# MADGUI: Multi-Application Design Graphical User Interface for active learning assisted by Bayesian optimization

https://mdr.nims.go.jp/datasets/22869952-2936-41eb-a5fe-fd4c4248fa44

## File

- [1-s2.0-S0169743925000085-main.pdf](https://mdr.nims.go.jp/filesets/32289358-e667-4f5c-8f87-0a289c018fd5/download) ([Detail](https://mdr.nims.go.jp/filesets/32289358-e667-4f5c-8f87-0a289c018fd5.md))

## Id

22869952-2936-41eb-a5fe-fd4c4248fa44

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-01-25T03:06:35.113124Z

## Updated at

2025-01-27T03:30:30.452888Z

## Published at

2025-01-27T03:30:30.636043Z

## Doi



## First published url

https://doi.org/10.1016/j.chemolab.2025.105323

## Date published

2025-01-21

## Recorded date published

2025-3

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: 'MADGUI: Multi-Application Design Graphical User Interface for active learning
    assisted by Bayesian optimization'
  title_type: original
  lang: en

## Description

- description: We present MADGUI, Multi-Application Design Graphical User Interface
    (GUI) using Bayesian Optimization and prediction model for data analysis and optimize
    process or composition. Its strength is its user-friendly design, which requires
    no programming knowledge. It is built using the Streamlit library in Python and
    is divided into three parts, allowing users to select various parameters and fill
    csv/xlsx files without any coding required. Overall, MADGUI is designed as an
    optimal experiment design platform with active machine learning, which accelerates
    the discovery of optimal solutions and provides an intuitive GUI for users with
    no experience in coding, machine learning, or optimization.
  description_type: abstract
  lang: und

## Creator

- name: Christophe Bajan
  role: author
  orcid: https://orcid.org/0009-0008-1433-9618
- name: Guillaume Lambard
  role: author
  orcid: https://orcid.org/0000-0003-0275-4079

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Graphical user interface
  schema: not_defined
- subject: Statistical analysis
  schema: not_defined
- subject: Machine learning
  schema: not_defined
- subject: Bayesian optimization
  schema: not_defined
- subject: Process optimization
  schema: not_defined
- subject: User-friendly
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Chemometrics and Intelligent Laboratory Systems
  issn: '01697439'
  volume: '258'
  article_number: '105323'

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## Instrument



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## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



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## Fileset

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  size: 6273245
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## Thumbnail

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filename: 1-s2.0-S0169743925000085-main.pdf