An Accurate Regression of Developmental Stages for Breast Cancer Based on Transcriptomic Biomarkers

Feng, Xin; Zhang, Ruochi; Liu, Minge; Liu, Quewang; Li, Fēi; Yan, Zhimin; Zhou, Fengfeng

doi:10.2217/bmm-2018-0305

Cited by 11 publications

(11 citation statements)

References 24 publications

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“…Ridge regression (RR) is a biased estimation regression method for collinear data analysis, which is an improvement of the least square estimation method [45][46][47][48][49][50]. It gives up the unbiased advantage of the least square and gains the stability of the regression coefficient at the cost of losing part of the information and reducing the fitting accuracy [48]. The multiple regression model can be expressed as follows:…”

Section: Ridge Regressionmentioning

confidence: 99%

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Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Qin

Tian

et al. 2021

Crystals

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Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO3 with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, including support vector regression based on radial basis kernel function (SVM-RBF), ridge regression (RR), random forest (RF), and back propagation neural network (BPNN), are established to predict the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy of perovskite materials. Combined with the fitting diagrams of the predicted values and DFT calculated values, the results show that SVM-RBF has a smaller bias in predicting the crystal volume. RR has a smaller bias in predicting the thermodynamic stability. RF has a smaller bias in predicting the formation energy, crystal volume, and thermodynamic stability. BPNN has a smaller bias in predicting the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy. Obviously, different machine learning algorithms exhibit different sensitivity to data sample distribution, indicating that we should select different algorithms to predict different performance parameters of perovskite materials.

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Section: Ridge Regressionmentioning

confidence: 99%

“…If the independent variables have multiple collinearities, the matrix X T X is singular and the eigenvalue is very small; this causes the elements on the diagonal of the matrix X T X −1 to be very large and makes the parameter estimation extremely unstable [48].…”

Section: Ridge Regressionmentioning

confidence: 99%

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Qin

Tian

et al. 2021

Crystals

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“…One more redundancy-removal step was conducted to further refine the feature subset obtained in the above step. The iterative exclusion of the feature with the least performance decrease was carried out, which was the same as the backFS strategy in the other studies (Feng et al, 2019;Zhang et al, 2019). The performance was calculated by the 10-fold cross validation strategy.…”

Section: Feature Selection Algorithm Ageguessmentioning

confidence: 99%

“…The proposed algorithm AgeGuess further removed the redundancies in the methylated features by the function backFS (Feng et al, 2019;Zhang et al, 2019). The 750 methylation features chosen in the above step was iteratively evaluated and one feature was removed per iteration if its removal generated the least contribution to the age prediction performance metrics EI.…”

Section: Optimizing the Proposed Algorithm Ageguessmentioning

confidence: 99%

AgeGuess, a Methylomic Prediction Model for Human Ages

Gao

Liu

Song

et al. 2020

Front. Bioeng. Biotechnol.

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Aging was a biological process under regulations from both inherited genetic factors and various molecular modifications within cells during the lifespan. Multiple studies demonstrated that the chronological age may be accurately predicted using the methylomic data. This study proposed a three-step feature selection algorithm AgeGuess for the age regression problem. AgeGuess selected 107 methylomic features as the gender-independent age biomarkers and the Support Vector Regressor (SVR) model using these biomarkers achieved 2.0267 in the mean absolute deviation (MAD) compared with the real chronological ages. Another regression algorithm Ridge achieved a slightly better MAD 1.9859 using the same biomarkers. The gender-independent age prediction models may be further improved by establishing two gender-specific models. And it's interesting to observe that there were only two methylation biomarkers shared by the two gender-specific biomarker sets and these two biomarkers were within the two known age-associated biomarker genes CALB1 and KLF14.

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“…These measurements were used in various prediction models like the DNA and RNA functional elements (He et al, 2018;Feng et al, 2019). And they were calculated using the 10-fold cross-validation (10FCV) strategy as similar in Ye et al (2017) and Zhao et al (2018).…”

Section: Performance Measurementsmentioning

confidence: 99%

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

et al. 2020

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Rheumatoid arthritis (RA) is a common autoimmune disorder influenced by both genetic and environmental factors. To investigate possible contributions of DNA methylation to the etiology of RA with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. This study hypothesized that methylomic biomarkers might facilitate accurate RA detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for detecting RA patients using the methylomic data of the peripheral blood samples. The best model achieved 100.00% in accuracy (Acc) with 81 methylomic biomarkers and a 10-fold cross-validation (10FCV) strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of RA. It is also interesting to observe that many of the detected biomarkers were from chromosome Y, supporting the knowledge that RA has a significant gender discrepancy.

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An Accurate Regression of Developmental Stages for Breast Cancer Based on Transcriptomic Biomarkers

Cited by 11 publications

References 24 publications

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

AgeGuess, a Methylomic Prediction Model for Human Ages

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

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