Machine learning techniques for identification of carcinogenic mutations, which cause breast adenocarcinoma

Shah, Asghar Ali; Malik, Hafiz Abid Mahmood; Mohammad, AbdulHafeez; Khan, Yaser Daanial; Alourani, Abdullah

doi:10.1038/s41598-022-15533-8

Cited by 16 publications

(8 citation statements)

References 45 publications

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“…In this research the latest dataset for normal and mutated genes sequence of breast adenocarcinoma is used. A similar study is also presented for other types of mutations [ 17 , 18 ] and some testing techniques are also presented in [ 19 , 20 ].…”

Section: Analysis and Discussionmentioning

confidence: 99%

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Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

Shah

Alturise

Alkhalifah

et al. 2022

IJMS

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Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew’s correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen’s kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively.

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Section: Analysis and Discussionmentioning

confidence: 99%

“…This Appendix A is used to discuss and explain the databases used in this study. Normal gene sequences are extracted from asia.ensambl.org [ 16 ] and mutation information of each gene related to breast adenocarcinoma is extracted from intogen.org [ 17 ]. These normal gene sequences and mutation information are extracted through web scraping code.…”

mentioning

confidence: 99%

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

Shah

Alturise

Alkhalifah

et al. 2022

IJMS

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“…GaNB classifies sample data using probability and statistical methods based on the Bayesian theorem, assuming that the feature conditions are independent of each other. GaNB is also a commonly used algorithm ( Yan et al, 2020 ; Shah et al, 2022 ). AdaBoost, XGBoost, and GBDT are all boosting models.…”

Section: Methodsmentioning

confidence: 99%

PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites

Zhang

Wang²,

Xie³

et al. 2023

Front. Genet.

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Background: Pseudouridine (Ψ) is one of the most abundant RNA modifications found in a variety of RNA types, and it plays a significant role in many biological processes. The key to studying the various biochemical functions and mechanisms of Ψ is to identify the Ψ sites. However, identifying Ψ sites using experimental methods is time-consuming and expensive. Therefore, it is necessary to develop computational methods that can accurately predict Ψ sites based on RNA sequence information.Methods: In this study, we proposed a new model called PseU-ST to identify Ψ sites in Homo sapiens (H. sapiens), Saccharomyces cerevisiae (S. cerevisiae), and Mus musculus (M. musculus). We selected the best six encoding schemes and four machine learning algorithms based on a comprehensive test of almost all of the RNA sequence encoding schemes available in the iLearnPlus software package, and selected the optimal features for each encoding scheme using chi-square and incremental feature selection algorithms. Then, we selected the optimal feature combination and the best base-classifier combination for each species through an extensive performance comparison and employed a stacking strategy to build the predictive model.Results: The results demonstrated that PseU-ST achieved better prediction performance compared with other existing models. The PseU-ST accuracy scores were 93.64%, 87.74%, and 89.64% on H_990, S_628, and M_944, respectively, representing increments of 13.94%, 6.05%, and 0.26%, respectively, higher than the best existing methods on the same benchmark training datasets.Conclusion: The data indicate that PseU-ST is a very competitive prediction model for identifying RNA Ψ sites in H. sapiens, M. musculus, and S. cerevisiae. In addition, we found that the Position-specific trinucleotide propensity based on single strand (PSTNPss) and Position-specific of three nucleotides (PS3) features play an important role in Ψ site identification. The source code for PseU-ST and the data are obtainable in our GitHub repository (https://github.com/jluzhangxinrubio/PseU-ST).

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“…Machine learning techniques are applied in numerous areas of medicine like diagnostics. Clonal dynamics and relative frequencies are utilized to develop an antibody clonal examining framework to explore certain antigenic human monoclonal antibodies [5][6][7]. In the various feld of the healthcare system, immunological and biological usage, including infection control, immunization diagnostics, and B-cell detection, is of key signifcance [8,9].…”

Section: Introductionmentioning

confidence: 99%

IGPred‐HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning‐Based Approach

Ali

Alturise

Alkhalifah

et al. 2023

Computational Intelligence and Neuroscience

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Motivation. Immunoglobulin proteins (IGP) (also called antibodies) are glycoproteins that act as B-cell receptors against external or internal antigens like viruses and bacteria. IGPs play a significant role in diverse cellular processes ranging from adhesion to cell recognition. IGP identifications via the in-silico approach are faster and more cost-effective than wet-lab technological methods. Methods. In this study, we developed an intelligent theoretical deep learning framework, “IGPred-HDnet” for the discrimination of IGPs and non-IGPs. Three types of promising descriptors are feature extraction based on graphical and statistical features (FEGS), amphiphilic pseudo-amino acid composition (Amp-PseAAC), and dipeptide composition (DPC) to extract the graphical, physicochemical, and sequential features. Next, the extracted attributes are evaluated through machine learning, i.e., decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), and hierarchical deep network (HDnet) classifiers. The proposed predictor IGPred-HDnet was trained and tested using a 10-fold cross-validation and independent test. Results and Conclusion. The success rates in terms of accuracy (ACC) and Matthew’s correlation coefficient (MCC) of IGPred-HDnet on training and independent dataset (Dtrain Dtest) are ACC = 98.00%, 99.10%, and MCC = 0.958, and 0.980 points, respectively. The empirical outcomes demonstrate that the IGPred-HDnet model efficacy on both datasets using the novel FEGS feature and HDnet algorithm achieved superior predictions to other existing computational models. We hope this research will provide great insights into the large-scale identification of IGPs and pharmaceutical companies in new drug design.

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Machine learning techniques for identification of carcinogenic mutations, which cause breast adenocarcinoma

Cited by 16 publications

References 45 publications

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites

IGPred‐HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning‐Based Approach

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