Auflistung nach Schlagwort "Bioinformatics"
1 - 3 von 3
Treffer pro Seite
Sortieroptionen
- TextdokumentMSDataStream – Connecting a Bruker Mass Spectrometer to the Internet(BTW 2019, 2019) Zoun, Roman; Schallert, Kay; Broneske, David; Fenske, Wolfram; Pinnecke, Marcus; Heyer, Robert; Brehmer, Sven; Benndorf, Dirk; Saake, GunterMetaproteomics is the biological research of proteins of whole communities comprised of thousands of species using tandem mass spectrometry. But still it follows a sequential non parallelizable workflow. Hence, researchers have to wait for hours or even days until the measurement data are available. In our demo, we show a way to decrease the smallest unit of the workflow to a minimum to realize a near real time stream processing system on a fast data architecture.
- TextdokumentMultiple Sequence Alignment using Deep Reinforcement Learning(SKILL 2021, 2021) Joeres, RomanMultiple sequence alignment (MSA) is one of the primal problems in biology and bioinformatics. The question of how to align multiple sequences correctly is crucial for many other fields of research, e.g., gaining information about the evolutionary distance of two or more sequences and therefore about their corresponding species, finding protein targets for drugs, or finding a drug for a certain target protein. Reinforcement learning (RL), and especially deep reinforcement learning (DRL), has become popular in recent years. To name just a few, DRL has shown major success in complex games such as Atari Games, Chess, and Go. We model the problem of aligning multiple sequences as a Markov decision process (MDP) and examine the performance of different (D)RL algorithms compared to state-of-the-art tools.
- KonferenzbeitragPiPa: custom integration of protein interactions and pathways(INFORMATIK 2011 – Informatik schafft Communities, 2011) Arzt, Sebastian; Starlinger, Johannes; Arnold, Oliver; Kröger, Stefan; Jaeger, Samira; Leser, UlfInformation about proteins and their relationships to each other are a common source of input for many areas of Systems Biology, such as protein function prediction, relevance-ranking of disease genes and simulation of biological networks. While there are numerous databases that focus on collecting such data from, for instance, literature curation, expert knowledge, or experimental studies, their individual coverage is often low, making the building of an integrated protein-protein interaction database a pressing need. Accordingly, a number of such systems have emerged. But in most cases their content is only accessible over the web on a per-protein basis, which renders them useless for automatic analysis of sets of proteins. Even if the databases are available for download, often certain data sources are missing (e.g. because redistribution is forbidden by license), and update intervals are sporadic. We present PiPa, a system for the integration of protein-protein interactions (PPI) and pathway data. PiPa is a stand-alone tool for loading and updating a large number of common PPI and pathway databases into a homogeneously structured relational database. PiPa features a graphical administration tool for monitoring its state, triggering updates, and for computing statistics on the content. Due to its modular architecture, addition of new data sources is easy. The software is freely available from the authors.