HomeAuthor InterviewsInterview with Upendra Kumar Devisetty

Interview with Upendra Kumar Devisetty

Upendra Kumar Devisetty is the author of Deep Learning for Genomics; we got the chance to sit down and find out more about his experience of writing with Packt.

Q: What are your specialist tech areas?

Upendra: Bioinformatics, Genomics, Machine Learning, Deep Learning, Distributed Computing.

Q: How did you become an author for Packt? Tell us about your journey. What was your motivation for writing this book?

Upendra: My journey with Packt began when I was invited to review their book “Machine Learning in Biotechnology and Life Sciences.” When I saw the invitation, I had no hesitation in accepting it due to my interest and background in machine learning in the biotechnology space. I was inspired by the author’s work in the book and reviewing it made me wonder how amazing it would be if I could author a book myself. After the review, I made up my mind to write a book in an area in which I was proficient. In addition to taking inspiration from the author of the book, my motivation to write also partly comes from my interest in teaching. I have taught many workshops on Bioinformatics, big data, and Containerization, and I developed a course on Big Data Fundamentals on DataCamp which was taken by more than 10,000 students. I am a firm believer that concepts are better understood if one can explain them in a way that the readers find useful. This thought led me to explore the opportunity to become an author with Packt. Initially, I expressed interest in authoring a book combining PySpark, a distributed computing framework, and genomics, but after much brainstorming, we decided on “Deep Learning for Genomics” as the topic and title. It turned out to be a fitting topic and title due to my passion for deep learning and background in genomics.

My motivation for writing this book mainly also comes from the fact that there is a lack of proper sources of knowledge on deep learning in genomics. Deep learning for genomics applications is already revolutionizing many domains such as healthcare, agriculture, clinical, pharmaceutical, and so on. While there are many resources available for understanding deep learning, there are very few resources that provide a proper understanding of deep learning applications for genomics to solve real-world problems. The intersection of deep learning and genomics is little explored through books because of how technical and complex the concepts are, and it can be quite intimidating for beginners to enter this field. Through the book, my goal is to provide a single source of information that every genomic researcher, data scientist, or any research scientist can rely on to understand the basic concepts of deep learning and how it can be leveraged for genomics applications in life sciences and biotechnology industries. The book is meant to provide a one-stop solution for applying deep learning techniques to address some of the most challenging problems in genomics.

Q: What kind of research did you do, and how long did you spend researching before beginning the book?

Upendra: To be honest, it didn’t take long for me to start writing the book because I was very clear from the beginning about what I wanted to cover. As a Bioinformatics Data Scientist with a background in deep learning and training in genomics, I didn’t have to spend much time coming up with an outline for the book and getting started with the process. The Packt team told me that my outline for the book was well-structured and properly covered all relevant topics in the very first draft, which was enough to validate that I was going in the right direction. Of course, I did a little research to see if there were any similar books available, but unfortunately, there were none. This made it easy for me to use my thought process for the book. As soon as the outline was approved, I started writing the chapters without spending much time on research. Overall, I spent less than two weeks on research before beginning the book. Most of the time was spent writing the book, reviewing it, and creating real-world case studies.

Q: Did you face any challenges during the writing process? How did you overcome them?

Upendra: I would be lying if I said the journey was smooth. During the entire process, I faced many hurdles. Balancing a full-time job and writing a book was extremely challenging, especially toward the end when the turnaround time was very short. Most of what I wanted to cover in the book is very technical, so I had to spend a lot of time breaking down the concepts and making them easy for potential readers to understand. The field of deep learning is itself very complicated and coupled with the fact that I was trying to integrate it with genomics made things even harder. In summary, I had to break down the concepts not just for one field, but for two in this case. Another challenge was coming up with genomics Case Studies that were as close to real-world problems as possible, and leveraging deep learning to address them was also a formidable task. I overcame all these challenges by sticking to a routine for chapter writing. I dedicated 2-3 hours every workday and 8-12 hours during weekends, and of course, holidays were a bonus for me. This allowed me to make incremental progress every day without worrying about spending the whole day writing. Working every day for 2-3 hours may not seem like a lot, but you will be amazed at how much writing can be done during that time. When I wasn’t writing, I used to read a lot of research papers and see how researchers are currently using deep learning in the genomics field in various domains such as healthcare, agriculture, pharmaceuticals, and so on. Furthermore, as soon as I delivered a chapter, I didn’t wait for the feedback but instead started the next chapter, which allowed me to continue the momentum. I could go on and on, but I would say the main takeaways are incremental progress, focused writing, and a clear thought process.

Q: What’s your take on the technologies discussed in the book? Where do you see these technologies heading in the future?

Upendra: The main technology discussed in the book is deep learning. It is well known in the industry that deep learning is revolutionizing every field it is applied to, whether it is life science, biotechnology, genomics, and so on. However, the adoption of deep learning in different fields varies from one field to another. For example, deep learning as applied to fields such as computer vision and natural language processing has taken off significantly compared to genomics. Genomics has scientifically proven its abilities in the prevention, management, and treatment of diseases. Healthcare is the main beneficiary of genomics, and because of the incredible progress in genomics, the healthcare industry is gradually shifting from conventional treatment methods towards precision medicine. The field of genomics is one of the fastest-growing markets in the world right now and is projected to grow to around 100 billion USD by 2028. The global impact of COVID-19 has contributed significantly to this staggering growth, and because of that, the genomics market is experiencing positive demand from all sectors. The recent advances in high-throughput sequencing technologies that generate large-scale data, improvements in algorithms, and the development of next-generation hardware have all contributed to the adoption of deep learning in genomics. I would imagine that deep learning would be more routinely applied to genomics in life sciences and biotechnology because of these reasons, and I look forward to a future where this intersection will be talked about at the same level as deep learning for computer vision or natural language processing.

