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相关概念视频

Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

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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,...
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Predicting Products: Substitution vs. Elimination02:52

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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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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.
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Prediction Intervals01:03

Prediction Intervals

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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. 
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End Point Prediction: Gran Plot01:07

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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.
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Sequences

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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...
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相关实验视频

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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通过深度学习从初级序列预测拼接

Kishore Jaganathan1, Sofia Kyriazopoulou Panagiotopoulou1, Jeremy F McRae1

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

Cell
|January 22, 2019
PubMed
概括
此摘要是机器生成的。

一个新的深度神经网络准确地预测结合, 识别由遗传变异引起的神秘结合. 这一发现揭示了以前被低估的罕见遗传疾病和神经发育疾病的原因.

关键词:
人工智能深度学习遗传学接合方式

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科学领域:

  • 分子生物学
  • 遗传学
  • 生物信息学

背景情况:

  • 拼接精度对于基因表达至关重要,
  • 由遗传变异引发的神秘拼接会导致疾病, 但很难预测.
  • 了解结预测对于诊断遗传疾病至关重要.

研究的目的:

  • 开发一个深度神经网络,
  • 识别导致神秘拼接的非编码基因变异.
  • 评估结合改变变异在人类疾病中的作用.

主要方法:

  • 开发了一个深度神经网络模型来预测结合.
  • 分析同义和内基突变的结合改变后果.
  • 使用RNA测序数据验证的预测.
  • 研究了自闭症和智力障碍患者的新突变.

主要成果:

  • 深度神经网络准确地预测了前mRNA序列的结合点.
  • 预测的结合改变突变显示了高的RNA-seq验证率,并且在人群中具有有害性.
  • 在患有自闭症和智力障碍的患者中,具有结合改变后果的de novo突变增强.
  • 在28名患者中,21名患者的RNA测序验证了预测的结合改变突变.

结论:

  • 深度神经网络可以准确地预测拼接并识别神秘拼接.
  • 改变结合的基因变异是罕见的遗传疾病和神经发育疾病的重要原因.
  • 这种方法有助于诊断遗传疾病并了解突变的影响.