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関連する概念動画

Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

13.2K
Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
13.2K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

11.1K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
11.1K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

10.5K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
10.5K
Prediction Intervals01:03

Prediction Intervals

2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.5K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.5K
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...
1.5K
Sequences01:29

Sequences

504
Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where...
504

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Updated: May 5, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

14.3K

ディープラーニングによるプライマリシーケンスのスプライシングを予測する

Kishore Jaganathan1, Sofia Kyriazopoulou Panagiotopoulou1, Jeremy F McRae1

  • 1Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.

Cell
|January 22, 2019
PubMed
まとめ
この要約は機械生成です。

新しい深層ニューラルネットワークは スプライス・ジャンクションを正確に予測し 遺伝的変異によって引き起こされる 謎めいたスプライシングを特定します この発見は 希少な遺伝的疾患や 神経発達障害の 原因を明らかにしています

キーワード:
人工知能ディープラーニング遺伝学スプライシング

さらに関連する動画

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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A Reporter Based Cellular Assay for Monitoring Splicing Efficiency
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A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

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Last Updated: May 5, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

14.3K
Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

6.3K
A Reporter Based Cellular Assay for Monitoring Splicing Efficiency
08:53

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

Published on: September 15, 2021

3.2K

科学分野:

  • 分子生物学
  • 遺伝学
  • バイオ情報学

背景:

  • スプライシングの精度は 遺伝子発現に不可欠ですが 背後にあるメカニズムは不明です
  • 遺伝子の変異によって引き起こされる 謎めいたスプライシングは 病気につながるが 予測するのは難しい
  • スプライス・ジャンクションの予測を理解することは 遺伝的疾患の診断に不可欠です

研究 の 目的:

  • スプライス・ジャンクションを正確に予測するための 深いニューラル・ネットワークを開発する
  • 謎のスプライシングを 引き起こしている遺伝子の変異を 特定するために
  • ヒトの病気におけるスプライス変異の役割を評価する.

主な方法:

  • スプライス・ジャンクション予測のための深層ニューラル・ネットワークモデルを開発した.
  • スプライス変異の結果について 同義変異と内在変異を分析した.
  • RNA配列データを用いて検証した予測.
  • 自閉症や知的障害の患者における de novo 変異を調べた.

主要な成果:

  • ディープニューラルネットワークは mRNA前配列から スプライス・ジャンクションを正確に予測します
  • 予想されたスプライス変異は,RNA-seqで高い検証率を示し,ヒト集団において有害である.
  • 自閉症や知的障害を持つ患者では,スプライス変化の影響を伴うデノボ変異が強化されています.
  • 28人中21人の患者で,RNA-seqで検証されたスプライス変異を予測した.

結論:

  • ディープニューラルネットワークは スプライス・ジャンクションを正確に予測し 暗号的なスプライシングを特定できます
  • スプライス変異遺伝子は 珍しい遺伝疾患や神経発達障害の 重要で過小評価されている原因です
  • このアプローチは遺伝疾患の診断と 変異の影響を理解するのに役立ちます