Do computer scientists have a role in the Century of Biology?
Yes!
Biology has become data driven. Modern technology allows for measuring various things, very fast and in large amounts. All this data needs to be analyzed so we can make sense of it, a process which uses computers. It is more important than ever to train persons in computer science to help generate and analyse all this data, allowing us to work together to solve the biggest problems.
What is Bioinformatics?
The field of data science that works with biological datasets!
Bioinformatics is the process of working on large and complex data sets coming from biology. It relies on computers to process and analyse data, looking within to understand what is going on at a molecular level and how this affects biological function. Visualising data with graphs or diagrams and writing reports to communicate the findings are also important tasks in bioinformatics.
Omics measures what kind of information?
All genes, proteins or other molecules in a sample, are measured at the same time!
Omics refers to a collection of different data types, each generated by measuring a specific type of molecule in a sample. For example, genomics finds the sequence of DNA (or genome), while transcriptomics measures different mRNAs (or transcripts), proteomics the proteins, metabolomics the metabolites, and so on.
What problems can we solve with bioinformatics?
Examples given can benefit as they include living systems as a key part of the problem!
Among other things, we can: better understand disease to develop new treatments or even cures, change food production to be more sustainable and increase animal welfare, reduce or prevent biodiversity loss and ecosystem damage and improve health and longevity across different populations.
Who designs a machine learning algorithm?
The computer designs the algorithm itself!
Machine learning (ML) is the process where computers evolves and adapts the algorithm without human help, trying different methods to analyse the data, keeping what works and throwing out what does not until it narrows down to the best.
This automatic training makes ML different and creates a more powerful and efficient system than humans could design.
