Kernel principal component analysis combining rotation forest method for linearly inseparable data

Lu, Huijuan; Meng, Yaqiong; Yan, Ke; Gao, Zhigang

doi:10.1016/j.cogsys.2018.01.006

Cited by 25 publications

(24 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To fulfill a valid PCA in the feature space which can never be explicitly calculated, the kernel data is required to be centralized:

\bar{Ψ} = Ψ - 1_{D} Ψ - Ψ 1_{D} + 1_{D} Ψ 1_{D},

where

1_{D}

indicates a

D

‐by‐

D

identity matrix divided by value

D

. Several frequently‐used kernel functions (Lu, Meng, Yan, & Gao, ) for high‐dimensional nonlinear mapping are shown as following:…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Lin

Jiang

et al. 2019

Natural Resource Modeling

View full text Add to dashboard Cite

The kernel‐based statistical semantic topic model is introduced for comprehending three species of internationally important Ramsar wetland documents describing the Lashi Lake wetland in the Yunnan Province, the Yancheng wetland in the Jiangsu Province, and the Zoige wetland in the Sichuan Province of China. Latent Dirichlet allocation (LDA) features are used to represent the semantic components of wetland documents. Kernel principal component analysis (KPCA) maps the topic components to the kernel space to attain the low dimensional principal components. Support vector machines (SVMs) are used to comprehend the semantic distribution of distinct wetland documents in the kernel space. The LDA+KPCA+SVM algorithm reaches 77.0% training and 75.9% test accuracy and 0.902 training and 0.840 test mean average precision scores in the application of comprehending the wetland documents, respectively. The performance of the proposed kernel‐based model is superior to the traditional models of LDA+SVM and LDA+PCA+SVM.

show abstract

“…To fulfill a valid PCA in the feature space which can never be explicitly calculated, the kernel data is required to be centralized:

\bar{Ψ} = Ψ - 1_{D} Ψ - Ψ 1_{D} + 1_{D} Ψ 1_{D},

where

1_{D}

indicates a

D

‐by‐

D

identity matrix divided by value

D

. Several frequently‐used kernel functions (Lu, Meng, Yan, & Gao, ) for high‐dimensional nonlinear mapping are shown as following:…”

Section: Methodsmentioning

confidence: 99%

“…D indicates a D-by-D identity matrix divided by value D. Several frequently-used kernel functions(Lu, Meng, Yan, & Gao, 2019) for high-dimensional nonlinear mapping are shown as following…”

mentioning

confidence: 99%

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Lin

Jiang

et al. 2019

Natural Resource Modeling

View full text Add to dashboard Cite

show abstract

“…RF is a successful classifier proposed by Rodriguez et al 36 . The basic idea of RF is to simultaneously build accurate and robust differential ensemble classifiers [37][38][39] . When the algorithm executes, RF first randomly divides the sample set, and then uses the transformation method to transform the subset to increase the difference between the subsets.…”

Section: Drug Molecular Characterization Studies Show That Molecularmentioning

confidence: 99%

Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions

Wang

You

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Accumulating evidence has shown that drug-target interactions (Dtis) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential Dtis. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and integrate them with the chemical sub-structures descriptor, and finally utilize Rotation Forest (RF) classifier to accurately predict whether there is interaction between the drug and the target protein. In the 5-fold cross-validation (CV) experiment, the average accuracy of the proposed model on the benchmark datasets of Enzymes, Ion Channels, GPCRs and Nuclear Receptors reached 0.9140, 0.8919, 0.8724 and 0.8111, respectively. In order to fully evaluate the performance of the proposed model, we compare it with different feature extraction model, classifier model, and other state-of-the-art models. Furthermore, we also implemented case studies. As a result, 8 of the top 10 drug-target pairs with the highest prediction score were confirmed by related databases. These excellent results indicate that the proposed model has outstanding ability in predicting Dtis and can provide reliable candidates for biological experiments. Drugs can regulate the physiological function of the human body, to provide guarantee for disease prevention, treatment and other aspects. More importantly, the discovery and identification of drug targets is the source of drug research, which plays a key role in the success of drug development. The complexity of the etiologies of most diseases leading to disease-related genes or proteins may be potential drug targets, but because of target specificity, robustness of biological networks and other factors, the number of newly developed drugs does not rapidly increase with the development of proteomics and chemical genomics. So far, only a small number of targets in the human genome, in which the total number of pharmacological interest is about 6000 to 8000, have been confirmed to be associated with approved drugs 1-4. As the experiment-based method having the disadvantage of high cost, time consuming and limitations of small-scale in identifying drug-target interactions, researchers try to mine drug-related targets in the whole genome using computational-based methods 5-11. At present, researchers have designed many computational-based models to analyze and predict drug-target interactions (DTIs) 12-18. For example, Yamanishi et al. designed a model based on statistical algorithm to predict potential DTIs, which ...

show abstract

“…In this paper, we use the feature weighted rotation forest (FwRF) [33][34][35] to accurately predict DTIs. Compared with the original rotation forest, FwRF adds the function of weight selection.…”

Section: Feature Weighted Rotation Forest Classifiermentioning

confidence: 99%

RFDTI: Using Rotation Forest with Feature Weighted for Drug-Target Interaction Prediction from Drug Molecular Structure and Protein Sequence

Wang

You

et al. 2020

Preprint

View full text Add to dashboard Cite

The identification and prediction of Drug-Target Interactions (DTIs) is the basis for screening drug candidates, which plays a vital role in the development of innovative drugs. However, due to the time-consuming and high cost constraints of biological experimental methods, traditional drug target identification technologies are often difficult to develop on a large scale. Therefore, in silico methods are urgently needed to predict drug-target interactions in a genome-wide manner. In this article, we design a new in silico approach, named RFDTI to predict the DTIs combine Feature weighted Rotation Forest (FwRF) classifier with protein amino acids information. This model has two outstanding advantages: a) using the fusion data of protein sequence and drug molecular fingerprint, which can fully carry information; b) using the classifier with feature selection ability, which can effectively remove noise information and improve prediction performance. More specifically, we first use Position-Specific Score Matrix (PSSM) to numerically convert protein sequences and utilize Pseudo Position-Specific Score Matrix (PsePSSM) to extract their features.Then a unified digital descriptor is formed by combining molecular fingerprints representing drug information. Finally, the FwRF is applied to implement on Enzyme, Ion Channel, GPCR, and Nuclear Receptor data sets. The results of the five-fold cross-validation experiment show that the prediction accuracy of this approach reaches 91.68%, 88.11%, 84.72% and 78.33% on four benchmark data sets, respectively. To further validate the performance of the RFDTI, we compare it with other excellent methods and Support Vector Machine (SVM) model. In addition, 7 of the 10 highest predictive scores in predicting novel DTIs were validated by relevant databases. The experimental results of cross-validation indicated that RFDTI is feasible in predicting the relationship among drugs and target, and can provide help for the discovery of new candidate drugs.

show abstract

Kernel principal component analysis combining rotation forest method for linearly inseparable data

Cited by 25 publications

References 27 publications

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions

RFDTI: Using Rotation Forest with Feature Weighted for Drug-Target Interaction Prediction from Drug Molecular Structure and Protein Sequence

Contact Info

Product

Resources

About