug similarity matrix S D and a target-target similarity matrix S T . Similarities between targets were PLOS ONEModified linear regression predicts drug-target interactions accurately PLOS ONE | https://doi.

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De novo Prediction of Drug-Target Interaction via Laplacian Regularized Schatten-p Norm Minimization

Yang

et al. 2020

Bioinformatics Research and Applications

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Neighborhood Constraint Matrix Completion for Drug-Target Interaction Prediction

Cited by 6 publications

References 21 publications

Imbalanced Data Classification Method Based on LSSASMOTE

Imbalanced Data Classification Method Based on LSSASMOTE

Modified linear regression predicts drug-target interactions accurately

De novo Prediction of Drug-Target Interaction via Laplacian Regularized Schatten-p Norm Minimization

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