Computational Modeling of Gene-Specific Transcriptional Repression, Activation and Chromatin Interactions in Leukemogenesis by LASSO-Regularized Logistic Regression

Steinauer, Nickolas; Zhang, Kevin; Guo, Chun; Zhang, Jinsong

doi:10.1109/tcbb.2021.3078128

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…Therefore, SC-3 is a regression problem. The Lasso, Elastic Net, Ridge ( Steinauer et al, 2021 ), Linear Support Vector Regression (SVR), Gradient Boosting Regressor ( Madhuri, Anuradha & Pujitha, 2019 ), K Neighbors Regressor (KNN R.), Decision Tree Regressor ( El Sayed et al, 2022 ), XGBRegressor ( Tahseen & Danti, 2022 ) and Bayesian Ridge ( Shi, Abdel-Aty & Lee, 2016 ) methods were used for the SC-3 problem.…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity

Işık

Aydın

2023

PeerJ

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Respiratory diseases are among the major health problems causing a burden on hospitals. Diagnosis of infection and rapid prediction of severity without time-consuming clinical tests could be beneficial in preventing the spread and progression of the disease, especially in countries where health systems remain incapable. Personalized medicine studies involving statistics and computer technologies could help to address this need. In addition to individual studies, competitions are also held such as Dialogue for Reverse Engineering Assessment and Methods (DREAM) challenge which is a community-driven organization with a mission to research biology, bioinformatics, and biomedicine. One of these competitions was the Respiratory Viral DREAM Challenge, which aimed to develop early predictive biomarkers for respiratory virus infections. These efforts are promising, however, the prediction performance of the computational methods developed for detecting respiratory diseases still has room for improvement. In this study, we focused on improving the performance of predicting the infection and symptom severity of individuals infected with various respiratory viruses using gene expression data collected before and after exposure. The publicly available gene expression dataset in the Gene Expression Omnibus, named GSE73072, containing samples exposed to four respiratory viruses (H1N1, H3N2, human rhinovirus (HRV), and respiratory syncytial virus (RSV)) was used as input data. Various preprocessing methods and machine learning algorithms were implemented and compared to achieve the best prediction performance. The experimental results showed that the proposed approaches obtained a prediction performance of 0.9746 area under the precision-recall curve (AUPRC) for infection (i.e., shedding) prediction (SC-1), 0.9182 AUPRC for symptom class prediction (SC-2), and 0.6733 Pearson correlation for symptom score prediction (SC-3) by outperforming the best leaderboard scores of Respiratory Viral DREAM Challenge (a 4.48% improvement for SC-1, a 13.68% improvement for SC-2, and a 13.98% improvement for SC-3). Additionally, over-representation analysis (ORA), which is a statistical method for objectively determining whether certain genes are more prevalent in pre-defined sets such as pathways, was applied using the most significant genes selected by feature selection methods. The results show that pathways associated with the ‘adaptive immune system’ and ‘immune disease’ are strongly linked to pre-infection and symptom development. These findings contribute to our knowledge about predicting respiratory infections and are expected to facilitate the development of future studies that concentrate on predicting not only infections but also the associated symptoms.

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Section: Methodsmentioning

confidence: 99%

Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity

Işık

Aydın

2023

PeerJ

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“…LR is a classification algorithm in machine learning, which is widely used in practice because of its good interpretability and high generalization ability [36]. The idea of logistic regression algorithm is derived from linear regression, and in order to solve the quantitative sensitivity problem of linear regression, logistic regression nests a logistic function on the basis of linear regression [37]. Its core idea is that if the output result of linear regression is a continuous value and the range of values is unbounded, the output result can be mapped to the interval [0,1] by a sigmoid function.…”

Section: E Logistic Regression(lr)mentioning

confidence: 99%

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

Chen,

Mei,

Zhou

et al. 2023

IEEE Access

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Breast cancer has replaced lung cancer as the number one cancer among women worldwide. In this paper, we take breast cancer as the research object, and pioneer a hybrid strategy to process the data, and combine the machine learning method to build a more accurate and efficient breast cancer auxiliary diagnosis model. First, the combined sampling method SMOTE-ENN is used to solve the problem of sample imbalance, and the data are standardized to make the data have better separability. Then, the features of the dataset are initially screened using the mutual information method, and further secondary feature selection is performed using the recursive feature elimination method based on the XGBoost algorithm. Thus, the feature dimensionality of the dataset is reduced and the generalization ability of the model is improved. Finally, five different machine learning models are used for classification prediction, the best combination of parameters for each model is found using a grid search method, and the final results of each model are derived using a 10-fold cross-validation method. The experiments are conducted using the Wisconsin Diagnostic Breast Cancer dataset (WDBC), and the results of the study find that after the data are processed by the hybrid strategy, the best prediction results are obtained using the RF model with 99.52% accuracy, which is better than the previous research methods.

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Lung Adenocarcinoma Diagnosis via Regularized Logistic Regression with Mutation Information

Cao

Wang

et al. 2022

Lecture Notes in Electrical Engineering

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Computational Modeling of Gene-Specific Transcriptional Repression, Activation and Chromatin Interactions in Leukemogenesis by LASSO-Regularized Logistic Regression

Cited by 6 publications

References 44 publications

Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity

Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

Lung Adenocarcinoma Diagnosis via Regularized Logistic Regression with Mutation Information

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