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

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...

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

Meta-analysis of the clinical efficacy of microcatheter-assisted trabeculotomy in the treatment of glaucoma.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research·2026
Same author

Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.

Nature communications·2026
Same author

Bridging sequence-structure motifs and genetic variants for genome-wide dynamic RNA-protein interaction profiling.

Nature communications·2026
Same author

Olfactory Decline in Elderly at High Altitudes: A Narrative Review.

International journal of general medicine·2026
Same author

Development and Interpretation of a Machine Learning-Based Predictive Model Using Clinical Parameters for Eosinophilic Chronic Rhinosinusitis With Nasal Polyps.

American journal of rhinology & allergy·2026
Same author

Orthogonal disentanglement of single-cell multi-omics reveals private and shared drivers of tissue development and pathogenesis.

Proceedings of the National Academy of Sciences of the United States of America·2026

関連する実験動画

Updated: May 8, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.7K

scOTM:大規模な言語モデルで単細胞の混乱反応を予測するためのディープラーニングフレームワーク

Yuchen Wang1, Tianchi Lu1, Xingjian Chen2

  • 1Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077, China.

Bioengineering (Basel, Switzerland)
|August 28, 2025
PubMed
まとめ

scOTMという ディープラーニングモデルを開発し 単細胞の薬物反応を 配列されていないデータから予測しました この方法は,トランスクリプションのシフトを柔軟にモデル化し,新しい細胞タイプに一般化することで,既存のアプローチの限界を克服します.

キーワード:
ディープラーニング大型言語モデル最適な輸送シングル・セル・パートルバション予測

さらに関連する動画

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

3.7K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

684

関連する実験動画

Last Updated: May 8, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.7K
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

3.7K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

684

科学分野:

  • コンピュータ生物学
  • ゲノミクス
  • 機械学習

背景:

  • 単細胞の薬剤反応の正確なモデリングは,医療に不可欠です.
  • 現在の方法はペアリングされていないデータと 生物学的な解釈が欠けている.
  • 既存のモデルはしばしば制限的な事前のアライメントを課し,表現力を制限します.

研究 の 目的:

  • scOTMというディープラーニング・フレームワークを開発し,ペア化されていないデータから単細胞の混乱反応を予測する.
  • 目に見えない細胞タイプへの一般化を改善し,生物学的解釈性を高める.
  • ペアリングされていないデータと厳格な先行制約の処理における既存の方法の限界を克服する.

主な方法:

  • scOTMは,大きな言語モデルからの生物学的知識を,変数的な自動エンコーダーに統合します.
  • 最大平均不一致の正規化は,転写シフトの柔軟なモデリングを可能にします.
  • 最適な輸送は,コントロールと混乱した細胞分布の間の解釈可能なマッピングを確立します.

主要な成果:

  • scOTMは,全トランスクリプトームの反応を予測し,差異的に発現する遺伝子を特定する既存の方法よりも優れています.
  • このフレームワークは,データ限定のシナリオにおいて優れた強度を示しています.
  • scOTMは,多様な細胞タイプに強い汎用性を示しています.

結論:

  • scOTMは,単細胞の薬物反応を予測するための強力で柔軟な枠組みを提供します.
  • この方法は,解釈可能な埋め込みと柔軟なモデリングを提供することで,生物学的理解を高めます.
  • scOTMはペア化されていないデータを効果的に処理し,新しい細胞タイプに一般化することで,この分野を前進させています.