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• W4313400501 abstract "Deep learning (DL) is an actively growing domain of machine learning owing to the proliferation of big data in multifaceted fields of study. Widespread in literature, there are multiple reports on the implementation of long short-term memory network (LSTM)-based models in the field of bioinformatics for the analysis of the vast amount of genomic data. Genomic data is quintessentially a contiguous stretch of unspaced characters that correspond to the nucleotides adenine (A), guanine (G), cytosine (C), and thymine (T) known as deoxyribonucleic acid (DNA). The DNA sequence must be converted to formats readable by LSTM algorithms run on computers, a process known as digitization. This paper aims to uncover and elucidate the various techniques for digitization in studies that apply LSTM for predictive and classification problems as it relates genomics. We hope this paper would serve as a template for assessing pre-processing techniques for suitability in future studies involving deep learning." @default.
• W4313400501 created "2023-01-06" @default.
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• W4313400501 date "2023-01-01" @default.
• W4313400501 modified "2023-09-30" @default.
• W4313400501 title "Digitization Techniques for the Representation of Genomic Sequences in LSTM-Based Models" @default.
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• W4313400501 doi "https://doi.org/10.1007/978-981-19-7660-5_59" @default.
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