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Related Concept Videos

Types of RNA01:23

Types of RNA

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Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA...
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Types of RNA01:20

Types of RNA

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Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA Performs Diverse...
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Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

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Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
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Data: Types and Distribution01:19

Data: Types and Distribution

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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
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RNA Editing02:23

RNA Editing

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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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Ribosomal RNA Synthesis02:53

Ribosomal RNA Synthesis

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Ribosome synthesis is a highly complex and coordinated process involving more than 200 assembly factors. The synthesis and processing of ribosomal components occurs not only in the nucleolus but also in the nucleoplasm and the cytoplasm of eukaryotic cells.
Ribosome biogenesis begins with the synthesis of 5S and 45S pre-rRNAs by distinct RNA polymerases. The primary transcripts are extensively processed and modified before they are bound and folded by ribosomal proteins and assembly factors,...
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Cancer Type Prediction and Classification Based on RNA-sequencing Data.

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This study applied machine learning (ML) to classify 33 cancer types using The Cancer Genome Atlas (TCGA) data. Linear support vector machine (SVM) achieved the highest accuracy, demonstrating ML

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    Area of Science:

    • Oncology
    • Bioinformatics
    • Computational Biology

    Background:

    • Pan-cancer analysis is crucial for understanding cancer development.
    • Advancing sequencing technologies provide rich data for cancer research.
    • The Cancer Genome Atlas (TCGA) offers a valuable resource for computational studies.

    Purpose of the Study:

    • To classify thirty-three distinct cancer types using RNA-sequencing data from TCGA.
    • To evaluate and compare the performance of five machine learning algorithms for cancer classification.
    • To identify the most effective machine learning model for pan-cancer classification.

    Main Methods:

    • Utilized RNA-sequencing data from The Cancer Genome Atlas (TCGA).
    • Applied five machine learning algorithms: decision tree (DT), k nearest neighbor (kNN), linear support vector machine (linear SVM), polynomial support vector machine (poly SVM), and artificial neural network (ANN).
    • Performed comparative analysis based on accuracy, training time, precision, recall, and F1-score, including data pre-processing.

    Main Results:

    • Linear support vector machine (SVM) demonstrated the highest classification accuracy at 95.8%.
    • Comparative performance metrics (accuracy, training time, precision, recall, F1-score) were evaluated for all tested algorithms.
    • Data pre-processing techniques were refined to enhance model performance.

    Conclusions:

    • Linear SVM is the most effective classifier for the pan-cancer classification task using TCGA RNA-seq data.
    • Machine learning approaches, particularly linear SVM, show significant potential in analyzing complex cancer genomics data.
    • Further refinement of data pre-processing can optimize ML model performance in cancer research.