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Related Concept Videos

Cancer02:18

Cancer

51.0K
Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
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Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

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Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
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Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
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Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

8.4K
Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
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Tumor Progression02:07

Tumor Progression

6.7K
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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Loss of Tumor Suppressor Gene Functions01:12

Loss of Tumor Suppressor Gene Functions

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Tumor suppressor genes are normal genes that can slow down cell division, repair DNA mistakes, or program the cells for apoptosis in case of irreparable damage. Hence, they play an essential role in preventing the proliferation of damaged cells.
When the tumor suppressor genes develop mutations or are lost, cells start growing out of control, leading to cancer. However, a single functional copy of the tumor suppressor gene is enough for the cells to maintain their normal functions and cell...
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Updated: Oct 25, 2025

Simple and Rapid Method to Obtain High-quality Tumor DNA from Clinical-pathological Specimens Using Touch Imprint Cytology
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Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection.

Meenu Gupta1, Hao Wu2, Simrann Arora3

  • 1Department of Computer Science and Engineering, Chandigarh University, Ajitgarh, Punjab, India.

Journal of Healthcare Engineering
|August 9, 2021
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Summary

This study developed a Natural Language Processing (NLP) multiclass classifier to distinguish driver from neutral genetic mutations in cancer. The Recurrent Neural Network (RNN) model achieved the highest accuracy, improving automated cancer mutation classification.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Medical Informatics

Background:

  • Cancer tumors harbor numerous genetic mutations, complicating the identification of driver mutations crucial for tumor growth.
  • Manual classification of genetic mutations based on clinical evidence is time-consuming and subjective, hindering efficient cancer research and treatment.
  • Existing methods lack automated approaches for classifying genetic mutations into distinct clinical evidence categories.

Purpose of the Study:

  • To propose and evaluate a multiclass classifier using Natural Language Processing (NLP) techniques for automated classification of genetic mutations based on clinical text descriptions.
  • To compare the performance of various text transformation and machine learning models in distinguishing driver mutations from neutral genetic mutations.
  • To leverage clinical evidence to improve the accuracy and efficiency of cancer mutation analysis.

Main Methods:

  • Utilized a dataset from Memorial Sloan Kettering Cancer Center (MSKCC) containing genetic mutation descriptions.
  • Applied text transformation models: CountVectorizer, TfidfVectorizer, and Word2Vec.
  • Implemented machine learning classifiers: Logistic Regression (LR), Random Forest (RF), XGBoost (XGB), and a Recurrent Neural Network (RNN).
  • Evaluated classifier performance using accuracy scores derived from confusion matrices.

Main Results:

  • The Recurrent Neural Network (RNN) model demonstrated superior performance compared to other evaluated classifiers.
  • The RNN model achieved the highest accuracy of 70% in classifying genetic mutations based on clinical evidence.
  • Empirical results indicate the potential of deep learning approaches for automated mutation classification.

Conclusions:

  • The proposed NLP-based multiclass classifier, particularly the RNN model, offers a promising automated solution for distinguishing driver from neutral genetic mutations.
  • This approach can significantly reduce the manual effort and time required by pathologists, accelerating cancer research.
  • Further development and validation of deep learning models can enhance the precision of cancer mutation analysis and support clinical decision-making.