ANBE KAORI 安部賀央里

… Alternative Names

Ambe Kaori 安部賀央里

Less

Researcher Number	70440625
Other IDs	https://orcid.org/0000-0003-4387-9134
Affiliation (Current)	2024: 名古屋市立大学, 医薬学総合研究院(薬学), 准教授
Affiliation (based on the past Project Information) *help	2021 – 2024: 名古屋市立大学, 医薬学総合研究院(薬学), 講師 2019 – 2020: 名古屋市立大学, 医薬学総合研究院(薬学), 助教 2018: 名古屋市立大学, 大学院薬学研究科, 助教
Review Section/Research Field	Principal Investigator Basic Section 47060:Clinical pharmacy-related / Basic Section 47030:Pharmaceutical hygiene and biochemistry-related Except Principal Investigator Basic Section 58010:Medical management and medical sociology-related / Basic Section 53040:Nephrology-related
Keywords	Principal Investigator 機械学習 / 副作用 / インシリコ予測手法 / 皮膚感作性 / 動物実験代替法 / 医療情報データベース / 有害事象自発報告データベース / 医療ビッグデータ / 薬剤性腎障害 / 機械学習法 … More / 人工知能 / ビッグデータ / インシリコ予測 / JADER / 薬物性肝障害 … More Except Principal Investigator AI / 医療ビッグデータ / 新型コロナウイルス後遺症 / 新型コロナウイルス感染症 / 機械学習 / 化学療法 / 尿細管 / AKI / 電解質異常 / 低Na血症 Less

Development of Data-Driven Predictive System by AI Aimed at Mitigating Risks of COVID-19 Long-Term Effects
- Principal Investigator
  
  和知野千春
- Project Period (FY)
  2024 – 2026
- Research Category
  
  Grant-in-Aid for Scientific Research (C)
- Review Section
  
  Basic Section 58010:Medical management and medical sociology-related
- Research Institution
  Nagoya City University
Development of an in silico prediction model and practical research for evaluating skin sensitizationPrincipal Investigator
- Principal Investigator
  
  安部賀央里
- Project Period (FY)
  2023 – 2025
- Research Category
  
  Grant-in-Aid for Scientific Research (C)
- Review Section
  
  Basic Section 47030:Pharmaceutical hygiene and biochemistry-related
- Research Institution
  Nagoya City University
Onconephrology focusing on tubules
- Principal Investigator
  
  濱野高行
- Project Period (FY)
  2021 – 2023
- Research Category
  
  Grant-in-Aid for Scientific Research (C)
- Review Section
  
  Basic Section 53040:Nephrology-related
- Research Institution
  Nagoya City University
Development of prediction method for drug-induced kidney injury using medical big data and machine learningPrincipal Investigator
- Principal Investigator
  
  Ambe Kaori
- Project Period (FY)
  2020 – 2022
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 47060:Clinical pharmacy-related
- Research Institution
  Nagoya City University
Prediction of drug-induced liver injury by artificial intelligence based on adverse drug reaction reportsPrincipal Investigator
- Principal Investigator
  
  Ambe Kaori
- Project Period (FY)
  2018 – 2019
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 47060:Clinical pharmacy-related
- Research Institution
  Nagoya City University

All 2023 2022 2021 2020 2019 2018

All Journal Article Presentation

[Journal Article] Skin sensitization potency prediction of ingredients in hair colorants using in silico models of machine learning.2023
- Author(s)
  2.Ashikaga T, Narita K, Kobayashi M, Tachibana S, Murasaki W, Suzuki M, Ambe K, Tohkin M.
- Journal Title
  
  Journal of Japanese Cosmetic Science Society
  
  Volume: 47 Pages: 1-5
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-20K16050
[Journal Article] Comparison of Efficacy and Safety of Direct Oral Anticoagulants and Warfarin between Patients in Asian and non-Asian Regions: A Systematic Review and Meta-Regression Analysis2023
- Author(s)
  Ambe K., Akita A., Wei J., Yoshii Y., Onishi M., Tohkin M.
- Journal Title
  
