Machine learning methods for prediction of cancer driver genes: a survey paper

Andrades, Renan Soares de; Recamonde‐Mendoza, Mariana

doi:10.1093/bib/bbac062

Cited by 17 publications

(12 citation statements)

References 130 publications

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“…Machine learning is used in many applications for prediction and detection including can-cer driver genes [12]- [14]. There exist review articles covering the details of different methods with the pros and cons of the available methods [15]- [17]. This section presents methods more related to the proposed model.…”

Section: Literature Reviewmentioning

confidence: 99%

IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning

et al. 2023

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The development of high throughput sequencing technologies i.e. Next Generation Sequencing (NGS) is revolutionizing the exploration of cancer. Though sequence datasets are highly complex, mutation can occur randomly in DNA or RNA sequences that can make cells sicker or less fit. The unusual growth and behavior of genes in cells cause cancer. Cancer-driver gene cells grow when mutation occurs. Identification of cancer driver genes is a critical and challenging issue for researchers. In the proposed work, initially, robust features are extracted from the sequence dataset through Position Relative Incidence Matrix (PRIM) integrated with Accumulative Absolute Position Incidence Vector (AAPIV) generation. PRIM and AAPIV convert the single-dimensional sequence data into 2-dimensional numeric data. Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) are used to train the model. The proposed model is validated with different validation methods i.e., independent testing, k-fold crossvalidation, self-consistency, and jackknife testing. The proposed model predicts whether the given primary structure corresponds to cancer driver genes or not. Results analyses show 95%, 92%, and 69% accuracy on RF, Artificial Neural Networks (ANN), and SVM respectively. The comparative analysis with existing state-of-the-art models i.e., 20/20+ and Multimodal Deep Neural Network by integrating Multi-dimensional Data (NDNNMD) shows that the proposed model outperforms the existing techniques.

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Section: Literature Reviewmentioning

confidence: 99%

IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning

et al. 2023

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“…It builds analytical models by analyzing existing data, and learns by observations with the primary purpose of making decisions on its own in the future. Models can be trained and automated to analyze multidimensional data for classification, clustering and predictive purposes [26,27]. Classification is a supervised learning approach in machine learning which is used to analyze a dataset provided and construct a model to divide data into a unique set of categories [28].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma

Yap

Goh

Lim

et al. 2023

Cancers

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Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79–0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC.

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“…[12, 13]). Interesting reviews on these methods can be found in [14, 8, 15]. Most current methods for discovering cancer drivers do not consider the temporal information of the disease, nor do they include suitable procedures for testing the causal nature of cancer progression.…”

Section: Introductionmentioning

confidence: 99%

Identifying cooperative genes causing cancer progression with dynamic causal inference

Cifuentes-Bernal,

Liu,

et al. 2023

Preprint

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It is well known that some gene aberrations can cause cancer by disrupting the delicate balance of critical biological processes at the cellular level. Such aberrations are rare and are not limited to gene mutations alone and hence are difficult to be identified from data. Moreover, focusing exclusively on gene aberrations neglects other significant aspects of cancer development such as the fact that cancer occurs due to gene interactions evolving as a dynamical system. Therefore, expanding our knowledge about the dynamics of genetic mechanisms that cause cancer is crucial for a comprehensive understanding of cancer development. In this paper, a novel causal method for identifying collaborative networks of cancer drivers based on dynamic system analysis is introduced. The method integrates the temporal dimension of the data throughout cancer progression and provides a way of testing for the causality of candidate genes in cancer. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method systematically identifiesbona fidedriver genes and detects sets of genes strongly linked to cancer progression. The results suggest that our method can discover mutated and non mutated drivers of cancer to provide a comprehensive view of cancer development.R package implementing our approach as well as scripts for the experiments and datasets used can be found athttps://github.com/AndresMCB/DynamicCancerDriverKM.

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Machine learning methods for prediction of cancer driver genes: a survey paper

Cited by 17 publications

References 130 publications

IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning

IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning

Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma

Identifying cooperative genes causing cancer progression with dynamic causal inference

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