Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Feng, Xin; Wang, Shaofei; Liu, Quewang; Han, Li; Liu, Jiamei; Cheng, Xu; Yang, Weifeng; Shu, Yayun; Zheng, Weiwei; Yu, Bin; Qi, Mingran; Zhou, Wenyang; Zhou, Fengfeng

doi:10.3791/57738

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…The previous study suggested the existence of multiple solutions with the similarly good performance for a machine learning problem [ 39 ], [ 40 ]. Difference correlation coefficients evaluated the MCI prediction problem from different perspectives.…”

Section: Resultsmentioning

confidence: 99%

“…Other ROIs like lingual gyrus [37] and postcentral cingulate cortex [38] demonstrated to have reduced metabolism levels and were observed in the MCI or AD patients. The previous study suggested the existence of multiple solutions with the similarly good performance for a machine learning problem [39,40]. Difference correlation coefficients evaluated the MCI prediction problem from different perspectives.…”

Section: F Brain Rois Associated With the MCI Predictionmentioning

confidence: 99%

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MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI

Yao

Duan

et al. 2020

IEEE Access

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The neurological disorder mild cognitive impairment (MCI) demonstrates minor impacts on the patient's daily activities and may be ignored as the status of normal aging. But some of the MCI patients may further develop into severe statuses like Alzheimer's disease (AD). The brain functional connectivity network (BFCN) was usually constructed from the resting-state functional magnetic resonance imaging (rs-fMRI) data. This technology has been widely used to detect the neurodegenerative dementia and to reveal the intrinsic mechanism of neural activities. The BFCN edge was usually determined by the pairwise correlation between the brain regions. This study proposed a weighted voting model of multi-source connectivity networks (MuscNet) by integrating multiple BFCNs of different correlation coefficients. Our model was further improved by removing redundant features. The experimental data demonstrated that different BFCNs contributed complementary information to each other and MuscNet outperformed the existing models on detecting MCI patients. The previous study suggested the existence of multiple solutions with similarly good performance for a machine learning problem. The proposed model MuscNet utilized a weighted voting strategy to slightly outperform the existing studies, suggesting an effective way to fuse multiple base models. The reason may need further theoretical investigations about why different base models contribute to each other for the MCI prediction. INDEX TERMS Mild cognitive impairment; Alzheimer's disease; resting-state functional MRI; brain functional connectivity network; multi-source connectivity network; weighted voting model; MuscNet.

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Section: Resultsmentioning

confidence: 99%

Section: F Brain Rois Associated With the MCI Predictionmentioning

confidence: 99%

MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI

Yao

Duan

et al. 2020

IEEE Access

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“…The balanced accuracy [bAcc = (Sn + Sp)/2] was usually utilized to evaluate the classification model without generating bias for a dataset with significantly different numbers of positive and negative samples (Feng et al, 2018). Matthew’s correlation coefficient (MCC) was defined as MCC = (TP × TN-FP × FN)/sqrt[(TP + FP) × (TP + FN) × (TN + FP) × (TN + FN)], where sqrt() is the squared root (Xu et al, 2018; Zhang et al, 2018; Zhao et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers

Feng

Liang

et al. 2019

Front. Genet.

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Patients at different ages have different rates of cell development and metabolisms. As a result, age should be an essential part of how a disease diagnosis model is trained and optimized. Unfortunately, most of the existing studies have not taken age into account. This study demonstrated that disease diagnosis models could be improved by merely applying individual models for patients of different age groups. Both transcriptomes and methylomes of the TCGA breast cancer dataset (TCGA-BRCA) were utilized for the analysis procedure of feature selection and classification. Our experimental data strongly suggested that disease diagnosis modeling should integrate patient age into the whole experimental design.

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“…This study used six performance metrics to evaluate how a classification algorithm performed on the investigated binary classification problem. Five metrics were sensitivity (Sn), specificity (Sp), overall accuracy (Acc), balanced accuracy (bAcc), and Matthews correlation coefficient (MCC) (Feng, et al, 2018;Xu, et al, 2018). These five performance metrics were defined as:…”

Section: Evaluation Methods Of Performancementioning

confidence: 99%

sefOri: selecting the best-engineered sequence features to predict DNA replication origins

et al. 2019

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Motivation Cell divisions start from replicating the double-stranded DNA, and the DNA replication process needs to be precisely regulated both spatially and temporally. The DNA is replicated starting from the DNA replication origins. A few successful prediction models were generated based on the assumption that the DNA replication origin regions have sequence level features like physicochemical properties significantly different from the other DNA regions. Results This study proposed a feature selection procedure to further refine the classification model of the DNA replication origins. The experimental data demonstrated that as large as 26% improvement in the prediction accuracy may be achieved on the yeast Saccharomyces cerevisiae. Moreover, the prediction accuracies of the DNA replication origins were improved for all the four yeast genomes investigated in this study. Availability and implementation The software sefOri version 1.0 was available at http://www.healthinformaticslab.org/supp/resources.php. An online server was also provided for the convenience of the users, and its web link may be found in the above-mentioned web page. Supplementary information Supplementary data are available at Bioinformatics online.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Cited by 6 publications

References 55 publications

MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI

MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI

Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers

sefOri: selecting the best-engineered sequence features to predict DNA replication origins

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