A robust method for shift detection in time series

Dehling, Herold; Fried, Roland; Wendler, Martin

doi:10.1093/biomet/asaa004

Cited by 17 publications

(10 citation statements)

References 22 publications

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“…Differences in prediction performances, including loss (Val), Acc (Val), BAC, F, AUC, Acc (Test), and MCC were analyzed by the Mann-Whitney U test (Chakraborty and Chaudhuri, 2014;Dehling et al, 2015;Dedecker and Saulière, 2017). Finding of corrected P (Pc) < 0.05 is significant based on corrections from multiple testing, such as the Bonferroni's method.…”

Section: Discussionmentioning

confidence: 99%

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

Matsuzaka

Uesawa

2020

Front. Bioeng. Biotechnol.

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The progesterone receptor (PR) is important therapeutic target for many malignancies and endocrine disorders due to its role in controlling ovulation and pregnancy via the reproductive cycle. Therefore, the modulation of PR activity using its agonists and antagonists is receiving increasing interest as novel treatment strategy. However, clinical trials using the PR modulators have not yet been found conclusive evidences. Recently, increasing evidence from several fields shows that the classification of chemical compounds, including agonists and antagonists, can be done with recent improvements in deep learning (DL) using deep neural network. Therefore, we recently proposed a novel DL-based quantitative structure-activity relationship (QSAR) strategy using transfer learning to build prediction models for agonists and antagonists. By employing this novel approach, referred as DeepSnap-DL method, which uses images captured from 3-dimension (3D) chemical structure with multiple angles as input data into the DL classification, we constructed prediction models of the PR antagonists in this study. Here, the DeepSnap-DL method showed a high performance prediction of the PR antagonists by optimization of some parameters and image adjustment from 3Dstructures. Furthermore, comparison of the prediction models from this approach with conventional machine learnings (MLs) indicated the DeepSnap-DL method outperformed these MLs. Therefore, the models predicted by DeepSnap-DL would be powerful tool for not only QSAR field in predicting physiological and agonist/antagonist activities, toxicity, and molecular bindings; but also for identifying biological or pathological phenomena.

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Section: Discussionmentioning

confidence: 99%

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

Matsuzaka

Uesawa

2020

Front. Bioeng. Biotechnol.

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“…Differences in prediction performances in terms of the parameter loss (Val), Acc (Val), BAC, F, AUC, Acc (Test), and MCC were analyzed using the Mann-Whitney U test [79][80][81]. For each of the 10 angles (38,38,38), (42,42,42), (50,50,50), (55,55,55), (65,65,65), (85, 85, 85), (105, 105, 105), (176, 176, 176), (300, 300, 300), and (360, 360, 360) in the two datasets Tra/Val/Test = 1:1:1 and 2:2:2, seven evaluation indicators of loss (Val), Acc (Val), BAC, F, AUC, Acc (Test), and MCC are represented as box plots.…”

Section: Discussionmentioning

confidence: 99%

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

et al. 2020

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The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure–activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap–DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap–DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity.

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“…, k q have to be estimated. Since methodologies to detect change points in the context of independent random variables cannot be applied directly in time series [22,23], parametric, nonparametric, and Bayesian methodologies have also been developed [12,16,[24][25][26][27][28][29][30][31].…”

Section: Parametric Methodologies For Detecting Change Pointsmentioning

confidence: 99%

Parametric Methodologies for Detecting Changes in Maximum Temperature of Tlaxco, Tlaxcala, México

Cortés

Hernández

Guevara

et al. 2019

Journal of Probability and Statistics

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In this paper, comparison results of parametric methodologies of change points, applied to maximum temperature records from the municipality of Tlaxco, Tlaxcala, México, are presented. Methodologies considered are likelihood ratio test, score test, and binary segmentation (BS), pruned exact linear time (PELT), and segment neighborhood (SN). In order to compare such methodologies, a quality analysis of the data was performed; in addition, lost data were estimated with linear regression, and finally, SARIMA models were adjusted.

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A robust method for shift detection in time series

Cited by 17 publications

References 22 publications

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

Parametric Methodologies for Detecting Changes in Maximum Temperature of Tlaxco, Tlaxcala, México

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