Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients

Lim, Ashley J. W.; Lim, Li Min; Ooi, Brandon Nick Sern; Koh, Ee Tzun; Tan, Justina Wei Lynn; Chong, Samuel S.; Khor, Chiea Chuen; Tucker‐Kellogg, Lisa; Leong, Khai Pang; Lee, Caroline

doi:10.1016/j.ebiom.2021.103800

Cited by 11 publications

(12 citation statements)

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“…To reduce dimensionality and identify a robust set of SNPs that are resistant to sample size bias [ 35 , 36 ], feature selection using the RFECV algorithm was employed on eight randomly generated variable-sized sample subsets. Thirteen SNP features, with mean feature importance scores between 0.0118 and 0.0612 were commonly identified across all 8 subsets (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Thereafter, the recursive feature elimination with cross-validation (RFECV) algorithm was implemented with a Random Forest classifier estimator using the Scikit-learn Python module to identify an optimal set of important features sufficient for the prediction for each training subset. With the goal of obtaining features with a high stability of importance [ 35 , 36 , 43 ], features commonly selected across all eight subsets were chosen as the final set of features for further evaluation of predictive performances.…”

Section: Methodsmentioning

confidence: 99%

“…Here, as a complement to GWAS, we employ ML, using similar algorithm that we previously reported for the prediction of methotrexate response in RA patients [ 35 , 36 ], to select predictive SNPs that can robustly predict RA across 3 separate unseen test cohorts. We then developed an ML-optimized PRS to facilitate better clinical decision making.…”

Section: Introductionmentioning

confidence: 99%

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Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score

et al. 2023

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Background The popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical application. Rheumatoid Arthritis (RA) was selected as a model disease to demonstrate the robustness of ML in disease prediction as RA is a prevalent chronic inflammatory joint disease with high mortality rates, affecting adults at the economic prime. Early identification of at-risk individuals may facilitate measures to mitigate the effects of the disease. Methods This study employs a robust ML feature selection algorithm to identify single nucleotide polymorphisms (SNPs) that can predict RA from a set of training data comprising RA patients and population control samples. Thereafter, selected SNPs were evaluated for their predictive performances across 3 independent, unseen test datasets. The selected SNPs were subsequently used to generate PRS which was also evaluated for its predictive capacity as a sole feature. Results Through robust ML feature selection, 9 SNPs were found to be the minimum number of features for excellent predictive performance (AUC > 0.9) in 3 independent, unseen test datasets. PRS based on these 9 SNPs was significantly associated with (P < 1 × 10–16) and predictive (AUC > 0.9) of RA in the 3 unseen datasets. A RA ML-PRS calculator of these 9 SNPs was developed (https://xistance.shinyapps.io/prs-ra/) to facilitate individualized clinical applicability. The majority of the predictive SNPs are protective, reside in non-coding regions, and are either predicted to be potentially functional SNPs (pfSNPs) or in high linkage disequilibrium (r2 > 0.8) with un-interrogated pfSNPs. Conclusions These findings highlight the promise of this ML strategy to identify useful genetic features that can robustly predict disease and amenable to translation for clinical application.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score

et al. 2023

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“…Table 7 lists studies incorporating ML for predicting treatment response in RA [ 114 – 127 , 129 , 131 – 134 ].…”

Section: Artificial Intelligence In Ramentioning

confidence: 99%

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

2022

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Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of Sara Momtazmanesh and Ali Nowroozi have contributed equally to this work and share first authorship.

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“…Lim et al [ 11 ] modelled a new feature engineering technique compiling potentially functional coding haplotypes (pfcHap), including ML feature selection to detect biologically meaningful, probably causative genetic factors, that considers effective SNP–SNP interactions in the pfcHap to optimally forecast the methotrexate (MTX) response in RA patients. Ahalya et al [ 12 ], by utilizing modified pre-trained CNN techniques, produced automated patch-related classification of hand Xray images, and then for for automated classification and feature extraction of hand Xray images and, for comparing the efficiency of CNN techniques with linear and non-linear kernels, a customized CNN technique was developed; they finally classified the normal and RA by employing ML methods and framing the hand-crafted feature fusion (SIFT and Customized CNN features).…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence Driven Biomedical Image Classification for Robust Rheumatoid Arthritis Classification

et al. 2022

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Recently, artificial intelligence (AI) including machine learning (ML) and deep learning (DL) models has been commonly employed for the automated disease diagnosis process. AI in biological and biomedical imaging is an emerging area and will be a future trend in the field. At the same time, biomedical images can be used for the classification of Rheumatoid arthritis (RA) diseases. RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects. The disease frequently becomes progressive and decreases physical function, causing articular damage, suffering, and fatigue. After a time, RA causes harm to the cartilage of the joints and bones, weakens the tendons and joints, and finally causes joint destruction. Sensors (thermal infrared camera sensor, accelerometers and wearable sensors) are more commonly employed to collect data for RA. This study develops an Automated Rheumatoid Arthritis Classification using an Arithmetic Optimization Algorithm with Deep Learning (ARAC-AOADL) model. The goal of the presented ARAC-AOADL technique lies in the classification of health disorders depending upon RA and orthopaedics. Primarily, the presented ARAC-AOADL technique pre-processes the input images by median filtering (MF) technique. Then, the ARAC-AOADL technique uses AOA with an enhanced capsule network (ECN) model to produce feature vectors. For RA classification, the ARAC-AOADL technique uses a multi-kernel extreme learning machine (MKELM) model. The experimental result analysis of the ARAC-AOADL technique on a benchmark dataset reported a maximum accuracy of 98.57%. Therefore, the ARAC-AOADL technique can be employed for accurate and timely RA classification.

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Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients

Cited by 11 publications

References 82 publications

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Artificial Intelligence Driven Biomedical Image Classification for Robust Rheumatoid Arthritis Classification

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