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

Machines01:19

Machines

581
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
581
Machines: Problem Solving II01:30

Machines: Problem Solving II

674
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
674
Machines: Problem Solving I01:22

Machines: Problem Solving I

719
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
719
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Associative Learning01:27

Associative Learning

1.4K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.4K
Purposive Learning01:22

Purposive Learning

513
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
513

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

LAML-Pro: joint maximum likelihood inference of cell genotypes and cell lineage trees.

Bioinformatics (Oxford, England)·2026
Same author

Riemannian metric learning for alignment of spatial multiomics.

Bioinformatics (Oxford, England)·2026
Same author

Virtual Tumors Enable Prediction of Personalized Therapeutic Combinations for Non-Small Cell Lung Cancer.

Cancer research·2026
Same author

The tree labeling polytope: A unified approach to ancestral reconstruction problems.

Cell systems·2026
Same author

Dango: Predicting higher-order genetic interactions.

Cell systems·2026
Same author

Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.

Cancer discovery·2026

関連する実験動画

Updated: Feb 8, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K

生物医学のための可視機械学習

Michael K Yu1, Jianzhu Ma2, Jasmin Fisher3

  • 1Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA; UCSD Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA.

Cell
|June 16, 2018
PubMed
まとめ
この要約は機械生成です。

人工知能は患者のデータを分析することで 治療法を改善できます 実験生物学による可視機械学習アプローチは データの多様性や 生物医学的な予測の洞察力不足などの課題に対処します

さらに関連する動画

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.1K

関連する実験動画

Last Updated: Feb 8, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.1K

科学分野:

  • 生物医学研究
  • 人工知能
  • 機械学習

背景:

  • 患者のデータを効果的な治療法に変換することは 人工知能 (AI) の重要な目標です
  • 生物医学における機械学習 (ML) モデルは,非常に多様なデータと,その予測の生物学的根拠を理解するために苦労しています.
  • 機械的な洞察の欠如は医学におけるAIの臨床的応用を妨げています

研究 の 目的:

  • 生物医学における"目に見える"人工知能のアプローチを提唱する.
  • 実験生物学原理を機械学習モデル設計に統合することを提案する.
  • AI主導の治療戦略の解釈性と信頼性を高めること

主な方法:

  • 生物学的知識を組み込む機械学習の枠組みを開発する.
  • 実験生物学による構造でAIモデルを設計する.
  • 予測の透明性を高めるために"目に見える"アプローチに焦点を当てます.

主要な成果:

  • 提案された可視人工知能の方法は,データの異質性を克服する道を示しています.
  • 実験生物学を統合することで,AIの予測のメカニズム的理解が強化されます.
  • 視覚的なアプローチは 患者のデータを治療法に変換することを改善します

結論:

  • 実験生物学によって導かれる 視覚的なAIは 生物医学的な応用を進めていく上で 極めて重要です
  • このアプローチは 医療における現在の機械学習の 重要な限界を解決します
  • より信頼性があり 解釈可能な AI 駆動の治療開発を可能にします