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Related Concept Videos

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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Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.

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
Summary
This summary is machine-generated.

This study uses machine learning and bioinformatics on The Cancer Genome Atlas (TCGA) data to identify breast cancer biomarkers. Machine learning models achieved high accuracy in cancer staging, improving diagnostic potential.

Keywords:
TCGAbreast cancercancer stagingmachine learningontologytranscription factors

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Breast cancer is a diverse global health issue requiring advanced analytical strategies.
  • The Cancer Genome Atlas (TCGA) provides extensive genomic data crucial for understanding cancer complexity.

Purpose of the Study:

  • To leverage machine learning and bioinformatics for breast cancer staging, classification, and diagnosis.
  • To identify molecular signatures and potential biomarkers associated with breast cancer subtypes and stages.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) gene expression data.
  • Applied machine learning algorithms (Random Forest, XGBoost) and systems biology techniques.
  • Analyzed differentially expressed genes, signaling pathways, protein-protein interactions, and regulatory networks.

Main Results:

  • Identified specific proteins (MYH2, MYL1, MYL2, MYH7) and microRNAs (hsa-let-7d-5p) as potential biomarkers for cancer progression.
  • Achieved high diagnostic accuracy for cancer staging: Random Forest at 97.19% and XGBoost at 95.23%.

Conclusions:

  • The integration of bioinformatics and machine learning offers a powerful approach to discovering breast cancer biomarkers.
  • This methodology enhances the understanding of breast cancer complexity and improves clinical outcomes in diagnosis and categorization.