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

BMC Genomics ◽  
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.

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

Leukemia ◽  
2021 ◽  
Author(s):  
Adrián Mosquera Orgueira ◽  
Marta Sonia González Pérez ◽  
José Ángel Díaz Arias ◽  
Beatriz Antelo Rodríguez ◽  
Natalia Alonso Vence ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Multiple Myeloma ◽  
Gene Expression Data ◽  
Survival Prediction ◽  
Expression Data ◽  
Learning Models ◽  
Treatment Optimization ◽  
Machine Learning Models

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

2020 ◽  
Vol 21 (S14) ◽  
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.

scholarly journals Unsupervised Machine Learning for Data Encoding applied to Ovarian Cancer Transcriptomes

2019 ◽  
Author(s):  
Tom M George ◽  
Pietro Lio
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Ovarian Cancer ◽  
Gene Expression Data ◽  
Clustering Algorithm ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Dimensional Manifold ◽  
Expression Data ◽  
Wide Range

AbstractMachine learning algorithms are revolutionising how information can be extracted from complex and high-dimensional data sets via intelligent compression. For example, unsupervised Autoen-coders train a deep neural network with a low-dimensional “bottlenecked” central layer to reconstruct input vectors. Variational Autoencoders (VAEs) have shown promise at learning meaningful latent spaces for text, image and more recently, gene-expression data. In the latter case they have been shown capable of capturing biologically relevant features such as a patients sex or tumour type. Here we train a VAE on ovarian cancer transcriptomes from The Cancer Genome Atlas and show that, in many cases, the latent spaces learns an encoding predictive of cisplatin chemotherapy resistance. We analyse the effectiveness of such an architecture to a wide range of hyperparameters as well as use a state-of-the-art clustering algorithm, t-SNE, to embed the data in a two-dimensional manifold and visualise the predictive power of the trained latent spaces. By correlating genes to resistance-predictive encodings we are able to extract biological processes likely responsible for platinum resistance. Finally we demonstrate that variational autoencoders can reliably encode gene expression data contaminated with significant amounts of Gaussian and dropout noise, a necessary feature if this technique is to be applicable to other data sets, including those in non-medical fields.

scholarly journals Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Elisabetta Manduchi ◽  
Weixuan Fu ◽  
Joseph D. Romano ◽  
Stefano Ruberto ◽  
Jason H. Moore
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Big Data ◽  
Gene Expression Data ◽  
Data Sets ◽  
Biomedical Data ◽  
Training Procedure ◽  
Expression Data ◽  
Differential Gene ◽  
Automated Machine Learning

Abstract Background A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. Results We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids ‘leakage’ during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj. Conclusions In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

scholarly journals Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

2020 ◽  
Author(s):  
Elisabetta Manduchi ◽  
Weixuan Fu ◽  
Joseph D. Romano ◽  
Stefano Ruberto ◽  
Jason H. Moore
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Big Data ◽  
Gene Expression Data ◽  
Data Sets ◽  
Biomedical Data ◽  
Training Procedure ◽  
Expression Data ◽  
Differential Gene ◽  
Automated Machine Learning

AbstractBackgroundA typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis.ResultsWe present an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids ‘leakage’ during the cross-validation training procedure. We then describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj.ConclusionsIn this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

scholarly journals Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2927 ◽  
Author(s):  
Linh Nguyen ◽  
Cuong C Dang ◽  
Pedro J. Ballester
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Drug Sensitivity ◽  
Single Gene ◽  
Cancer Cell Line ◽  
Expression Data ◽  
Gene Markers ◽  
Pan Cancer ◽  
Machine Learning Models

Background:Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets, such as those by Genomics of Drug Sensitivity in Cancer (GDSC) consortium, were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data.Methods:Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC50measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than standard k-fold cross-validation.Results and Discussion:Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by the multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG.Conclusions:Thanks to this unbiased validation, we now know that this type of models can predictin vitrotumour response to some of these drugs. These models can thus be further investigated onin vivotumour models. R code to facilitate the construction of alternative machine learning models and their validation in the presented benchmark is available athttp://ballester.marseille.inserm.fr/gdsc.transcriptomicDatav2.tar.gz.

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