Gene Expression Prediction using Stacked Temporal Convolutional Network

Kamal, Imam Mustafa; Wahid, Nur Ahmad; Bae, Hyerim

doi:10.1109/bigcomp48618.2020.00-41

Cited by 4 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each WPF method has its advantages and weak points and is suitable for a particular objective or application. A Temporal Convolutional Network (TCN) architecture, in contrast to its predecessor, like Long Short-Term Memory (LSTM), can process long input sequences but require little memory during training [13]. TCN model has been developed for shortterm WPFs, and it outperformed the other existing forecasting methods, such as Support Vector Machine (SVM), LSTM, GRU, and so on [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a modified version of TCN called the Stacked Temporal Convolutional Network (S-TCN), has been developed. S-TCN has demonstrated a good performance in dealing with sequence problems in gene predictions [13] or anomaly detection in IoT. Therefore, this study has enhanced the existing S-TCN model for multi-step ahead wind power forecasting, and the historical measurements for wind speed and wind power were employed as input features.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)

Nguyen

Dzung

et al. 2023

Energies

View full text Add to dashboard Cite

Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management difficulties for power systems. Currently, the methods of wind power forecasting (WPF) are various and numerous. An accurate forecasting method of WPF can help system dispatchers plan unit commitment and reduce the risk of the unreliability of electricity supply. In order to improve the accuracy of short-term prediction for wind power and address the multi-step ahead forecasting, this research presents a Stacked Temporal Convolutional Network (S-TCN) model. By using dilated causal convolutions and residual connections, the suggested solution addresses the issue of long-term dependencies and performance degradation of deep convolutional models in sequence prediction. The simulation outcomes demonstrate that the S-TCN model’s training procedure is extremely stable and has a powerful capacity for generalization. Besides, the performance of the proposed model shows a higher forecasting accuracy compared to other existing neural networks like the Vanilla Long Short-Term Memory model or the Bidirectional Long Short-Term Memory model.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)

Nguyen

Dzung

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…To improve prediction accuracy, they ( Singh et al, 2017 ) integrated attention mechanism into a neural network and proposed a prediction model AttentiveChrome. Temporal Convolutional Network ( Zhu et al, 2018 ; Kamal et al, 2020 ) is also utilized to predict the gene expression from histone modifications. In 2022, Hamdy et al (2022) proposed three variations of CNN models called ConvChrome.…”

Section: Introductionmentioning

confidence: 99%

Predicting gene expression from histone modifications with self-attention based neural networks and transfer learning

2022

View full text Add to dashboard Cite

It is well known that histone modifications play an important part in various chromatin-dependent processes such as DNA replication, repair, and transcription. Using computational models to predict gene expression based on histone modifications has been intensively studied. However, the accuracy of the proposed models still has room for improvement, especially in cross-cell lines gene expression prediction. In the work, we proposed a new model TransferChrome to predict gene expression from histone modifications based on deep learning. The model uses a densely connected convolutional network to capture the features of histone modifications data and uses self-attention layers to aggregate global features of the data. For cross-cell lines gene expression prediction, TransferChrome adopts transfer learning to improve prediction accuracy. We trained and tested our model on 56 different cell lines from the REMC database. The experimental results show that our model achieved an average Area Under the Curve (AUC) score of 84.79%. Compared to three state-of-the-art models, TransferChrome improves the prediction performance on most cell lines. The experiments of cross-cell lines gene expression prediction show that TransferChrome performs best and is an efficient model for predicting cross-cell lines gene expression.

show abstract

ConvChrome: Predicting Gene Expression Based on Histone Modifications Using Deep Learning Techniques

Hamdy

Omar

Maghraby

2022

CBIO

View full text Add to dashboard Cite

Background: Gene regulation is a complex and a dynamic process that not only depends on the DNA sequence of genes, but also is influenced by a key factor called Epigenetic Mechanisms. This factor along with other factors contributes to change the behavior of DNA. While these factors cannot affect the structure of DNA, they can control the behavior of DNA by turning genes "on" or "off" that leads to determine which proteins are transcribed. Objective: This paper will focus on histone modifications mechanism, histones are the group of proteins that bundle the DNA into a structural form called nucleosomes (coils); how DNA wraps with these histone proteins describes how gene can be accessed to express or not. When histones bound tightly to DNA, that make the gene cannot be expressed and vise verse. It is important to know Histone Modifications’ combinatorial patterns, and how these combinatorial patterns can affect and work together to control the process of gene expression. Methods: In this paper, ConvChrome deep learning methodologies are proposed for predicting the gene expression behavior from Histone modifications data as an input to use more than one Convolutional Network model, this happens in order to recognize patterns of histones signals and to interpret their spatial relationship arranged on chromatin structure to give insights into regulatory signatures of histone modifications. Results and Conclusion: The experiments results show that ConvChrome achieved 88.741 % in terms of Area under the Curve (AUC) score, which is an outstanding improvement over the baseline for gene expression classification prediction task from combinatorial interactions among five histone modifications on 56 different cell-types.

show abstract

Gene Expression Prediction using Stacked Temporal Convolutional Network

Cited by 4 publications

References 6 publications

Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)

Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)

Predicting gene expression from histone modifications with self-attention based neural networks and transfer learning

ConvChrome: Predicting Gene Expression Based on Histone Modifications Using Deep Learning Techniques

Contact Info

Product

Resources

About