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

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

116
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
116
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

140
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
140
Gauss's Law: Problem-Solving01:10

Gauss's Law: Problem-Solving

2.1K
Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
2.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.7K
2.7K
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

302
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
302
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

2.1K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
2.1K

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

On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.

Journal of mathematical biology·2026
Same author

Genetic background shapes AI-predicted variant effects.

bioRxiv : the preprint server for biology·2026
Same author

Bacterial proteome foundation model enhances functional prediction from enzymes to ecological interactions.

bioRxiv : the preprint server for biology·2026
Same author

Inferring genotype-phenotype maps using attention models.

PNAS nexus·2026
Same author

Evolution of the rate, molecular spectrum, and fitness effects of mutation under minimal selection in Caenorhabditis elegans.

Genetics·2026
Same author

Evolution of the rate, molecular spectrum, and fitness effects of mutation under minimal selection in <i>Caenorhabditis elegans</i>.

bioRxiv : the preprint server for biology·2026
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 9, 2025

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

スケール可能で解釈可能なガウスのプロセスによる学習配列関数関係

Juannan Zhou, Carlos Martí-Gómez, Samantha Petti

    bioRxiv : the preprint server for biology
    |September 2, 2025
    PubMed
    まとめ
    この要約は機械生成です。

    ゲノタイプ-フェノタイプ関係を理解するために 解釈可能なガウスのプロセスモデルを開発しました 大量の生物学的配列データセットにおけるエピスタシスを説明します 私たちのアプローチは 優れた予測能力を提供し 新しい遺伝子の相互作用を 明らかにします

    さらに関連する動画

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    4.1K
    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
    11:20

    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

    Published on: June 2, 2014

    12.1K

    関連する実験動画

    Last Updated: Sep 9, 2025

    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
    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    4.1K
    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
    11:20

    Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

    Published on: June 2, 2014

    12.1K

    科学分野:

    • 遺伝学とバイオ情報学
    • コンピュータ生物学
    • システム生物学

    背景:

    • ゲノタイプとフェノタイプの関係を理解することは遺伝学において極めて重要であるが,エピスタシス (文脈に依存する変異的効果) によって複雑である.
    • ハイ・スループット・フェノタイプ化は大きなデータセットを生成しますが,標準モデルは一般化性と解釈性に問題があります.
    • ディープニューラルネットワークは 柔軟性がありますが 解釈性や不確実性の量化には欠けています

    研究 の 目的:

    • 配列関数関係の解釈可能なガウスのプロセスモデルの新しいファミリーを導入する.
    • フィットネス・ランドスケープモデルを一般化する柔軟な先行分布を用いてエピスタシスを捉える.
    • 複雑な遺伝子の相互作用を探求するためのスケーラブルで解釈可能な方法を提供する.

    主な方法:

    • モデルエピスタシスに柔軟な先行分布を持つ解釈可能なガウスのプロセスモデルを開発した.
    • エピスタティック効果を定量化するために,サイト,アレル,および変異特有の要因を組み込む.
    • 大量のデータセット (タンパク質,RNA,全ゲノムSNP) へのスケーラビリティのためのGPU加速を使用した.

    主要な成果:

    • 大きな生物学的配列データセットで優れた予測性能を達成しました.
    • 既知の遺伝的特徴を復元する解釈可能なモデルパラメータを生成します.
    • ゲノタイプ-フェノタイプマップに関する新しい洞察を提供した.

    結論:

    • 開発されたガウスのプロセスモデルは,配列関数関係を研究するためのスケーラブルで解釈可能なアプローチを提供します.
    • これらのモデルはエピスタシスを効果的に捕捉し,遺伝子型-フェノタイプマップのより深い洞察を提供します.
    • この方法は,DNA,RNA,タンパク質の配列を含む多様な生物系に適用できます.