Akira Funahashi

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Professor

Keio University

Japan

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About

Professor Akira Funahashi is a faculty member at Keio University in Japan. His research interests encompass various domains, including metabolic responses in carcinoma, cross-modal tactile reproduction, olfactory perception, and the dynamics of cellular processes. He has contributed to the field with recent publications exploring topics such as deep learning applications in reproductive biology, mechanical vibrations affecting cellular metabolism, and advancements in systems biology modeling.

Recent Grants

Grant: Close

深層学習を用いた細胞追跡アルゴリズムの開発

Open Date: 2020-04-01

Close Date: 2023-03-31

Grant: Close

深層学習を用いたヒトES,iPS細胞由来心筋細胞の分化、成熟度評価法の開発

Open Date: 2019-06-28

Close Date: 2021-03-31

Grant: Close

力学刺激の知能化によるin vitro3次元組織の超効率的成熟化

Open Date: 2019-04-01

Close Date: 2022-03-31

Grant: Close

教師なし特徴学習による高精度多細胞種セグメンテーションアルゴリズムの開発

Open Date: 2018-04-01

Close Date: 2020-03-31

Grant: Close

Gene regulatory networks underlying 3-D conformational change in genome associated with anhydrobiosis

Open Date: 2017-04-01

Close Date: 2021-03-31

Articles (15)

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2025

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2022

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2021

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2021

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2020

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2020

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2020

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2020

Collaborators (23)

Claudine Chaouiya

Associate Professor

Aix-Marseille University

FRANCE

Akira Funahashi

Research Interests

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About

Recent Grants

Articles (15)

Profiling the effect of low frequency mechanical vibration on the metabolic and oxidative stress responses of A431 carcinoma

A Cross-Modal Tactile Reproduction Utilizing Tactile and Visual Information Generated by Conditional Generative Adversarial Networks

Metabolic Tug-of-War between Glycolysis and Translation Revealed by Biochemical Reconstitution

A deep position-encoding model for predicting olfactory perception from molecular structures and electrostatics

Switching from weak to strong cortical attachment of microtubules accounts for the transition from nuclear centration to spindle elongation in metazoans

SBML level 3 package: spatial processes, version 1, release 1

Temperature elevation detection in migrating cells

An explainable deep learning-based algorithm with an attention mechanism for predicting the live birth potential of mouse embryos

Symbolic Integration by Integrating Learning Models With Different Strengths and Weaknesses

SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

Genome-Wide Role of HSF1 in Transcriptional Regulation of Desiccation Tolerance in the Anhydrobiotic Cell Line, Pv11

SBML Level 3: an extensible format for the exchange and reuse of biological models

3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis

COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms

Identification of a master transcription factor and a regulatory mechanism for desiccation tolerance in the anhydrobiotic cell line Pv11

Collaborators (23)

Claudine Chaouiya

Marco Antoniotti

Hidde de Jong

Devin Sullivan

Sylvain Soliman

Lucian P. Smith

Colin Gillespie

Emanuel Gonçalves

Brett Olivier

Kenjiro Takemura

Chris Cox

Sarah Keating

Richard CORNETTE

Rainer Machné

Matthias König

Christoph Flamm

Satoshi Arai

Yasuhiro Kamei

Allyson Lister

Martina Kutmon

Frank Bergmann

Kedar Nath Natarajan

Yugo Miyata

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