Effect of seasonal variation in ambient temperature on RNA quality of breast cancer tissue in a remote biobank setting

Fouda, Merhan A.; Elzefzafy, Nahla; Taha, Iman I.; Mohemmed, Omar M.; Wahab, Abdelhady Ali Abdel; Farahat, Iman Gouda

doi:10.1016/j.yexmp.2019.104334

Cited by 2 publications

(1 citation statement)

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“…Whether most transcripts decay at a similar rate remains unknown at present [ 2 ]. Understanding RNA degradation patterns is critical for studies of samples collected in the field or samples collected in clinical settings, as these tissue samples are often not immediately stored under conditions that can prevent RNA degradation [ 3 ]. For example, obtaining donated samples in the clinic may require several hours of transport for RNA extraction and sequencing analysis.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%