Instructor: Aleksi Aaltonen, Section 002

Machine Learning Forecasted to Predict Human Cell Organization

Scientists have been able to leverage technology to advance the current methods of viewing human cells. Historically, they have used a process known as fluorescent microscopy. This process does not allow a full comprehensive view of the cell, and does not provide the scientist with the full benefit of watching the proteins interact in real time. The addition of machine learning technology has large impacts throughout the health field. The algorithm can be used to view the behavior of cancerous cells, and pinpoint what goes wrong in the cells before they become diseased. This modeling technology is huge for the development of drugs, and can even help scientists to grow real organs from human cells in a lab.

Technology is proving to be very valuable to the advancement of the entire medical field. Every year, life expectancy goes up thanks to the development of new cutting edge machines that aid doctors all the way from diagnosis through treatment. Scientists are even speaking about applying this new algorithm to old data that has been gathered from cancerous cells. Over time, learning from the models will help scientists predict diseases earlier to their inception. Information systems used in this capacity do not only improve efficiency, but save lives. One day, we may see a doctors that are not traditionally trained in medicine, but more as analysts and technicians of medical technology.

2 Responses to Machine Learning Forecasted to Predict Human Cell Organization

  • I wonder if the algorithm will be accurate, and if so how will they ensure it is accurate? Nonetheless, I agree with all the benefits that can be provided with the new technology. This can also open new doors for new tech to be applied to older data and have potential new outcomes. Life expectancy can now increase even further than it is today along with new ways of diagnosing and treatment.

  • This is an excellent example of analyzing subsystems to optimize the larger system, or performing abstraction. In healthcare, practitioners diagnose a person based on the symptoms or emergent properties of the underlying illness. Machine learning enables the diagnosis of cellular subsystems before the person exhibits any symptoms. With this knowledge, healthcare professionals can prevent the transformation that takes place within the human system that leads to illness symptoms.

    Huge datasets describing individual cellular activities will be difficult to use in the typical patient-doctor setting. For that reason, I agree with your insight that it is possible that doctors will act more as analysts who prevent diseases than as practitioners who treat it.

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