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From the first genome sequencing in the second revolution to Life Science Analytics as a growing field in the fourth industrial revolution, change has been both welcomed and fraught with fear. Everyone worries about robots, Artificial Intelligence, and in some cases even professionals who have stayed current by keeping up-to-date with trends. And it’s beginning to affect not only “office politics” within the tech space, but even interviewer and interviewee relationships. We’ve seen a growing trend of apprehension between Computational Biologists and Machine Learning Engineers. What could be the cause? Aren’t they each working toward a common goal? It seems the answer isn’t quite so cut and dry as we’d like it to be. Here are some thoughts on what could be driving this animosity. But first, a bit of background. So, What’s the Difference? Computational Biology and Machine Learning are two sides of the same coin; one sets the framework and the other applies what’s been learned. Both use statistical and computational methods to construct models from existing databases to create new Data. However, it is within the framework of biomedical problems as computational problems, that there seems to be a bit of a breakdown. It’s one thing to have all the information and all the Data, but its quite another to know how the Data might interact or affect the health and medications of people seeking help. This is the job of those in Life Science Analytics. Determine through Data what needs to be done, quickly, and efficiently, but at the same time, ensure the human element is still active. A few examples of Computational Biology include concentrations, sequences, images and are used in such areas as Algorithmics, Robotics, and Machine Learning. The job of Machine Learning can help to classify spam emails, recognize human speech, and more. Here’s a good place to start if you’d like to take a deeper dive into the differences between the two or read this article about mindsets and misconceptions. Office Politics in the Tech Space Circling back to the concern between Computational Biologists and Data Scientists with a focus on Machine Learning. The latest around the water cooler within the tech space is that those with a biological background who understand Machine Learning are looked upon as dangerous to the status quo. But, as many of our candidates know, it’s important to stay on the cutting edge and if that means, upskilling in Machine Learning so you have both the human element as well as the mathematical, robotic components, then that is more marketable than just having one or the other. The learning curve in biology training within the Life Sciences Analytics space means Computational Biologist with a Machine Learning skillset is best able to apply Data Science and computer science tools to more organic and biological datasets. Someone with just a computer science background may not have the depth of knowledge to understand how these models, systems, and data affect and impact medicine. Computational Biologists who are trained simultaneously in computer science and biology, and are a little heavier on the biology side, see Machine Learning Engineers as a threat because utilizing Machine Learning and other cutting-edge tools could mean their job is on the line. They worry their job will fall by the wayside. That when somebody proves Machine Learning is faster and more efficient the impetus might be why hire a Computational Biologist when a Machine Learning engineer will do? It’s like when a lot of people joke about how robots are going to take over the world and everybody will be out of a job. I think the worry with some folks on the Computational Biology side is that maybe they just aren’t up to date with their training or haven’t kept up with cutting edge of technology. With a Recruiter’s Eye While what I’ve seen agrees that, yes, Machine Learning is incredibly helpful and fast and you can get through so much more data. But its still that understanding of biology and chemistry that you will need because you need to be able to understand, for example, how these proteins are going to be reacting with one another or you need to understand how DNA and RNA work, how best to analyze, and what analyzing those things means. On the other hand, just because you know, “oh, this reaction comes out of it”, if you don’t know why that is or how that could impact a drug or a person, then you don’t really have anything to go on. There’s a caveat there. Though there may be concerns among Computational Biologists and Machine Learning Engineers, at both the upper and entry levels, it’s still the technical lead who will say, “we really do need somebody with a biological background because if we get all this Data and don’t really know what to do with it, then we’ll need to hire a Project Manager to converse between the two and that’s an inefficient use of time and resources”. What I hear most often is a company wants a Computational Biologist but they also want someone who knows Machine Learning. But they don’t want to compromise on either because they don’t understand there are limitations to things. We all want the unicorn employee, but we can’t make them fit into a box with too specific parameters. It’s a Fact of Life Any job, whether it’s in the tech industry, the food industry, Ad Optimization, or even recruitment, uses Machine Learning in one way or another. Yet compared to spaces which work on sequencing the human genome, it's amazing to see how far things have come. It used to take days to process DNA. Now you can spit in a tube and send it off to 23andMe to learn a little about your health. That’s what Machine Learning enables people to do. But it doesn’t mean Computational Biologists are going to fall by the wayside. It means there will be times you’ll have to liaise more between the two groups. It means you’ll be more marketable by adding Machine Learning to the work you’re already doing or taking some classes in Computational Science, for example, to