Recurrent Neural Network for Gene Regulation Network Construction on Time Series Expression Data

Zhao, Yue; Joshi, Pujan; Shin, Dong-Guk

doi:10.1109/bibm47256.2019.8983068

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…RNNs, particularly LSTM recurrent structures, have been recurrently deployed in various studies. They've been instrumental in identifying genes associated with tumour diagnosis, pinpointing breast cancer cells, distinguishing between cancerous and healthy cells, and recognizing biological entities [95][96][97][98]. Furthermore, Zhao et al pioneered an RNN-centric model targeting transcriptional target factor identification [99].…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Utilizing Machine Learning Techniques for Categorizing Cancer Based on Gene Expression Data: A Review

Hembrom T., Hazra S., Barman D.

2024

jes

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Cancer is a group of diseases which share one common feature: the growth of abnormal cells, thus ranking as the second leading cause of death globally, after cardiovascular diseases in WHO's report. Evaluating gene expression is based on the fact that it is the genesis of early cancer detection, the concurrence of the molecular and genetic processes. By using DNA microarray techniques and RNA-sequencing approaches, researchers in computational genomics can give quantitative measures of gene expression levels providing very accurate input data for computational evaluation. The current paper is about machine learning technology, which identifies cancer subtypes according to patterns of gene expression. It embraces the two distinct methodologies which are traditional plus deep learning with high proficiency focused on the cancer-related gene. The outline includes the most popular deep neural network designs such as MLPs, CNN, RNN, GNN, and the recently emerged Transformer networks. The review describes common data collection methods used in this field and some of the essential datasets for supervised machine learning. In addition, the specific techniques developed to cover the complicated horizontal spread of gene expression data are also presented. The article explores theoretical possibilities for the promotion of machine learning-based gene expression analysis in cancer classification towards the end.

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Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Utilizing Machine Learning Techniques for Categorizing Cancer Based on Gene Expression Data: A Review

Hembrom T., Hazra S., Barman D.

2024

jes

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“…LSTM recurrent networks were frequently used in other related works to find associated genes for tumor diagnosis, breast cancer detection, identify cancerous cells from normal cells, and biological entity recognition [107][108][109][110]. Zhao et al [111] developed an RNN model to identify the transcriptional target factor. The memetic technique was used in [112] to learn RNN parameters, while LASSO-RNN was used to rebuild Gene Regulatory Networks (GRNs).…”

Section: Multi-layer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Alharbi

Vakanski

2023

Bioengineering

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Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

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“…Owing to its memorability, parameter sharing, and Turing completeness, the RNN has advantages when learning the nonlinear characteristics of a sequence. 18 Currently, the RNN model and its modifications are widely used in time-series signal modeling, 19 prediction, 20 classification, and recognition, 21 which are also very suitable for processing driving data and driver's intention identification. 13 The LSTM network is one of the RNN models with an improved special structure, which can effectively solve the long-term dependence problem of a normal RNN.…”

Section: Establishment Of Starting Intention Identification Model Bas...mentioning

confidence: 99%

Research on the identification of DCT vehicle driver’s starting intention based on LSTM neural network and multi-sensor data fusion

Liu

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Currently, the objective evaluation of the DCT vehicle drivability requires the accurate identification of the driver’s intention and vehicle state as well as the selection of the targeted evaluation indicators. The existing identification methods usually cannot divide the driver’s intentions in detail and make full use of the characteristics of time-series signals. Simultaneously, external kinematic sensors are more commonly used than the sensors of vehicle powertrain, which impacts the recognition effect. This paper proposes a new method for identifying the DCT vehicle driver’s starting intentions based on an LSTM neural network and multi-sensor data fusion. The DCT vehicle driver’s starting intentions are subdivided and defined based on human–vehicle interaction analysis and K-means clustering. The input of the model consists of 11-dimensional variables that include motion parameters of the vehicle collected by the external sensors and the powertrain parameters collected by onboard sensors. The method proposed in this paper first establishes a recognition window, which is utilized to extract the starting process samples from the DCT vehicle driving data. Second, the 11 variables of each sample are used as one set of multi-dimensional time-series signals, which are preprocessed through wavelet denoising. Finally, the LSTM network is used to identify the samples. The identification results indicate that the highest recognition accuracy of the proposed algorithm is 94.27%, which is approximately 5% higher than conventional methods, such as fully connected neural networks and support vector machines. Furthermore, the model with 11 input variables outperforms the model with fewer input variables. The effectiveness and superiority of the identification model have been demonstrated.

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Recurrent Neural Network for Gene Regulation Network Construction on Time Series Expression Data

Cited by 6 publications

References 16 publications

Utilizing Machine Learning Techniques for Categorizing Cancer Based on Gene Expression Data: A Review

Utilizing Machine Learning Techniques for Categorizing Cancer Based on Gene Expression Data: A Review

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Research on the identification of DCT vehicle driver’s starting intention based on LSTM neural network and multi-sensor data fusion

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