An Ensembled SVM Based Approach for Predicting Adverse Drug Reactions

Joshi, Pratik; Masilamani, V.; Ramesh, Raj

doi:10.2174/1574893615999200707141420

Cited by 23 publications

(11 citation statements)

References 26 publications

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“…Four classic metrics, including accuracy, recall, precision, and F1 measure, were used to quantify the performance of the model, which are defined as follows [ 51 – 53 ]: where , , , and represent the numbers of true positives, true negatives, false positives, and false negatives, respectively.…”

Section: Methodsmentioning

confidence: 99%

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery

Wang

Zhang

et al. 2023

Cell Biosci

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Background The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate. Results Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28–32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server (http://bioinfor.imu.edu.cn/placenta). Conclusion Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.

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Section: Methodsmentioning

confidence: 99%

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery

Wang

Zhang

et al. 2023

Cell Biosci

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“…Support Vector Machine-Recursive Feature Elimination (SVM-RFE) is a sequence backward selection algorithm based on the maximum interval principle of Support Vector Machine (SVM) ( Guyon et al, 2002 ; Tang et al, 2018 ; Cheng et al, 2019 ; Yang et al, 2020 ; Liu et al, 2021b ; Joshi et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of miRNA Signature Associated With Erectile Dysfunction in Type 2 Diabetes Mellitus by Support Vector Machine-Recursive Feature Elimination

Zhao

Zhong

et al. 2021

Front. Genet.

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Diabetic mellitus erectile dysfunction (DMED) is one of the most common complications of diabetes mellitus (DM), which seriously affects the self-esteem and quality of life of diabetics. MicroRNAs (miRNAs) are endogenous non-coding RNAs whose expression levels can affect multiple cellular processes. Many pieces of studies have demonstrated that miRNA plays a role in the occurrence and development of DMED. However, the exact mechanism of this process is unclear. Hence, we apply miRNA sequencing from blood samples of 10 DMED patients and 10 DM controls to study the mechanisms of miRNA interactions in DMED patients. Firstly, we found four characteristic miRNAs as signature by the SVM-RFE method (hsa-let-7E-5p, hsa-miR-30 days-5p, hsa-miR-199b-5p, and hsa-miR-342–3p), called DMEDSig-4. Subsequently, we correlated DMEDSig-4 with clinical factors and further verified the ability of these miRNAs to classify samples. Finally, we functionally verified the relationship between DMEDSig-4 and DMED by pathway enrichment analysis of miRNA and its target genes. In brief, our study found four key miRNAs, which may be the key influencing factors of DMED. Meanwhile, the DMEDSig-4 could help in the development of new therapies for DMED.

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“…In recent years, computational methods, especially deep learning, have emerged as a promising avenue for drug discovery by combining domain knowledge and data-driven learning ( Button et al , 2019 ; Camacho et al , 2018 ; Hoffman et al , 2022 ; Joshi et al , 2021 ; Kotsias et al , 2020 ; LeCun et al , 2015 ; Lv et al , 2022 ; Su et al , 2019 ; von Lilienfeld and Burke, 2020 ). Among them, deep generative methods have been shown to be ideal for drug candidate discovery ( Gómez-Bombarelli et al , 2018 ; Putin et al , 2018 ; Ru et al , 2020 ; Sanchez-Lengeling and Aspuru-Guzik, 2018 ; Segler et al , 2018b ; Shaker et al , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Potent antibiotic design via guided search from antibacterial activity evaluations

Chen

Gao

2023

Bioinformatics

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Motivation The emergence of drug-resistant bacteria makes the discovery of new antibiotics an urgent issue, but finding new molecules with the desired antibacterial activity is an extremely difficult task. To address this challenge, we established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules. Results By designing the antibacterial activity latent space and guiding the optimization of functional compounds based on this space, the model MDAGS can generate novel compounds with desirable antibacterial activity without the need for extensive expensive and time-consuming evaluations. Compared with existing antibiotics, candidate antibacterial compounds generated by MDAGS always possessed significantly better antibacterial activity and ensured high similarity. Furthermore, although without explicit constraints on similarity to known antibiotics, these candidate antibacterial compounds all exhibited the highest structural similarity to antibiotics of expected function in the DrugBank database query. Overall, our approach provides a viable solution to the problem of bacterial drug resistance. Availability Code of the model and datasets can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MDAGS). Supplementary information Supplementary data are available at Bioinformatics online.

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An Ensembled SVM Based Approach for Predicting Adverse Drug Reactions

Cited by 23 publications

References 26 publications

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery

Identification of miRNA Signature Associated With Erectile Dysfunction in Type 2 Diabetes Mellitus by Support Vector Machine-Recursive Feature Elimination

Potent antibiotic design via guided search from antibacterial activity evaluations

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