  Clin Pharmacol. Ther
  
  Volume: n/a Issue: 6 Pages: 1240-1250
- DOI
  10.1002/cpt.2881
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-21K06647, KAKENHI-PROJECT-20K16050
[Journal Article] Development of quantitative model of a local lymph node assay for evaluating skin sensitization potency applying machine learning CatBoost2021
- Author(s)
  Ambe Kaori、Suzuki Masaharu、Ashikaga Takao、Tohkin Masahiro
- Journal Title
  
  Regulatory Toxicology and Pharmacology
  
  Volume: 125 Pages: 105019-105019
- DOI
  10.1016/j.yrtph.2021.105019
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-20K16050
[Journal Article] In Silico Approach to Predict Severe Cutaneous Adverse Reactions Using the Japanese Adverse Drug Event Report Database2021
- Author(s)
  Ambe Kaori, Ohya Kazuyuki, Takada Waki, Suzuki Masaharu, Tohkin Masahiro
- Journal Title
  
  Clinical and Translational Science
  
  Volume: 14 Issue: 2 Pages: 756-763
- DOI
  10.1111/cts.12944
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-20K16050
[Journal Article] Establishment of a threshold of toxicological concern concept for skin sensitization by in vitro/in silico approaches.2021
- Author(s)
  Ashikaga Takao、 Ambe Kaori、Suzuki Masaharu、 Kurimoto Masayuki、 Yamada Takashi、Tohkin Masahiro
- Journal Title
  
  Journal of Japanese Cosmetic Science Society.
  
  Volume: 45 Pages: 331-335
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-20K16050
[Journal Article] Comparison of the developmental/reproductive toxicity and hepatotoxicity of phthalate esters in rats using an open toxicity data source2019
- Author(s)
  Ambe K, Sakakibara Y, Sakabe A, Makino H, Ochibe T, Tohkin M.
- Journal Title
  
  The Journal of Toxicological Sciences
  
  Volume: 44 Issue: 4 Pages: 245-255
- DOI
  10.2131/jts.44.245
- NAID
  130007623321
- ISSN
  0388-1350, 1880-3989
- Language
  English
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-18K14987
[Journal Article] In silico prediction of chemical-induced hepatocellular hypertrophy using molecular descriptors.2018
- Author(s)
  Ambe K, Ishihara K, Ochibe T, Ohya K, Tamura S, Inoue K, Yoshida M, Tohkin M.
- Journal Title
  
