Tons of machine learning models, algorithms and complicated methods are being developed in academic level of bioinformatics. Every phd student working in bioinformatics is expected to develop a novel method to do some task. Some prefer using SVM, some prefer Bayesian networks and some neural network. Some try to develop new kernels that could have various applications in biology, and so on. They try to prove higher and higher accuracy of their models over others. I want to know how many of these tons and million models and methods developed are being used by other people in bioinformatics community? Are they simple enough to be easily understood and communicated by biologists? And top of it no research team want to use what already have been developed. They prefer to hire their own local programmers to build their in-house tools and methods doing the same thing. And here is one big reason why many bioinformatics companies are not able to do good business in selling their software and tools.
What is important is first to understand “What is bioinformatics?”and “What is the main goal of doing bioinformatics?”. I believe that the study and approach in bioinformatics is “Problem-driven” instead of “Solution-driven”. One should try to prove the quality and novelty of findings instead of the efficiency or accuracy of method applied. To be good bioinformatician, I would first start with understanding the biological question, problem and its hypothesis and then look for methods which can be used. This approach definitely needs interdisciplinary training to bioinformatics scientist. On the contrary, the interdisciplinary trained scientist are good for solving problems, but they might not be very good at implementing solutions which require more than interdisciplinary approach or some particular expertise skill.
Indeed multidisciplinary team has become a trend over interdisciplinary integration. Most of the pharmaceutical and bioinformatics companies are recruiting computer scientist and biologist rather than recruiting interdisciplinary bioinformaticians. I suspect this to be the approach where communication gap or linguistic difference arises within two groups trying to solve the same problem. Although, we have already seen experts in both fields to turn into interdisciplinary to take this field further so far, I still believe there is some gap where interdisciplinary experts and training like mine can prove a big asset.