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Register a new userTen Quick Tips for Deep Learning in Biology
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Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscripts writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others.
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Much of biology (and, indeed, all of science) is becoming increasingly computational. We tend to think of this in regards to algorithmic approaches and software tools, as well as increased computing power. There has also been a shift towards slicker, packaged solutions--which mirrors everyday life, from smart phones to smart homes. As a result, its all too easy to be detached from the fundamental elements that power these changes, and to see solutions as black boxes. The major goal of this piece is to use the example of the Raspberry Pi--a small, general-purpose computer--as the central component in a highly developed ecosystem that brings together elements like external hardware, sensors and controllers, state-of-the-art programming practices, and basic electronics and physics, all in an approachable and useful way. External devices and inputs are easily connected to the Pi, and it can, in turn, control attached devices very simply. So whether you want to use it to manage laboratory equipment, sample the environment, teach bioinformatics, control your home security or make a model lunar lander, its all built from the same basic principles. To quote Richard Feynman, What I cannot create, I do not understand.
The Virtual Institute for Integrative Biology (VIIB) is a Latin American initiative for achieving global collaborative e-Science in the areas of bioinformatics, genome biology, systems biology, metagenomics, medical applications and nanobiotechnolgy. The scientific agenda of VIIB includes: construction of databases for comparative genomics, the AlterORF database for alternate open reading frames discovery in genomes, bioinformatics services and protein simulations for biotechnological and medical applications. Human resource development has been promoted through co-sponsored students and shared teaching and seminars via video conferencing. E-Science challenges include: interoperability and connectivity concerns, high performance computing limitations, and the development of customized computational frameworks and flexible workflows to efficiently exploit shared resources without causing impediments to the user. Outreach programs include training workshops and classes for high school teachers and students and the new Adopt-a-Gene initiative. The VIIB has proved an effective way for small teams to transcend the critical mass problem, to overcome geographic limitations, to harness the power of large scale, collaborative science and improve the visibility of Latin American science It may provide a useful paradigm for developing further e-Science initiatives in Latin America and other emerging regions.
Scientific objectivity was not a problem in the early days of molecular biology. However, relativism seems to have invaded some areas of the field, damaging the objectivity of its analyses. This review reports on the status of this issue by investigating a number of cases.
This is an article submitted to the Ten Simple Rules series of professional development articles published by PLOS Computational Biology.
Abnormality detection is essential to the performance of safety-critical and latency-constrained systems. However, as systems are becoming increasingly complicated with a large quantity of heterogeneous data, conventional statistical change point detection methods are becoming less effective and efficient. Although Deep Learning (DL) and Deep Neural Networks (DNNs) are increasingly employed to handle heterogeneous data, they still lack theoretic assurable performance and explainability. This paper integrates zero-bias DNN and Quickest Event Detection algorithms to provide a holistic framework for quick and reliable detection of both abnormalities and time-dependent abnormal events in the Internet of Things (IoT). We first use the zero-bias dense layer to increase the explainability of DNN. We provide a solution to convert zero-bias DNN classifiers into performance assured binary abnormality detectors. Using the converted abnormality detector, we then present a sequential quickest detection scheme that provides the theoretically assured lowest abnormal event detection delay under false alarm constraints. Finally, we demonstrate the effectiveness of the framework using both massive signal records from real-world aviation communication systems and simulated data. Code and data of our work is available at url{https://github.com/pcwhy/AbnormalityDetectionInZbDNN}
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