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Life Science Knowledge Engineering Lab
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A lab that uses machine learning and statistics to decipher metabolic and signaling pathways.
AboutAbout the lab
Uncovering connections between biomolecules from data
The focus of life science research is shifting from analyzing the role of individual biomolecules to understanding how living systems work among many kinds of mixed molecules. A representative example is the pathway, a set of interactions among biomolecules.
To uncover the molecular mechanisms of biological phenomena, such as metabolic pathways and signal transduction pathways, from diverse data, the lab develops many informatics methods grounded in computer science and statistics.
Key terms
- Pathway
- A route along which biomolecules interact one after another, such as metabolism or the transmission of signals
- Machine learning
- A technology that lets computers learn rules and patterns from data
- Data mining
- Extracting useful patterns and knowledge from large amounts of data
ResearchResearch themes
The lab develops new machine learning and data mining methods for analyzing biological and chemical data, especially data represented as graphs or networks.
*Illustrative imageAnalyzing network data with machine learning
The lab develops methods to analyze biological and chemical data represented as graphs or networks, sometimes combined with tabular data such as gene expression.
*Illustrative imageInferring molecular networks from drug administration data
The lab works on drug discovery informatics, an information science that infers networks among biomolecules from drug administration data to support drug discovery.
MembersFaculty
- ProfessorHiroshi Mamitsuka
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Hist1 Biological Network Graph” LuAtOU / CC0 (cropped)
- Analyzing network data with machine learning: “Feature Learning Diagram” Fgpacini / CC BY-SA 4.0 (cropped)
- Inferring molecular networks from drug administration data: “Protein–protein interaction network and GO enrichment visualization generated by the author using STRING v12.0 from a curated human immune gene set (FG-001)” BioMedia1091 / CC BY 4.0 (cropped)
- Related lab (生体分子計測学): “Mass spectrometer quadrupole” Fulvio314 / CC BY-SA 3.0 (cropped)
- Related lab (京都大学 緒方研究室
（微生物生態学・進化学・バイオインフォマティクス） ): “Cryoelectron microscopy of the Giant Mimivirus - journal.pbio.1000092.g005” Xiao C, Kuznetsov YG, Sun S, Hafenstein SL, Kostyuchenko VA, et al. (2009) / CC BY 2.5 (cropped) - Related lab (システムケモセラピー・
制御分子学): “Ecological Trail Garden of Medicinal and Aromatic Plants and South China Medicinal Plants Garden )” LN9267 / CC BY-SA 4.0 (cropped) - Related lab (数理治療薬学): “Iodine pills” Mx. Granger / CC0 (cropped)
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