Prediction method of dioxin emission concentration based on PCA and deep forest regression

2021 ◽  
Author(s):  
Wen Xu ◽  
Jian Tang ◽  
Heng Xia ◽  
Zijian Sun
Keyword(s):  
Prediction Method ◽  
Dioxin Emission ◽  
Deep Forest

scholarly journals A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiongfei Tian ◽  
Ling Shen ◽  
Zhenwu Wang ◽  
Liqian Zhou ◽  
Lihong Peng
Keyword(s):  
Protein Interactions ◽  
Forest Structure ◽  
Rna Binding ◽  
Prediction Models ◽  
Rna Binding Proteins ◽  
Prediction Method ◽  
Nucleotide Composition ◽  
Sequence Information ◽  
Deep Forest ◽  
Classical Association

AbstractLong noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA–protein Interactions (LPIs) is significantly important to well characterize the biological functions and mechanisms of lncRNAs. Existing computational methods have been effectively applied to LPI prediction. However, the majority of them were evaluated only on one LPI dataset, thereby resulting in prediction bias. More importantly, part of models did not discover possible LPIs for new lncRNAs (or proteins). In addition, the prediction performance remains limited. To solve with the above problems, in this study, we develop a Deep Forest-based LPI prediction method (LPIDF). First, five LPI datasets are obtained and the corresponding sequence information of lncRNAs and proteins are collected. Second, features of lncRNAs and proteins are constructed based on four-nucleotide composition and BioSeq2vec with encoder-decoder structure, respectively. Finally, a deep forest model with cascade forest structure is developed to find new LPIs. We compare LPIDF with four classical association prediction models based on three fivefold cross validations on lncRNAs, proteins, and LPIs. LPIDF obtains better average AUCs of 0.9012, 0.6937 and 0.9457, and the best average AUPRs of 0.9022, 0.6860, and 0.9382, respectively, for the three CVs, significantly outperforming other methods. The results show that the lncRNA FTX may interact with the protein P35637 and needs further validation.

DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features

2019 ◽  
Author(s):  
Yanyi Chu ◽  
Aman Chandra Kaushik ◽  
Xiangeng Wang ◽  
Wei Wang ◽  
Yufang Zhang ◽  
...  
Keyword(s):  
Drug Target ◽  
False Positive Rate ◽  
Prediction Method ◽  
Computational Prediction ◽  
Data Sets ◽  
Hybrid Features ◽  
Positive Rate ◽  
Deep Forest ◽  
Wet Lab ◽  
Test Sets

Abstract Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.

scholarly journals DTI-CDF: a CDF model towards the prediction of DTIs based on hybrid features

2019 ◽  
Author(s):  
Yan-Yi Chu ◽  
Yu-Fang Zhang ◽  
Wei Wang ◽  
Xian-Geng Wang ◽  
Xiao-Qi Shan ◽  
...  
Keyword(s):  
Drug Target ◽  
State Of The Art ◽  
False Positive Rate ◽  
Prediction Method ◽  
Computational Prediction ◽  
Data Sets ◽  
Training Set ◽  
Hybrid Features ◽  
Positive Rate ◽  
Deep Forest

AbstractDrug-target interactions play a crucial role in target-based drug discovery and exploitation. Computational prediction of DTIs has become a popular alternative strategy to the experimental methods for identification of DTIs of which are both time and resource consuming. However, the performances of the current DTIs prediction approaches suffer from a problem of low precision and high false positive rate. In this study, we aimed to develop a novel DTIs prediction method, named DTI-CDF, for improving the prediction precision based on a cascade deep forest model which integrates hybrid features, including multiple similarity-based features extracted from the heterogeneous graph, fingerprints of drugs, and evolution information of target protein sequences. In the experiments, we built five replicates of 10 fold cross-validations under three different experimental settings of data sets, namely, corresponding DTIs values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training set are missed, but existed in the test set. The experimental results show that our proposed approach DTI-CDF achieved significantly higher performance than the state-of-the-art methods.

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

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