Gujral Harshit, Kushwaha Ajay Kumar, Khurana Sukant
Department of Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, India.
CSIR-Central Drug Research Institute, Lucknow, Uttar Pradesh, India.
Neurosci Insights. 2020 Dec 8;15:2633105520963045. doi: 10.1177/2633105520963045. eCollection 2020.
Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals', other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.
时间序列工具是现代研究的重要组成部分。它们在生物医学领域，特别是神经科学中的应用，此前尚未得到量化。对趋势的量化可以揭示当前使用中的不足，并指明未来的用途。我们评估了一些经典时间序列工具的原理和应用，如主成分分析、神经网络、常见的自回归模型、马尔可夫模型、隐马尔可夫模型、傅里叶分析、频谱分析，此外还有各种通常归类为时间序列类别的工作。我们从两个角度对其使用情况进行了量化，一个是信息技术专业人员的角度，另一个是将这些工具用于生物医学和神经科学研究的研究人员的角度。为了从信息技术角度了解趋势，我们评估了Stack Overflow和Cross Validated这两个最大的开源问答数据库。我们通过在PubMed上搜索文献和应用使用情况，量化了它们在生物医学领域，特别是神经科学领域的应用趋势。虽然所有时间序列工具在一般生物医学和生命科学研究以及神经科学中的使用都在持续普及，Stack Overflow和Cross Validated上提出的问题总数也是如此，但近年来这些问题的总浏览量在下降，这表明文本、算法和库已经成熟，导致工程师不再寻找几年前常见的问题。这些工具在神经科学中的使用显然还有改进的空间。
