通过基于异构体表达排序的LSTM模型进行癌症诊断

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Óscar Reyes, Eduardo Pérez
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引用次数: 0

摘要

与几种类型的癌症发展有关的已知遗传因素已经大大扩大,从而易于设计和实施更好的治疗策略。然而,由于肿瘤的高度异质性和样本之间的生物学变异性,癌症的自动诊断仍然是一项复杂的任务。在这项工作中,提出了一个基于长短期记忆网络的模型,用于从转录基础数据诊断癌症。我们制定了一种有效的方法,将数据转换为基于基因/异构体表达的排名,使我们能够直接将重要信息嵌入到排名元素的相对顺序中,从而简化样本的分类。所提出的预测模型利用了深度递归神经网络的力量,能够学习描述种群中每个样本的同种异构体的个体排名的现有模式。为了评估该建议的适用性,对17个基于转录的数据集进行了广泛的实验研究,结果显示了这种新方法的有效性,并表明基于基因/同种异构体表达的排名包含有价值的信息,可以导致更有效的癌症诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performing Cancer Diagnosis via an Isoform Expression Ranking-based LSTM Model

The known set of genetic factors involved in the development of several types of cancer has considerably been expanded, thus easing to devise and implement better therapeutic strategies. The automatic diagnosis of cancer, however, remains as a complex task because of the high heterogeneity of tumors and the biological variability between samples. In this work, a long short-term memory network-based model is proposed for diagnosing cancer from transcript-base data. An efficient method that transforms data into gene/isoform expression-based rankings was formulated, allowing us to directly embed important information in the relative order of the elements of a ranking that can subsequently ease the classification of samples. The proposed predictive model leverages the power of deep recurrent neural networks, being able to learn existing patterns on the individual rankings of isoforms describing each sample of the population. To evaluate the suitability of the proposal, an extensive experimental study was conducted on 17 transcript-based datasets, and the results showed the effectiveness of this novel approach and also indicated the gene/isoforms expression-based rankings contained valuable information that can lead to a more effective cancer diagnosis.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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