Evaluation of classification and forecasting methods on time series gene expression data

Tripto, Nafis Irtiza; Kabir, M. R.; Bayzid, Md. Shamsuzzoha; Rahman, Atif

doi:10.1371/journal.pone.0241686

Cited by 6 publications

(6 citation statements)

References 45 publications

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“…Tripto et al . [ 114 ] evaluated Holt–Winters, ARIMA, LSTM, Artificial Neural Network (ANN) and GluonTS feedforward neural networks for forecasting time series in five sets of temporal gene expression profile data of different sizes, and found that ARIMA and ANN worked better.…”

Section: Proposed Research Directions and Methodsmentioning

confidence: 99%

Integrating bioinformatic strategies in spatial life science research

Hao

Liang

Liu

et al. 2022

Briefings in Bioinformatics

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As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools.

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Section: Proposed Research Directions and Methodsmentioning

confidence: 99%

Integrating bioinformatic strategies in spatial life science research

Hao

Liang

Liu

et al. 2022

Briefings in Bioinformatics

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“…Machine algorithms and in specific, neural networks, are emerging as state-of-the-art approaches in time series forecasting and classification of complex systems, including gene expression dynamics. 166 Once again, none have been applied in cancer research due to the lack of cancer time series datasets. In principle, the neural networks discussed below can and should be utilized to study attractor dynamics from time series cancer datasets.…”

Section: Causality Inference Via Machine Intelligencementioning

confidence: 99%

“…While autoregression models are better suited for short-term time series forecasting in low-dimensional systems, deep learning algorithms are the state-of-the-art approaches for time series gene expression forecasting in higher-dimensional systems. 166 Furthermore, neural network algorithms used in turbulence forecasting may be of great interest in the application to cancer dynamics prediction. If the AI can predict causal patterns, such as strange attractors in turbulent flows, then it should (in principle) within cancer datasets as well.…”

Section: Causality Inference Via Machine Intelligencementioning

confidence: 99%

“… 176 , 177 CNN-based methods may be better suited for the classification of time series gene expression dynamics, while RNN algorithms, such as RC, are best for time series forecasting/attractor reconstruction. 166 RC is computationally cheaper to train in the case of full-state information acquisition whereas, gated architectures are better for reduced order states/observables.…”

Section: Causality Inference Via Machine Intelligencementioning

confidence: 99%

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A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Uthamacumaran

2021

Patterns

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Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted. ll

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“…To validate our proposed method, we used the E. coli dataset with the same topological features used in S. Cerevisiae and seven different time series of the gene expression as biological features. The time series of the gene expression is the expression of the gene at different time points [46]. Many existing methods used this gene expression data for different purposes of classification [11,28,34].…”

Section: Introductionmentioning

confidence: 99%

Efficiency due to data balancing in the prediction of essential proteins

Hossain

Islam

2023

Preprint

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Essential proteins are important for the survival of the organism. Those play a vital role to prepare antibiotics, disease diagnosis, understanding the organism structure, etc. That is why essential proteins now become one of the hot topics in current research. In recent times, many methods were proposed to identify the essential proteins. But the identification rate of the essential proteins is still low. Therefore, an efficient approach is required to identify the essential proteins with high performance. In this paper, a machine learning-based efficient method is proposed to identify the essential proteins. To identify the essential proteins, both balanced and imbalanced datasets are used here. For data balancing, different techniques are used to observe the efficiency of the methods. Both topological and biological features are used in this method. The S. Cerevisiae dataset is used to evaluate the proposed method. Another dataset of species E. coli is used to validate the performance of this method. Three classifiers such as Random Forest, LightGBM, XGBoost, and an ensemble method of these three classifiers are used to predict the essential proteins. The best performance is achieved from the balanced dataset from the SMOT+ENN and Random Forest classifier. The results are balanced both in accuracy and F1-score which is also best compared to the existing related methods.

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Evaluation of classification and forecasting methods on time series gene expression data

Cited by 6 publications

References 45 publications

Integrating bioinformatic strategies in spatial life science research

Integrating bioinformatic strategies in spatial life science research

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Efficiency due to data balancing in the prediction of essential proteins

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