深度学习在挖掘生物数据中的应用
Deep Learning in Mining Biological Data
AI总结：
本文探讨深度学习在生物数据挖掘中的应用，涵盖其在序列、图像和信号数据中的应用、数据来源、工具比较及未来研究方向。
摘要：
近年来，数据获取工具的技术进步使生命科学家能够从不同生物应用领域获取多模态数据。这些数据大致分为三种类型（即序列、图像和信号），数量庞大且性质复杂。挖掘如此庞大的数据以进行模式识别是一项巨大的挑战，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，而近年来其深度架构——即深度学习（DL）——已被成功应用于解决许多复杂的模式识别问题。本文强调深度学习在识别生物数据模式中的作用，提供了深度学习在生物序列、图像和信号数据上的应用；概述了这些数据的开放获取来源；描述了适用于这些数据的开源深度学习工具；并从定性和定量的角度比较了这些工具。最后，它概述了挖掘生物数据中的一些开放研究挑战，并提出了若干可能的未来研究方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
