Topic modeling is an Information Retrieval (IR) technique that discovers representative topics from a collection of documents. Thus, we expect that logically related words will co-exist in the same document more frequently than words from different topics. For example, in a document about the space, it is more possibly to find words such as: planet, satellite, universe, galaxy, and asteroid. Whereas, in a document about the wildlife, it is more likely to find words such as: ecosystem, species, animal, and plant, landscape. But why text classification is so useful? In this blog post, we try to explain the importance of topic modeling and its use in software engineering.
I am very pleased to introduce the June issue for XRDS on computational biology. I had the privilege to work as Issue Editor for this issue alongside Guest Editor Cristina Pop, who recently received her Ph.D. from Stanford University.
Computational biology is ubiquitous. Every modern bioscience lab relies on computational biology and bioinformatics techniques to some extend, whether for gene and protein sequencing or data storage. Moreover, advances in computational biology techniques allow researchers to gain deeper insights into biological mechanisms, simplify lab-bench methods, and develop more reliable and sophisticated methods for diagnosis and clinical applications. Computational biology drives biological research and even bounds the types of questions that researchers and clinicians can ask. This is why it is such an exciting and rapidly growing area of computer science. Continue reading