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Statistical Learning Lab
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A lab developing methods that analyze biological data using statistics and optimization.
AboutAbout the lab
Building methods to analyze biological data.
The Statistical Learning Lab researches the development of methods for analyzing biological data.
Its main tools are statistics, optimization, and data mining.
Besides methods for predicting protein function and compound activity, the lab also develops machine learning methods for the manufacturing sector.
The lab cannot provide guidance on wet-lab experiments, but it can provide guidance on data analysis.
Key terms
- Data mining
- A method for extracting useful knowledge and patterns from large amounts of data.
- Kernel
- A mapping function designed to extract features from data.
- Virtual screening
- The task of narrowing down millions of compounds on a computer to those that could become drug candidates.
ResearchResearch themes
The lab's research is the development of methods for analyzing biological data. Its main tools are statistics, optimization, and data mining.
*Illustrative imagePredicting protein function
The lab studies methods for predicting protein function from amino acid sequences. By carefully designing kernels, which are mapping functions, to extract features, it develops function prediction methods that are more accurate than conventional ones.
*Illustrative imagePredicting compound activity
Against the background of virtual screening, which is used in the drug design process, the lab predicts the activity of compounds. It aims to build models that can explain why a compound has high activity and why it does not.
*Illustrative imageMachine learning for manufacturing data
The lab develops machine learning methods for the manufacturing sector. In the steel industry, for example, it has studied comparisons of unsupervised learning methods for detecting abnormalities in blast furnaces.
MembersFaculty
- Associate ProfessorHiroto Saigo
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: “Bioinformatics Division, National Institute of Biotechnology (NIB) laboratory” Ishtiaquebioinfo / CC BY-SA 4.0 (cropped)
- Predicting protein function: “ANKRD35 protein structure” 1. Yang Zhang. I-TASSER: Fully automated protein structure prediction in CASP8. Proteins, 77 (Suppl 9): 100-113, 2009. 2. Ambrish Roy, Jianyi Yang, Yang Zhang. COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Research, 40: W471-W477, 2012. 3. Jianyi Yang, Yang Zhang. I-TASSER server: new development for protein structure and function predictions, Nucleic Acids Research, 43: W174-W181, 2015. / CC BY-SA 4.0 (cropped)
- Predicting compound activity: “Chemical Compounds ( )” National Center for Advancing Translational Sciences from Bethesda, MD / Public domain (cropped)
- Machine learning for manufacturing data: “3SUN-Gigafactory-Catania-automated-production-line” Ottaluc / CC BY-SA 4.0 (cropped)
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