Cancer Classification of Gene Expression Data using Machine Learning Models

Cancer classification is one of the main objectives for analyzing big biological datasets. Machine learning algorithms (MLAs) have been extensively used to accomplish this task. Several popular MLAs are available in the literature to classify new samples into normal or cancer populations. Nevertheless, most of them often yield lower accuracies in the presence of outliers, which leads to incorrect classification of samples. Hence, in this study, we present a robust approach for the efficient and precise classification of samples using noisy GEDs. We examine the performance of the proposed procedure in a comparison of the five popular traditional MLAs (SVM, LDA, KNN, Naïve Bayes, Random forest) using both simulated and real gene expression data analysis. We also considered several rates of outliers (10%, 20%, and 50%). The results obtained from simulated data confirm that the traditional MLAs produce better results through our proposed procedure in the presence of outliers using the proposed modified datasets. The further transcriptome analysis found the significant involvement of these extra features in cancer diseases. The results indicated the performance improvement of the traditional MLAs with our proposed procedure. Hence, we propose to apply the proposed procedure instead of the traditional procedure for cancer classification.

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Predicting the targets of IRF8 and NFATc1 during osteoclast differentiation using the machine learning method framework cTAP

BMC Genomics ◽

10.1186/s12864-021-08159-z ◽

2022 ◽

Vol 23 (1) ◽

Author(s):

Honglin Wang ◽

Pujan Joshi ◽

Seung-Hyun Hong ◽

Peter F. Maye ◽

David W. Rowe ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Gene Expression Data ◽

Target Genes ◽

Osteoclast Differentiation ◽

Target Prediction ◽

Data Sets ◽

Expression Data ◽

Learning Models ◽

Machine Learning Models

Abstract Background Interferon regulatory factor-8 (IRF8) and nuclear factor-activated T cells c1 (NFATc1) are two transcription factors that have an important role in osteoclast differentiation. Thanks to ChIP-seq technology, scientists can now estimate potential genome-wide target genes of IRF8 and NFATc1. However, finding target genes that are consistently up-regulated or down-regulated across different studies is hard because it requires analysis of a large number of high-throughput expression studies from a comparable context. Method We have developed a machine learning based method, called, Cohort-based TF target prediction system (cTAP) to overcome this problem. This method assumes that the pathway involving the transcription factors of interest is featured with multiple “functional groups” of marker genes pertaining to the concerned biological process. It uses two notions, Gene-Present Sufficiently (GP) and Gene-Absent Insufficiently (GA), in addition to log2 fold changes of differentially expressed genes for the prediction. Target prediction is made by applying multiple machine-learning models, which learn the patterns of GP and GA from log2 fold changes and four types of Z scores from the normalized cohort’s gene expression data. The learned patterns are then associated with the putative transcription factor targets to identify genes that consistently exhibit Up/Down gene regulation patterns within the cohort. We applied this method to 11 publicly available GEO data sets related to osteoclastgenesis. Result Our experiment identified a small number of Up/Down IRF8 and NFATc1 target genes as relevant to osteoclast differentiation. The machine learning models using GP and GA produced NFATc1 and IRF8 target genes different than simply using a log2 fold change alone. Our literature survey revealed that all predicted target genes have known roles in bone remodeling, specifically related to the immune system and osteoclast formation and functions, suggesting confidence and validity in our method. Conclusion cTAP was motivated by recognizing that biologists tend to use Z score values present in data sets for the analysis. However, using cTAP effectively presupposes assembling a sizable cohort of gene expression data sets within a comparable context. As public gene expression data repositories grow, the need to use cohort-based analysis method like cTAP will become increasingly important.

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PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning

Genomics ◽

10.1016/j.ygeno.2022.01.001 ◽

2022 ◽

Author(s):

Kazi Ferdous Mahin ◽

Md. Robiuddin ◽

Mujahidul Islam ◽

Shayed Ashraf ◽

Farjana Yeasmin ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Single Cell ◽

Gene Expression Data ◽

Cancer Classification ◽

Expression Data ◽

Rna Seq ◽

Pan Cancer

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Leveraging TCGA gene expression data to build predictive models for cancer drug response

BMC Bioinformatics ◽

10.1186/s12859-020-03690-4 ◽

2020 ◽

Vol 21 (S14) ◽

Cited By ~ 3

Author(s):

Evan A. Clayton ◽

Toyya A. Pujol ◽

John F. McDonald ◽

Peng Qiu

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Gene Expression Data ◽

Predictive Models ◽

Drug Response ◽

Cancer Drug ◽

Expression Data ◽

Classification Methods ◽

Clustering And Classification ◽

Machine Learning Models

Abstract Background Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients’ primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. Results We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study’s limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. Conclusions Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.

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