论文标题
采矿生物学数据中的深度学习
Deep Learning in Mining Biological Data
论文作者
论文摘要
数据采集工具的最新技术进步使生活科学家可以从不同的生物应用领域获取多模式数据。这些数据在三种类型（即序列，图像和信号）中广泛分类，本质上是巨大且复杂的。挖掘如此大量的模式识别数据是一个巨大的挑战，需要复杂的数据密集型机器学习技术。基于人工神经网络的学习系统以其模式识别能力而闻名，最近它们的深度体系结构（称为深度学习（DL））已成功地用于解决许多复杂的模式识别问题。本文强调了DL在识别生物学数据中模式中的作用，提供了DL的应用到生物序列，图像和信号数据；这些数据的开放访问来源的概述；描述适用于这些数据的开源DL工具；并从定性和定量的角度比较这些工具。最后，它概述了采矿生物学数据的一些开放研究挑战，并提出了许多可能的未来观点。
Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Broadly categorized in three types (i.e., sequences, images, and signals), these data are huge in amount and complex in nature. Mining such an enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities and lately their deep architectures - known as deep learning (DL) - have been successfully applied to solve many complex pattern recognition problems. Highlighting the role of DL in recognizing patterns in biological data, this article provides - applications of DL to biological sequences, images, and signals data; overview of open access sources of these data; description of open source DL tools applicable on these data; and comparison of these tools from qualitative and quantitative perspectives. At the end, it outlines some open research challenges in mining biological data and puts forward a number of possible future perspectives.
