Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Natural and Artificial Concepts01:24

Natural and Artificial Concepts

264
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
264
Concepts and Prototypes01:24

Concepts and Prototypes

220
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
220
Stereotype Content Model02:16

Stereotype Content Model

14.9K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.9K
Schemata01:17

Schemata

142
A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
Two types of schemata are:
142
Data: Types and Distribution01:19

Data: Types and Distribution

838
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
838
Language and Cognition01:27

Language and Cognition

438
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
438

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Defining peptides in ChEBI.

Journal of cheminformatics·2026
Same author

Automatically detecting trends and open questions from mental health publications: a Wellcome-funded GALENOS project.

BMJ mental health·2026
Same author

Performance Evaluation of Large Language Models in Multilingual Medical Multiple-Choice Questions: Mixed Methods Study.

JMIR medical education·2026
Same author

A comparative performance analysis of regular expressions and a large language model-based approach to extract the BI-RADS score from radiological reports.

JAMIA open·2025
Same author

Predicting outcomes of smoking cessation interventions in novel scenarios using ontology-informed, interpretable machine learning.

Wellcome open research·2025
Same author

Comparative Evaluation of a Medical Large Language Model in Answering Real-World Radiation Oncology Questions: Multicenter Observational Study.

Journal of medical Internet research·2025
Same journal

Count your bits: fingerprint benchmarking to assess broad chemical space representation.

Journal of cheminformatics·2026
Same journal

Sampling out-of-distribution chemical spaces via Bayesian flow.

Journal of cheminformatics·2026
Same journal

Hold on tight: the kinetic profiling of opioid receptor ligands using the CORAL-MD.

Journal of cheminformatics·2026
Same journal

Transformer-accelerated discovery of inhibitors targeting the RpsA<sub>Δ438</sub> deletion in PZA-resistant tuberculosis.

Journal of cheminformatics·2026
Same journal

DICL: a manually curated database of ion channels and ligands as a useful platform for drug discovery targeting ion channels.

Journal of cheminformatics·2026
Same journal

DCPM-ADMET: fusion of dual-component pre-trained model and molecular fingerprints to enhance drug ADMET properties prediction.

Journal of cheminformatics·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681

オントロジーを拡張するためのボックスエンブレディング:データ主導で解釈可能なアプローチ

Adel Memariani1, Martin Glauer2, Simon Flügel3

  • 1Data Science Group (DICE), Heinz Nixdorf Institute, Paderborn University, Warburger Str. 100, 33098, Paderborn, North Rhine-Westphalia, Germany. adel.memariani@uni-paderborn.de.

Journal of cheminformatics
|September 2, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,階層的な関係を表すための箱状の埋め込みを使用して,マルチラベル分類で解釈可能なディープラーニングのための新しい方法を導入しています. このアプローチは,オントロジカルな概念化との一貫性を確保しながら,最先端のパフォーマンスを達成します.

キーワード:
ボックスの埋め込みチェビ分類するオントロジー

さらに関連する動画

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

関連する実験動画

Last Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

科学分野:

  • 人工知能
  • 化学情報学
  • バイオ情報学

背景:

  • ディープラーニングモデルは透明性がなく 象徴的な知識の抽出を妨げています
  • 複雑なモデルの出力を理解するには 解釈可能なAIが不可欠です
  • マルチラベル分類のタスクには,しばしば固有の階層的なラベル構造が含まれます.

研究 の 目的:

  • ディープラーニングモデルからシンボリックな知識を導き出す方法を開発する.
  • モデルアウトプットに分類的構造を適用し,解釈性を向上させる.
  • 幾何学的な埋め込みを使用して,マルチラベルデータセットで暗黙の論理的関係を表現する.

主な方法:

  • ベクトル空間におけるオントロジークラスの 箱状の埋め込みを使用した.
  • 訓練中にモデルアウトプットに分類構造を強制した.
  • ChEBIオントロジーのサブクラス関係を近似することによってモデルのパフォーマンスを評価します.

主要な成果:

  • このモデルは,ラベル間の暗黙の階層的な関係をうまく捉えています.
  • オントロジカル・コンセプチュアライゼーションと一貫性を確保した.
  • マルチラベル分類のタスクで最先端の性能を達成しました.

結論:

  • 提案されたアプローチは,化学分類で解釈可能な出力を可能にします.
  • 分子の幾何学的な表現は論理的な関係を理解するのに役立ちます.
  • 暗黙の階層は,訓練中に明示的な分類法なしで学習されます.