Research topic
Machine Learning in Bioinformatics
Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology
About Machine Learning in Bioinformatics
This cluster of papers focuses on the prediction of protein subcellular localization using various computational methods such as amino acid composition, machine learning algorithms like support vector machines, and the analysis of signal peptides and transmembrane topology. The research aims to improve the accuracy and reliability of predicting the subcellular location of proteins, which has significant implications for understanding protein function and cellular processes.
Keywords
Subcellular Localization Protein Prediction Amino Acid Composition Machine Learning Support Vector Machines Signal Peptides Transmembrane Topology Enzyme Subfamily Classes Bioinformatics
Development over time
| Year | research outputs |
|---|---|
| 2017 | 1 |
Units
Units strongly represented in this topic
| Units |
|---|
ZMT Academy1 Research outputs |
Geoecology and Carbonate Sedimentology1 Research outputs |
Ecosystem Co-Design1 Research outputs |
Societal Impact1 Research outputs |
People
Researchers strongly represented in this topic
Here, researchers who have publications in OSIRIS assigned to this spectrum topic are displayed. For better clarity, only a selection is shown.
Dr. Achim Meyer
1 Research outputs
Prof. Dr. Hildegard Westphal
1 Research outputs
Dr. Marleen Arlt-Stuhr
1 Research outputsThe assignment of publications to topics is done automatically and may be incomplete or incorrect. If a person does not appear in the list, it does not mean that they could not have contributed to the topic.