keep your skills up to date. It’s a Transparency Issue Ultimately, it seems the heart of this apprehension comes down to a transparency issue. For example, let’s say companies begin to bring in AI people and suddenly the staff already in place begins to get worried about the security of their jobs. Even in an industry tense with skills gaps, the fear still abounds. In coming back to speak with the Hiring Manager, it became clear the animosity is even more prevalent than first imagined. So, it’s important to get input from within the company and develop a unified story, a unified message across departments, and especially within the Life Science Analytics and Data Science teams as well. In other words, “keep people in the loop.” If it’s happening to this company, it seems other companies may be facing this same issue. However, it’s not going away and is creating a kind of competition between the old guard and the up-and-coming startups. For example, any new company is going to want to integrate AI and will be asking the question how best to integrate it into their structure. They might also ask how best to optimize the ads coming through AI. This is just another way of how companies are catching up, but also how people are catching up to the companies. Technology is coming whether you like it or not. So, if you want to stay marketable and work on really interesting projects, there’s always going to be the challenge of staying up-to-date and different companies attack this in different ways. Stay open minded, keep an eye and an ear out for ways to stay on top of your game. Even just taking a few minutes to watch a YouTube video, listen to a TedTalk or a podcast, so you can talk about it and be informed. These are some really simple ways to stay on the cutting edge and help you figure out where you can grow and improve for better opportunities. Ready for the next step? Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
04. July 2019
Boston, Massachusetts is once again on the cutting edge of medical research and technology. From Electronic Health Records (EHR) to Machine Learning and predictive modeling of healthcare best practices to Computational Biology; the final frontier of genetic editing. We have come a long way in our quest to understand and improve our quality of life. In the face of cancer research, diabetes, and liver or heart failure, the world of Computational Biology opens the scientific doors to discovery and solution. This is a place for scientists to not only get to the heart of the matter, but to the core of the problem at the cellular level. There is an old adage which states, “when pigs fly”, usually meaning some thing will never happen or is impossible. But what happens when the impossible becomes possible? The jury’s still out, but researchers are making great inroads in developing ways to save human lives using animal organs. Could Animal Organs Help Solve Donor Deficiency? There are over 100,000 patients in the U.S. waiting for a transplant operation and, for many, a this may be their only cure. Yet, our growing population and the sheer number of those waiting has created a donor deficiency of epic proportions. Researchers have been working toward successfully transplanting organs from animals into humans. Not only has their study of stem cell technology grown over the years, but with the advent of bioinformatics, statistics, and Computational Biology, a new possibility has arisen. The chance to not only transplant organs from one species to another, but using another species to host the growing of transplantable human tissue. Getting the Framework Right Computational Biology is a broad discipline honed to a fine point. Using statistical modelling, it builds a wide variety of experimental Data and biological systems to understand algorithmics, Machine Learning, automation, and robotics. Its job is to ask and answer the question of how to efficiently gather, collate, annotate, search for information. But how can it do all this to determine appropriate biological measurements and observations? At the tipping point is the notion that to truly get a good picture of the problem, the frame must be in focus. And it is this, which is the most important task for Computational Biologists to solve before continuing their research. It’s a reminder to step back and look at the problem from another angle and to challenge assumptions turning “what if” on its head. Stretching, bending, and twisting toward a solution that might not otherwise have been thought without a framework in place in order to begin modelling the system. It is in this constant learning phase, Machine Learning applications with parameters set by the biologists, in which new information is processed, analyzed, and understood. This active learning model offers opportunities for applications to learn how to learn and will play a critical role in biomedical research now and in the future. And from this place, the second biggest problem to be solved enters the equation. Now, it’s time to refine the methods of how to solve the problem. Next Steps As exciting as the possibilities are, like all things new, there are challenges. For example, not all animals will fit the bill for transplantation. The idea is to mimic as closely as possible the size and evolution of humans such as pig, sheep, or non-human primates. But, at an even finer point of challenge are our own cell’s reactions and expressions and understanding why they act the way they do. Ultimately, it’s important to be sure information at the individual cell level is inferred with statistical references to verify findings. At the pixel level, not using a fine-tooth comb could mean your conclusions are wrong. If you’re interested in Biostatistics, Bioinformatics, Computational Biology, Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our latest Computational Biology opportunities in our new Life Science Analytics specialism or our current vacancies for additional opportunities. Contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
13. February 2019