Computational Modelling and Data Management

The computational approaches, algorithms, databases, and methodologies used to handle, manage, analyse, and interpret the data produced in proteomics investigations are referred to as computational proteomics. The widespread use of proteomics in various biological and medical fields, as well as the widespread availability of high-throughput platforms, has resulted in an increase in the amount of available proteomics data, necessitating efficient algorithms, new data management capabilities, and novel analysis, inference, and visual analytics. Furthermore, high-throughput data collection and output provide additional issues in data processing and reuse, as well as tool interoperability and interconnection. On the other hand, the increasing availability of data and technologies brings up new research areas and opportunities (e.g., annotated spectrum libraries) that can only be realised by employing rigorous computer science, machine learning, knowledge discovery, statistics, and signal processing approaches. The ultimate goal of Computational Proteomics, like many other scientific fields, is to infer knowledge models (e.g., to verify a hypothesis or to discover proteins involved in a disease) from the examination of biological data.