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相关概念视频

Hydroboration-Oxidation of Alkenes03:08

Hydroboration-Oxidation of Alkenes

In addition to the oxymercuration–demercuration method, which converts the alkenes to alcohols with Markovnikov orientation, a complementary hydroboration-oxidation method yields the anti-Markovnikov product. The hydroboration reaction, discovered in 1959 by H.C. Brown, involves the addition of a B–H bond of borane to an alkene giving an organoborane intermediate. The oxidation of this intermediate with basic hydrogen peroxide forms an alcohol.
Preparation of Alcohols via Addition Reactions02:15

Preparation of Alcohols via Addition Reactions

Overview
The acid-catalyzed addition of water to the double bond of alkenes is a large-scale industrial method used to synthesize low-molecular-weight alcohols. An acidic atmosphere is required to allow the hydrogen in the water molecule to act as an electrophile and attack the double bond in an alkene. The addition of a proton to the double bond creates a carbocation intermediate. The proton preferentially bonds to the less substituted end of the double bond to create a more stable carbocation...
Acid-Catalyzed Dehydration of Alcohols to Alkenes02:35

Acid-Catalyzed Dehydration of Alcohols to Alkenes

In a dehydration reaction, a hydroxyl group in an alcohol is eliminated along with the hydrogen from an adjacent carbon. Here, the products are an alkene and a molecule of water. Dehydration of alcohols is generally achieved by heating in the presence of an acid catalyst. While the dehydration of primary alcohols requires high temperatures and acid concentrations, secondary and tertiary alcohols can lose a water molecule under relatively mild conditions.
Oxidation of Alcohols02:37

Oxidation of Alcohols

In this lesson, the oxidation of alcohols is discussed in depth. The various reagents used for oxidation of primary and secondary alcohols are detailed, and their mechanism of action is provided.
The process of oxidation in a chemical reaction is observed in any of the three forms:
Preparation of Aldehydes and Ketones from Alcohols, Alkenes, and Alkynes01:33

Preparation of Aldehydes and Ketones from Alcohols, Alkenes, and Alkynes

Aldehydes and ketones are prepared from alcohols, alkenes, and alkynes via different reaction pathways. Alcohols are the most commonly used substrates for synthesizing aldehydes and ketones. The conversion of alcohol to aldehyde, which involves the oxidation process, depends on the class of the alcohol used and the strength of the oxidizing agent. For instance, primary alcohol will form an aldehyde when treated with a weak oxidizing agent; however, it gets over-oxidized to a carboxylic acid in...
Reactions of Aldehydes and Ketones: Baeyer–Villiger Oxidation01:22

Reactions of Aldehydes and Ketones: Baeyer–Villiger Oxidation

Baeyer–Villiger oxidation converts aldehydes to carboxylic acids and ketones to esters. The reaction uses peroxy acids or peracids and is often catalyzed by acid. The reaction is named after its pioneers, Adolf von Baeyer and Victor Villiger. The reaction is achieved by a wide range of peracids such as m-chloroperoxybenzoic acid (mCPBA), perbenzoic acid (C6H5COOOH), peracetic acid (CH3COOOH), hydrogen peroxide (H2O2), and tert-butyl hydroperoxide (t-BuOOH).
The carbonyl center is activated by...

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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使用TCGA数据集对乳腺癌分期进行全面的生物信息学和机器学习分析.

Saurav Chandra Das1,2, Wahia Tasnim3, Humayan Kabir Rana3

  • 1Department of Computer Science and Engineering, Jagannath University, Dhaka-1100, Bangladesh.

Briefings in bioinformatics
|December 10, 2024
PubMed
概括

这项研究使用机器学习和生物信息学对癌症基因组图谱 (TCGA) 数据进行识别乳腺癌生物标志物. 机器学习模型在癌症分期方面取得了高准确性,提高了诊断潜力.

关键词:
在TCGA中,TCGA就是TCGA.乳腺癌 乳腺癌 乳腺癌癌症的分期 癌症的分期机器学习是机器学习.在本体论上,本体论是存在的.转录因子是一种转录因子.

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科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 乳腺癌是一个多元化的全球健康问题,需要先进的分析策略.
  • 癌症基因组图谱 (TCGA) 提供了广泛的基因组数据,对于理解癌症复杂性至关重要.

研究的目的:

  • 利用机器学习和生物信息学来进行乳腺癌的分期,分类和诊断.
  • 识别与乳腺癌亚型和阶段相关的分子特征和潜在生物标志物.

主要方法:

  • 利用了癌症基因组图谱 (TCGA) 的基因表达数据.
  • 应用机器学习算法 (随机森林,XGBoost) 和系统生物学技术.
  • 分析了差异表达的基因,信号通路,蛋白质-蛋白质相互作用和调节网络.

主要成果:

  • 确定了特定的蛋白质 (MYH2,MYL1,MYL2,MYH7) 和微RNA (hsa-let-7d-5p) 作为癌症进展的潜在生物标志物.
  • 实现了癌症分期的高诊断精度:随机森林在97.19%和XGBoost在95.23%.

结论:

  • 生物信息学和机器学习的整合提供了一个强大的方法来发现乳腺癌生物标志物.
  • 这种方法提高了对乳腺癌复杂性的理解,并改善了诊断和分类的临床结果.