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Statistical Learning Lab
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A data analysis lab that develops statistical machine learning methods.
AboutAbout the lab
Building machine learning methods to analyze real-world data.
The Statistical Learning Lab develops statistical machine learning methods for analyzing real-world data.
So far, the lab has mainly worked with biological and chemical data.
In addition to developing methods for predicting protein function, it aims to develop methods for predicting compounds in order to narrow down drug candidates.
The lab states that its current and future research is not limited to these fields.
Key terms
- Kernel function
- A function that expresses, as a number, how similar pieces of data are to each other. It is used in machine learning to handle similar items
- QSAR
- A method that computationally predicts the relationship between a compound's structure and its activity. It is one of the stages of drug development
- Unsupervised learning
- Machine learning that finds patterns and outliers in data that has no correct answers attached
ResearchResearch themes
This lab develops statistical machine learning methods for analyzing real-world data. It has mainly worked with biological and chemical data so far, but states that its current and future research is not limited to these fields.
*Illustrative imageProtein function prediction
Databases contain many amino acid sequences whose functions are unknown. Based on how readily amino acids substitute for one another and on sequence alignment, the lab developed a kernel function that measures how similar sequences are to each other.
*Illustrative imagePrediction methods for narrowing down drug candidates
These are prediction methods (QSAR) for reducing the number of candidate compounds that go to experiments in drug development. The lab aims not only for prediction accuracy but also for explanations that are easy for chemistry and biology researchers to accept.
*Illustrative imageMachine learning for manufacturing
The lab develops machine learning, data mining, and statistical methods for the manufacturing field. One study uses data from blast furnaces for steelmaking to compare unsupervised learning methods for detecting anomalies.
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: “AI relation to Generative Models subset, venn diagram” The Original Benny C / CC BY-SA 4.0 (cropped)
- Protein function prediction: “Átomos de uma proteína” Diego Mariano / CC BY-SA 4.0 (cropped)
- Prediction methods for narrowing down drug candidates: “Molecular docking” Diego Mariano / CC BY-SA 4.0 (cropped)
- Machine learning for manufacturing: “SSPF factory floor view with modules” Creator:George Shelton NASA KSC, retouched and enhanced by John Chryslar / Public domain (cropped)
- Related lab (Cultural Intelligence Science Lab): “Jacques-Louis David - Oath of the Horatii - Google Art Project” Anne-Louis Girodet / After Jacques-Louis David / Public domain (cropped)
- Related lab (Machine Learning Theory Lab): “Decision Tree Anatomy” Lapollard / CC BY-SA 4.0 (cropped)
- Related lab (Machine Learning, Data Mining, and AI Lab): “Bristol robotics laboratory ( )” Anders Sandberg from Oxford, UK / CC BY 2.0 (cropped)
- Related lab (Multi-Agent Lab): “Agreement-between-TCO-and-SACO” Unknown photographer / Public domain (cropped)
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