Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection

2021 ◽  
Author(s):  
Arzu Cakir ◽  
Melisa Tuncer ◽  
Hilal Taymaz-Nikerel ◽  
Ozlem Ulucan
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Random Forest ◽  
Side Effect ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Drug Induced

scholarly journals A Robust Gene selection Method for Microarray-based Cancer Classification

2010 ◽  
Vol 9 ◽  
pp. CIN.S3794 ◽  
Author(s):  
Xiaosheng Wang ◽  
Osamu Gotoh
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Information Gain ◽  
Expression Profiles ◽  
Feature Selection Method ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Selection Method ◽  
Chi Square

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.

Improved Feature Selection by Incorporating Gene Similarity into the LASSO

2012 ◽  
Vol 3 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Christopher E. Gillies ◽  
Xiaoli Gao ◽  
Nilesh V. Patel ◽  
Mohammad-Reza Siadat ◽  
George D. Wilson
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Personalized Medicine ◽  
Objective Function ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Genetic Profile ◽  
Data Set ◽  
Coordinate Descent Algorithm ◽  
Gene Similarity

Personalized medicine is customizing treatments to a patient’s genetic profile and has the potential to revolutionize medical practice. An important process used in personalized medicine is gene expression profiling. Analyzing gene expression profiles is difficult, because there are usually few patients and thousands of genes, leading to the curse of dimensionality. To combat this problem, researchers suggest using prior knowledge to enhance feature selection for supervised learning algorithms. The authors propose an enhancement to the LASSO, a shrinkage and selection technique that induces parameter sparsity by penalizing a model’s objective function. Their enhancement gives preference to the selection of genes that are involved in similar biological processes. The authors’ modified LASSO selects similar genes by penalizing interaction terms between genes. They devise a coordinate descent algorithm to minimize the corresponding objective function. To evaluate their method, the authors created simulation data where they compared their model to the standard LASSO model and an interaction LASSO model. The authors’ model outperformed both the standard and interaction LASSO models in terms of detecting important genes and gene interactions for a reasonable number of training samples. They also demonstrated the performance of their method on a real gene expression data set from lung cancer cell lines.

scholarly journals Diverse approaches to predicting drug-induced liver injury using gene-expression profiles

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
G. Rex Sumsion ◽  
Michael S. Bradshaw ◽  
Jeremy T. Beales ◽  
Emi Ford ◽  
Griffin R. G. Caryotakis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Injury ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Correlation-based Gene Selection and Classification Using Taguchi-BPSO

2010 ◽  
Vol 49 (03) ◽  
pp. 254-268 ◽  
Author(s):  
C.-S. Yang ◽  
K.-C. Wu ◽  
C.-H. Yang ◽  
L.-Y. Chuang
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Microarray Data ◽  
Error Rate ◽  
Gene Expression Analysis ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Classification Error ◽  
Classification Error Rate

Summary Background: Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems, and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and small sample size, which makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. Objective: The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Method: In this paper, correlation-based feature selection (CFS) and Taguchi-binary particle swarm optimization (TBPSO) were combined into a hybrid method, and the K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Results: Experimental results show that this hybrid method effectively simplifies feature selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. Conclusion: The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.

scholarly journals Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shengqiao Gao ◽  
Lu Han ◽  
Dan Luo ◽  
Gang Liu ◽  
Zhiyong Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Large Scale ◽  
Prediction Models ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Genetic Profile ◽  
Drug Induced ◽  
Drug Mechanism

Abstract Background Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression databases. However, due to the lack of code-free and user friendly applications, it is not easy for biologists and pharmacologists to model MOAs with state-of-art deep learning approach. Results In this work, a newly developed online collaborative tool, Genetic profile-activity relationship (GPAR) was built to help modeling and predicting MOAs easily via deep learning. The users can use GPAR to customize their training sets to train self-defined MOA prediction models, to evaluate the model performances and to make further predictions automatically. Cross-validation tests show GPAR outperforms Gene set enrichment analysis in predicting MOAs. Conclusion GPAR can serve as a better approach in MOAs prediction, which may facilitate researchers to generate more reliable MOA hypothesis.

scholarly journals DeepSide: A Deep Learning Framework for Drug Side Effect Prediction

2019 ◽  
Author(s):  
Onur Can Uner ◽  
Ramazan Gokberk Cinbis ◽  
Oznur Tastan ◽  
A. Ercument Cicek
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Side Effect ◽  
State Of The Art ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Ground Truth ◽  
The State ◽  
Specific Information ◽  
Context Specific

AbstractDrug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of cell line gene expression data perturbed by different drugs and creates a knowledge base for context specific features. The state-of-the-art approach that aims at using context specific information relies on only the high-quality experiments in LINCS L1000 and discards a large portion of the experiments. In this study, our goal is to boost the prediction performance by utilizing this data to its full extent. We experiment with 5 deep learning architectures. We find that a multi-modal architecture produces the best predictive performance among multi-layer perceptron-based architectures when drug chemical structure (CS), and the full set of drug perturbed gene expression profiles (GEX) are used as modalities. Overall, we observe that the CS is more informative than the GEX. A convolutional neural network-based model that uses only SMILES string representation of the drugs achieves the best results and provides 13.0% macro-AUC and 3.1% micro-AUC improvements over the state-of-the-art. We also show that the model is able to predict side effect-drug pairs that are reported in the literature but was missing in the ground truth side effect dataset. DeepSide is available at http://github.com/OnurUner/DeepSide.

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