  Toxicological Sciences
  
  Volume: 162 Issue: 2 Pages: 667-675
- DOI
  10.1093/toxsci/kfx287
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-18K14987
[Presentation] 悪性腫瘍患者における化学療法中の低Na血症を予測する機械学習モデルの構築およびリスク因子の検討2023
- Author(s)
  青木優佳、安部賀央里、頭金正博、村島美穂、濱野高行
- Organizer
  日本医療薬学会
- Data Source
  KAKENHI-PROJECT-21K08258
[Presentation] Development of in silico model for skin sensitization evaluation using machine learning2022
- Author(s)
  安部賀央里
- Organizer
  ACAAE2022, the 3rd Asian Congress for Alternatives to Animal Experiments
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] FAERSを用いた機械学習による薬物性肝障害の予測モデルの開発2022
- Author(s)
  土井更良, 安部賀央里, 頭金正博
- Organizer
  第49回日本毒性学会学術年会
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] 化学構造情報とin vitro情報を使用した特異体質性薬物性肝障害の予測手法の開発2022
- Author(s)
  片山早紀, 安部賀央里, 頭金正博
- Organizer
  第5回医薬品毒性機序研究会
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] ヒトの副作用予測に向けた機械学習アプローチ2022
- Author(s)
  安部賀央里
- Organizer
  第49回日本毒性学会学術年会
- Invited
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] 悪性腫瘍患者における化学療法中の低Na血症の発症を予測する機械学習モデルの構築2022
- Author(s)
  青木優佳、安部賀央里、村島美穂、濱野高行、頭金正博
- Organizer
  第８回次世代を担う若手のためのレギュラトリーサイエンスフォーラム
- Data Source
  KAKENHI-PROJECT-21K08258
[Presentation] Prediction of drug-induced liver injury in silico using large-scale adverse event database2022
- Author(s)
  土井更良, 安部賀央里, 頭金正博
- Organizer
  CBI学会2022年大会
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] JAPIC AERSと機械学習を活用した胆汁うっ滞型薬物性肝障害の予測モデルの開発2022
- Author(s)
  濱上敦史, 土井更良, 安部賀央里, 頭金正博
- Organizer
  日本薬学会第143年会
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] 医薬品副作用自発報告データベースを用いた機械学習による薬剤性急性腎障害の予測手法の開発2021
- Author(s)
  家田維哉、安部賀央里、頭金正博
- Organizer
  医療薬学フォーラム2021
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] 機械学習法による特異体質性副作用の予測2021
- Author(s)
  安部賀央里
- Organizer
  第42回日本臨床薬理学会学術総会
- Invited
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] 毒性データベースを用いた化学物質のインシリコ毒性予測2021
- Author(s)
  安部賀央里
- Organizer
  第65回日本薬学会関東支部大会
- Invited
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] 化学物質の安全性評価におけるin silico予測モデル2021
- Author(s)
  安部賀央里
- Organizer
  CBI学会2021年大会
- Invited
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] 医薬品副作用自発報告データベースを用いたアンサンブル機械学習モデルによる薬剤性急性腎障害の予測2021
- Author(s)
  家田維哉、安部賀央里、頭金正博
- Organizer
  第42回日本臨床薬理学会学術総会
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] 大規模副作用データを利用した機械学習による薬物性肝障害の予測手法の開発2020
- Author(s)
  高田和季、安部賀央里、頭金正博
- Organizer
  第47回日本毒性学会学術年会
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] 機械学習による薬物性肝障害の予測手法の開発～大規模副作用データベースを利用した予測法～2020
- Author(s)
  高田和季、安部賀央里、頭金正博
- Organizer
  第6回次世代を担う若手のためのレギュラトリーサイエンスフォーラム
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] JADERを用いた機械学習法による薬剤性急性腎障害の予測手法の開発2020
- Author(s)
  家田維哉、安部賀央里、頭金正博
- Organizer
  第6回次世代を担う若手のためのレギュラトリーサイエンスフォーラム
- Data Source
  KAKENHI-PROJECT-20K16050
[Presentation] JADERを用いた医薬品の重症皮膚副作用のin silico予測2019
- Author(s)
  大矢和幸, 安部賀央里、頭金正博
- Organizer
  第46回日本毒性学会学術年会
- Data Source
  KAKENHI-PROJECT-18K14987
[Presentation] 毒性データベースを用いた in silico 安全性予測2019
- Author(s)
  安部賀央里, 頭金正博
- Organizer
  第1回医薬品毒性機序研究会
- Invited
- Data Source
  KAKENHI-PROJECT-18K14987
[Presentation] In silico models for predicting hepatotoxicity and renal toxicity based on HESS database2019
- Author(s)
  Tatsuya Ochibe, Kaori Ambe, Masahiro Tohkin
- Organizer
  CBI 学会 2019 年大会
- Data Source
  KAKENHI-PROJECT-18K14987
[Presentation] 機械学習法を利用した化学物質誘発性腎毒性の予測2019
- Author(s)
  落部達也, 安部賀央里, 頭金正博
- Organizer
  第1回医薬品毒性機序研究会
- Data Source
  KAKENHI-PROJECT-18K14987
[Presentation] In Silico Prediction of Severe Cutaneous Adverse Drug Reactions Using the Japanese Adverse Drug Event Report Database2019
- Author(s)
  Kaori Ambe, Kazuyuki Ohya, Masahiro Tohkin
- Organizer
  ICTXV2019 (15th International Congress of Toxicology)
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-18K14987
[Presentation] In Silico Models for the Predicting of the Repeated Dose Toxicity Based on HESS Database2019
- Author(s)
  Tatsuya Ochibe, Kaori Ambe, Masahiro Tohkin
- Organizer
  ICTXV2019 (15th International Congress of Toxicology)
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-18K14987
[Presentation] Development of in silico predictive classification models for chemical-induced hepatocellular hypertrophy based on molecular descriptors.2018
- Author(s)
  Kaori Ambe, Tatsuya Ochibe, Kazuyuki Ohya, Masahiro Tohkin.
- Organizer
  第18回国際薬理学・臨床薬理学会議 (WCP2018)
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-18K14987
[Presentation] JADERを用いたDeep Learningによる医薬品の重症皮膚副作用の予測2018
- Author(s)
  大矢和幸, 安部賀央里, 頭金正博
- Organizer
  第4回次世代を担う若手のためのレギュラトリーサイエンスフォーラム
- Data Source
  KAKENHI-PROJECT-18K14987
[Presentation] In silico Prediction of Severe Cutaneous Adverse Drug Reactions Based on the Adverse Event Reporting Database2018
- Author(s)
  Kazuyuki Ohya, Kaori Ambe, Masahiro Tohkin
- Organizer
  CBI学会2018年大会
- Data Source
  KAKENHI-PROJECT-18K14987

