BP neural network modeling with sensitivity analysis on monotonicity based Spearman coefficient

Zhou, Yang; Li, Shaojun

doi:10.1016/j.chemolab.2020.103977

Cited by 29 publications

(8 citation statements)

References 30 publications

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“…The different components contain different gene expression information. The correlation between each component and the original data is quantified by Spearman’s [ 18 ] rank-order relationship, and the correlation heat map is shown in Figure 2 c. It is obvious that different components contain different gene expression information, and aggregating all components will obtain the total gene expression information. This indicates the high reliability of the multi-scale analysis method proposed in this study.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition

Zhou

Jia

Shi

et al. 2022

IJMS

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RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points.

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Section: Resultsmentioning

confidence: 99%

Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition

Zhou

Jia

Shi

et al. 2022

IJMS

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“…单维数据的正确性检测在一定程度上保证了单维数据的准确性。然而, 无法保证维度之间的准确性。维度间相关性的准确性会影响后续特征选择的结果, 进而影响模型预测精度。因此, 提升材料数据的准确性依赖于维度间正确的相关关系。对于多维数据间的相关性, 现有的检测方法有很多。例如, 对于线性关系, 通常使用皮尔逊相关系数 [22] (Pearson Correlation Coefficient, PCC), 斯皮尔曼相关系数 [23] (Spearman Correlation Coefficient, SCC)进行维度间相关性的检测。与 PCC 相比, SCC 对于数据错误和极端值不敏感, 使用不频繁。对于非线性关系, 通常使用距离相关系数 [24]…”

Section: 第二阶段: 基于描述符相关性规则的多维度数据相关性检测unclassified

Detection Method on Data Accuracy Incorporating Materials Domain Knowledge

Shi¹,

SUN²,

MA³

et al. 2022

Journal of Inorganic Materials

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Due to the characteristics of small samples, high dimensions, and much noise, materials data often produce inconsistent results with those obtained from domain experts when used for machine learning modeling.For the whole process of machine learning, developing machine learning models embedding materials domain knowledge is a solution to this problem. The accuracy of materials data directly affects the reliability of data-driven materials performance prediction. Here, a data accuracy detection method incorporating materials domain knowledge is proposed by focusing on the data preprocessing stage in the machine learning application process. Firstly, a 1312materials domain knowledge database is constructed based on the knowledge from materials experts. Secondly, it is coordinated with the data-driven data accuracy detection method to perform single-dimensional data accuracy detection based on the rule for value of descriptors, multi-dimensional data correlation detection based on the rule for correlation of descriptors, and full-dimensional data reliable detection based on multi-dimensional similar sample identification strategy from both data and domain knowledge perspectives. For the anomalous data identified at each stage, they are corrected by incorporating the materials domain knowledge. Furthermore, domain knowledge is incorporated into the whole process of the data accuracy detection method to ensure high accuracy of the dataset from the initial stage. Finally, experiments on the NASICON-type solid electrolyte activation energy prediction dataset demonstrate that this method can effectively identify anomalous data and make reasonable corrections. Compared with the original dataset, the prediction accuracy of all six machine learning models based on the revised dataset is improved to different degrees, among which R 2 achieves a 33% improvement on the optimal model.

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“…BP neural network is widely used in all walks of life because of its strong adaptability, including nonlinear mapping ability, self-learning ability, adaptive ability, generalization ability, fault tolerance ability, and other advantages [39,42,43]. At the same time, many scholars have improved the BP neural network considering its shortcomings and deficiencies, which improves the accuracy of the model [44][45][46].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Probabilistic Prediction of Unsafe Event in Air Traffic Control Department Based on the Improved Backpropagation Neural Network

Liao

Miao

Yang

2021

Mathematical Problems in Engineering

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Air traffic control is an important tool to ensure the safety of civil aviation. For the departments that do the work of air traffic control, reducing the percentage of unsafe event is the core task of safety management. If the relationship between the percentage of unsafe event and their influencing factors can be effectively clarified, then the probability of unsafe event in some control department can be predicted. So, it is of great importance to improve the level of safety management. To quantitatively estimate the probability of unsafe event, a three-layer BP neural network model is introduced in this paper. First, a probabilistic representation of unsafe event related to air traffic control department is made, and then, the probability of different classes of unsafe events and safe events is taken as the outputs of the BP neural network, the factors influencing occurrence of unsafe event connected with air traffic control is taken as inputs, and the sigmoid function is chosen as activation function for the hidden layer. Based on the error function of neural network, it is proved that the general BP neural network has two drawbacks when used for the training of small probability events, which are as follows: the pattern does not ensure that the sum of probability of all events is equal to one and the relative error between the actual outputs and desired outputs is very large after the training of neural network. The reason proved in this paper is that the occurrence rate of the unsafe event is much smaller than that of the safe event, resulting in each weight in the hide layer being subjected to the desired outputs of the safe event when using the gradient descent method for network training. To address this issue, a new mapping method is put forward to reduce the large difference of the desired outputs between the safe event and unsafe event. It is theoretically proved that the mapping method proposed in this paper can not only improve the training accuracy but also ensure that the sum of probability is equal to one. Finally, a numeric example is given to demonstrate that the method proposed in this paper is effective and feasible.

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BP neural network modeling with sensitivity analysis on monotonicity based Spearman coefficient

Cited by 29 publications

References 30 publications

Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition

Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition

Detection Method on Data Accuracy Incorporating Materials Domain Knowledge

Probabilistic Prediction of Unsafe Event in Air Traffic Control Department Based on the Improved Backpropagation Neural Network

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