Bioinformatics /ˌbaɪ.oʊˌɪnfərˈmætɪks/ ( listen) is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines Computer Science, Biology, Mathematics, and Engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques.
Bioinformatics is both an umbrella term for the body of biological studies that use computer programming as part of their methodology, as well as a reference to specific analysis “pipelines” that are repeatedly used, particularly in the field of genomics. Common uses of bioinformatics include the identification of candidate genes and single nucleotide polymorphisms (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences, called proteomics.
Source: Bioinformatics – Wikiwand
As a little boy in Oxford, I was encouraged to worship the mind. I and my friends, often sons of professors, were being drilled in French and Latin and Greek before we turned seven, and not long afterwards were to be found wrestling with Occam’s razors and Pythagorean theorems. We learned how to write with spurious fluency on every aspect of Plato or King Lear, and the less we knew, the more commandingly we could write. The mind became an instrument we could deploy as sword, shield and moat; on its own terms – and they were the only terms we were taught to honour – it was impossible to defeat.
We introduce a new method for training neural networks which allows an experimenter to quickly choose the best set of hyperparameters and model for the task. This technique – known as Population Based Training – trains and optimises a pool of networks at the same time, allowing the optimal set-up to be quickly found. Crucially, this adds no computational overhead, can be done as quickly as traditional techniques and is easy to integrate into existing machine learning pipelines.