1. 濱野高行 (50403077)

# of Collaborated Projects: 1 results

# of Collaborated Products: 2 results
2. 村島美穂 (40771837)

# of Collaborated Projects: 1 results

# of Collaborated Products: 2 results
3. 和知野千春 (90816563)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
4. 服部友紀 (90363936)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
5. 近藤勝弘 (00939770)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
6. 頭金正博 (00270629)

# of Collaborated Projects: 1 results

# of Collaborated Products: 1 results
7. 日比陽子 (70295616)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
8. 伊藤穣 (80362482)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
9. 堀田祐志 (90637563)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
10. 坪内希親 (70893311)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
11. 宮崎ゆか (50868297)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results

ANBE KAORI 安部 賀央里

Ambe Kaori 安部 賀央里

Research Projects

Research Products

Co-Researchers

Development of Data-Driven Predictive System by AI Aimed at Mitigating Risks of COVID-19 Long-Term Effects

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Development of an in silico prediction model and practical research for evaluating skin sensitizationPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Onconephrology focusing on tubules

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Development of prediction method for drug-induced kidney injury using medical big data and machine learningPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Prediction of drug-induced liver injury by artificial intelligence based on adverse drug reaction reportsPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] Skin sensitization potency prediction of ingredients in hair colorants using in silico models of machine learning.2023

Author(s)

Journal Title

Data Source

[Journal Article] Comparison of Efficacy and Safety of Direct Oral Anticoagulants and Warfarin between Patients in Asian and non-Asian Regions: A Systematic Review and Meta-Regression Analysis2023

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Development of quantitative model of a local lymph node assay for evaluating skin sensitization potency applying machine learning CatBoost2021

Author(s)

Journal Title

DOI

Data Source

[Journal Article] In Silico Approach to Predict Severe Cutaneous Adverse Reactions Using the Japanese Adverse Drug Event Report Database2021

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Establishment of a threshold of toxicological concern concept for skin sensitization by in vitro/in silico approaches.2021

Author(s)

Journal Title

Data Source

[Journal Article] Comparison of the developmental/reproductive toxicity and hepatotoxicity of phthalate esters in rats using an open toxicity data source2019

Author(s)

Journal Title

DOI

NAID

ISSN

Language

Data Source

[Journal Article] In silico prediction of chemical-induced hepatocellular hypertrophy using molecular descriptors.2018

Author(s)

Journal Title

DOI

Data Source

[Presentation] 悪性腫瘍患者における化学療法中の低Na血症を予測する機械学習モデルの構築およびリスク因子の検討2023

Author(s)

Organizer

Data Source

[Presentation] Development of in silico model for skin sensitization evaluation using machine learning2022

Author(s)

Organizer

Data Source

[Presentation] FAERSを用いた機械学習による薬物性肝障害の予測モデルの開発2022

ANBE KAORI 安部賀央里

Ambe Kaori 安部賀央里