Machine learning validation guidelines in biology
Abstract
Machine learning is often used in modern biology to give predictions and enhance decision-making processes. There has recently been a request for more investigation of machine learning performance and its limitations. We provide a collection of community-wide suggestions aimed at establishing machine learning validation standards in biology. Implementing a structured methods definition for machine learning based on DOME (data, optimization, model, evaluation) can help both reviewers and readers better understand and analyse a method's or outcome's performance and limits.
References
Singu, S., & Tunguturi, M. (2015). Fundamentals and awareness of robotics. Transactions on Latest Trends in Health Sector, 7(7). Retrieved from https://www.ijsdcs.com/index.php/TLHS/article/view/191
Tunguturi, M. (2017). Extremely Low strength design for Water Purification. Transactions on Latest Trends in Health Sector, 9(9). Retrieved from https://www.ijsdcs.com/index.php/TLHS/article/view/190
Tunguturi, M., & Singu, S. (2012). The growth of Bigdata in Information Technology. Transactions on Latest Trends in Health Sector, 4(4). Retrieved from https://www.ijsdcs.com/index.php/TLHS/article/view/189
Tunguturi, M. (2010). Artificial intelligence and machine learning in the enterprise. Transactions on Latest Trends in Health Sector, 2(2). Retrieved from https://www.ijsdcs.com/index.php/TLHS/article/view/187
Tunguturi, M. (2018). Avoid Road Accident Using IoT. Transactions on Latest Trends in IoT, 1(1), 31-40. Retrieved from https://www.ijsdcs.com/index.php/TLIoT/article/view/167
Singu, S. (2018). Blockchain based answer for comfortable Audit logs. Transactions on Latest Trends in IoT, 1(1), 21-30. Retrieved from https://www.ijsdcs.com/index.php/TLIoT/article/view/166
Tunguturi, M., & Singu, S. (2016). Automation of human behaviors and its prediction . International Journal of Statistical Computation and Simulation, 8(1). Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/53
Singu, S., & Tunguturi, M. (2014). More on Neural community and fuzzy device. International Journal of Statistical Computation and Simulation, 6(1), 1–10. Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/50
Singu, S., & Tunguturi, M. (2013). Fundamental standards and uses of big data analytics. International Journal of Statistical Computation and Simulation, 5(1). Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/47
Tunguturi, M. (2018). American & Indian Comics on Instagram using Machine learning. International Journal of Statistical Computation and Simulation, 10(1). Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/60
Tunguturi, M. (2011). More on Big data to the world. International Journal of Statistical Computation and Simulation, 3(1), 1–10. Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/45
Tunguturi, M. (2009). More On Principles and Applications of Big Data Analytics. International Journal of Statistical Computation and Simulation, 1(1), 1–10. Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/43
Tunguturi, M., & Singu, S. (2015). Latest machine learning applications across the globe. Transaction on Recent Devlopment in Industrial IoT, 7(7). Retrieved from https://journals.threws.com/index.php/TRDAIoT/article/view/64
Tunguturi, M., & Singu, S. (2014). Latest machine learning applications across the globe. Transaction on Recent Devlopment in Industrial IoT, 6(6). Retrieved from https://journals.threws.com/index.php/TRDAIoT/article/view/63
Singu, S., & Tunguturi, M. (2016). Smart agriculture utility the use of fog-iot. Transaction on Recent Devlopment in Artificial Intellgence and Machine Learning, 8(8). Retrieved from https://journals.threws.com/index.php/TRDAIML/article/view/67
Singu, S. (2019). A new method on provider Description support Customization . Transaction on Recent Devlopment in Artificial Intellgence and Machine Learning, 11(11). Retrieved from https://journals.threws.com/index.php/TRDAIML/article/view/68
Tunguturi, M. (2019). Comparative Analysis of Balancing Techniques in Cloud Computing. International Journal of Managment Education for Sustainable Development, 2(2), 41-50. Retrieved from https://www.ijsdcs.com/index.php/IJMESD/article/view/171
Singu, S. (2019). Trends of Text Mining Techniques used in Social Media Websites. International Journal of Managment Education for Sustainable Development, 2(2), 51-60. Retrieved from https://www.ijsdcs.com/index.php/IJMESD/article/view/172
Singu, S. (2017). A robust technique for image processing. Transactions on Latest Trends in Health Sector, 9(9). Retrieved from https://www.ijsdcs.com/index.php/TLHS/article/view/193
Singu, S., & Tunguturi, M. (2017). Protect Personal Privacy And Wasting Time Using AI and ML. Transaction on Recent Devlopment in Artificial Intellgence and Machine Learning, 9(9). Retrieved from https://journals.threws.com/index.php/TRDAIML/article/view/72
Whig, P., & Ahmad, S. N. (2012a). A CMOS integrated CC-ISFET device for water quality monitoring. International Journal of Computer Science Issues, 9(4), 1694–1814.
Whig, P., & Ahmad, S. N. (2012f). Performance analysis of various readout circuits for monitoring quality of water using analog integrated circuits. International Journal of Intelligent Systems and Applications, 4(11), 103.
Whig, P., & Ahmad, S. N. (2013a). A novel pseudo-PMOS integrated ISFET device for water quality monitoring. Active and Passive Electronic Components, 2013.
Whig, P., & Ahmad, S. N. (2014a). Development of economical ASIC for PCS for water quality monitoring. Journal of Circuits, Systems and Computers, 23(06), 1450079.
Whig, P., & Ahmad, S. N. (2014c). Simulation of linear dynamic macro model of photo catalytic sensor in SPICE. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering.