Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Survival Tree01:19

Survival Tree

159
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
159
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

586
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
586
Observational Learning01:12

Observational Learning

312
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
312
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
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...
1.9K
Associative Learning01:27

Associative Learning

575
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...
575
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.7K
2.7K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

GatedGeoGO:Multi-Modal Geometry-Aware Network with Gated Fusion and GO Semantic Attention for Protein Function Prediction.

Journal of chemical information and modeling·2026
Same author

Comprehensive Review of Contrastive and Generative Self-Supervised Learning for Small Molecular Representation.

Journal of chemical information and modeling·2026
Same author

Geometry-complete latent diffusion model for 3D molecule generation.

Bioinformatics (Oxford, England)·2025
Same author

MAPTrans: mutual attention transformer with dynamic meta-path pruning for drug repositioning.

Briefings in bioinformatics·2025
Same author

Drug-drug interaction prediction based on graph contrastive learning and dual-view fusion.

Computational biology and chemistry·2025
Same author

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction.

BMC bioinformatics·2024
Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026
Same journal

DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites.

Journal of chemical information and modeling·2026
Same journal

HyperDC: A Non-Uniform Hypergraph Framework for Dual- and Higher-Order Drug Combination Recommendation Across Diverse Complex Diseases.

Journal of chemical information and modeling·2026
Same journal

Correction to "AstraMEV (AI-Guided Structural Assembly of Multi-Epitope Vaccines) Against Infectious Bronchitis Virus".

Journal of chemical information and modeling·2026
Same journal

MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations.

Journal of chemical information and modeling·2026
Same journal

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Journal of chemical information and modeling·2026
查看所有相关文章

相关实验视频

Updated: Sep 11, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

基于转移学习和超连接图模型的任务特定活动悬崖预测方法.

Dongjiang Niu1, Zengqian Deng1, Xiaofeng Wang2

  • 1College of Computer Science and Technology, Qingdao University, Qingdao 266000, Shandong, China.

Journal of chemical information and modeling
|August 11, 2025
PubMed
概括
此摘要是机器生成的。

预测活动悬崖 (ACs) 对药物发现至关重要. 我们的新框架TS-AC使用转移学习和图形网络来准确识别这些悬崖,改善分子优化.

更多相关视频

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.0K

相关实验视频

Last Updated: Sep 11, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.0K

科学领域:

  • 药用化学 医学化学
  • 计算化学的计算化学
  • 药物发现 药物发现 药物发现

背景情况:

  • 活动悬崖 (ACs) 代表了从分子中微小的结构修改中显著的生物活动变化.
  • 准确预测ACs对于高效的药物发现和分子优化至关重要.
  • 当前的方法往往无法捕捉复杂的结构关系,限制了预测的准确性和通用性.

研究的目的:

  • 开发一个新的框架,TS-AC,用于准确的活动悬崖预测.
  • 通过整合从大规模药物相互作用 (DDI) 预测任务的转移学习来增强模型的概括性.
  • 通过使用超连接图形架构来改善结构-活动关系的表示.

主要方法:

  • 开发了TS-AC,一个特定任务的框架,结合了转移学习和超连接图形架构.
  • 预先训练了一个模型进行大规模的药物相互作用 (DDI) 预测任务,以获得一般化学知识.
  • 设计了一个超连接图模块,以在匹配的分子对中模拟核心和替代物片段之间的相互作用.

主要成果:

  • 与最先进的方法相比,TS-AC在三个独立数据集中表现出更高的性能.
  • 超连接图模块有效地捕捉了微妙的结构修改对生物活动的影响.
  • 可视化分析证实了TS-AC框架的可解释性和逻辑设计.

结论:

  • 对于药物发现,TS-AC在活动悬崖预测方面取得了重大进展.
  • 转移学习和图形神经网络的整合提供了一个强大的方法来建模结构-活动关系.
  • 拟议的框架提高了预测微小化学变化对生物活动的影响的准确性和通用性.