Q: Why should readers choose this book over others already on the market? How would you differentiate your book from its competition?

Upendra: The simple answer is that there is no other book out there that combines deep learning with genomics. While there are many resources available for understanding deep learning, there are very few resources that provide a proper understanding of deep learning applications for genomics to solve real-world problems. The intersection of deep learning and genomics has not been explored in life sciences and biotechnology through books. A keyword search of “deep learning” and “genomics” on Google returns zero books, indicating the gap in the market for this book. The closest I can think of is the machine learning books that were written to address biological problems in life sciences and biotechnology but do not specifically address genomics problems with deep learning. This book differentiates itself from other books in many ways, as it deals with the intersection of deep learning and genomics, two of the most popular subjects. It provides a clear conceptualization of topics that are proven to be important for addressing problems in genomics, including real-world use cases that are routinely used in industries, and includes topics that help get started with deep learning for genomics.

Q: What are the key takeaways you want readers to come away with from the book?

Upendra: The main takeaway is that deep learning is very powerful, easy to implement, and enables one to extract insights from big genomics data. Deep learning is currently being used for genomics applications in both companies and academia successfully. Unlike state-of-the-art technology such as bioinformatics for genomics, which relies on rules, readers will use deep learning algorithms introduced in this book for some practical applications of genomics in the life sciences and biotechnology industries to transform raw genomics data into valuable knowledge. Going forward, the field of genomics is ready to adopt this new revolution, which is deep learning.

Q. What advice would you give to readers learning tech? Do you have any top tips?

Upendra: As with any other technology, the learning curve for deep learning will be steep, so it is important to acknowledge that and strive to learn it with incremental steps. One cannot become an expert in any technology overnight; it requires constant practice, getting better at concepts, reading research papers/blogs, subscribing to tech newsletters, following experts in the field, and so on. Finding a project and using that project to learn new technology is the most widely accepted process for learning new technology. Among all the resources, finding a good book that teaches technology is one of the best ways to learn new technology. For example, if you want to get better at cloud computing, then finding a good book from a reputable publisher and author that teaches cloud computing is a better way to learn than googling for relevant materials on cloud computing. The online material is flooded with lots of information, but reading a book allows you to get the most relevant information that you might otherwise find by searching the internet.

Q. Do you have a blog that readers can follow?

Upendra: I am not an active blogger, but authoring this book helped me understand the importance of writing and how it helps to better understand concepts. Therefore, I decided to spend more time writing and started a blog on my website. Readers can check out this link for more information about it: https://upendrak.github.io.

Q: Can you share any blogs, websites and forums to help readers gain a holistic view of the tech they are learning?

Upendra: I refer to lots of peer-reviewed research papers to understand a concept (for example, convolutional neural networks in genomics) because they are peer-reviewed and have gone through a rigorous reviewing process, so we can trust them. I follow experts in machine learning, deep learning, genomics, and bioinformatics on Twitter and LinkedIn, and I read tutorials and blogs on Medium, Towards Data Science, and other websites. In summary, no one resource gives you a holistic view of the technology you are trying to learn, but a combination of all of these will get you there eventually.

Q. Do you belong to any tech community groups?

Upendra: I am Carpentries and DataCamp instructor in Data Science.

Q. How would you describe your author’s journey with Packt? Would you recommend Packt to aspiring authors?

Upendra: As I mentioned earlier, my author journey with Packt was overall very satisfying, even though it was tough towards the end when I was trying to wrap things up. One thing for sure is that the Packt team will give you all the guidance, help, and direction that you need, and they will work with you throughout the whole journey, so you don’t feel alone. That said, book writing is not trivial, as it takes so much out of you, and many times you feel frustrated, dejected, and disappointed. But once you overcome those phases with some positivity, it is rewarding. I have yet to reap the rewards since the book is not out yet, but I am hoping that this will help me achieve my future goal of advancing genomics toward the adoption of deep learning. I would recommend Packt to aspiring authors, but with the caveats that I mentioned above. However, if you are interested in authoring and have a clear idea of what you want to teach readers, then I would highly encourage aspiring authors to write a book.

Q. What are your favorite tech journals? How do you keep yourself up to date on tech?

Upendra: Since my background is not in tech, I don’t read a lot of tech journals, but since biotechnology and deep learning have a technology component, I read a lot of articles, blogs, and tutorials on Medium and other blogging sites. I am an active user on social media platforms such as Twitter and LinkedIn.

Q. How did you organize, plan, and prioritize your work and write the book?

Upendra: As I mentioned earlier, the key to writing the book is planning. Before I started the book, I made it a point not to be intimidated by the number of pages I had to write, but instead to focus on incremental progress. I had a routine schedule for writing the chapters. Every workday I spent 2-3 hours, and over the weekend I worked longer so that I could make significant progress. Before I submitted the chapter for review to the Packt team, I normally made it a point to read it 2-3 times so that I could fix the issues myself, rather than relying on the Packt team to do it.

Q. What is that one writing tip that you found most crucial and would like to share with aspiring authors?

Upendra: For aspiring authors, the one tip I want to give is to do your homework during project scheduling and ask for enough time to make sure that the chapters are delivered promptly as scheduled.

Q. Would you like to share your social handles? If so, please share.

Upendra: upendra_35 (Twitter); https://www.linkedin.com/in/upendradevisetty/

You can find Upendra’s book on Amazon by following this link: Please click here

Deep Learning for Genomics is Available on Amazon.com