Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study

Kang, Eung Shick; Jang, Jongha; Choi, Chang Hwan; Kang, Sang Bum; Bang, Ki Bae; Kim, Tae Oh; Seo, Geom Seog; Myung, Jae; Chun, Jaeyoung; Jung, Yunho; Kim, Hyun Gun; Im, Jong Pil; Kim, Sang Soo; Ahn, Kwang Sung; Lee, Chang Kyun; Kim, Hyo Jong; Kim, Min Suk; Park, Dong Il

doi:10.3390/jcm10040633

Cited by 19 publications

(14 citation statements)

References 40 publications

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“…CatBoost is an improved implementation of gradient enhanced decision trees (GDBT) algorithm developed by Yandex. It has demonstrated excellent performance on many classification and regression tasks ( Kang et al, 2021 ; Liu S. et al, 2021 ; Wang Y. et al, 2021 ). CatBoost was performed via the Python package to build a new optimized classification model (CatBoost model).…”

Section: Methodsmentioning

confidence: 99%

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Liu

Zhao

et al. 2021

Front. Cell Dev. Biol.

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Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

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Section: Methodsmentioning

confidence: 99%

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Liu

Zhao

et al. 2021

Front. Cell Dev. Biol.

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“…Validation data sets in addition to the expected training and testing sets were used in 5% of studies. 12 , 32 , 41 , 72 Another 7 studies trained their models with cross-validation on one data set and tested their method on an external, independent data set. 23 , 29 , 45 , 59 , 61 , 66 , 73 Crohn’s disease data (only) was used in 27 studies, 12 , 17 , 19 , 26–29 , 32 , 37–39 , 41 , 44 , 47–50 , 58 , 60 , 63 , 65 , 74–79 and UC data (only) was used in 15 studies, 25 , 40 , 42 , 46 , 52 , 55 , 59 , 61 , 62 , 64 , 68–70 , 80 , 81 with the remainder ( n = 36) using a mix of CD and UC data, or IBD data as one class.…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation

Stafford

Gosink

Mossotto

et al. 2022

Inflammatory Bowel Diseases

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Background Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time. Methods On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure (“machine learning” OR “artificial intelligence”) AND (“Crohn* Disease” OR “Ulcerative Colitis” OR “Inflammatory Bowel Disease”). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research. Results Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation. Discussion Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.

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“…SHapley Additive exPlanation (SHAP) values were adopted to calculate the contribution of each given feature [ 26 ]. This approach could explain the importance of features for the study outcome, providing visual results for interpreting how the feature value would affect the outcome [ 27 , 28 ]. All of the data preprocessing was performed in R software v4.0.2 (R Foundation, Vienna, Austria), and the related ML analyses were developed in Python 3.7 language.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

et al. 2021

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Background Rheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are autoimmune rheumatic diseases that share a complex genetic background and common clinical features. This study’s purpose was to construct machine learning (ML) models for the genomic prediction of RA and SLE. Methods A total of 2,094 patients with RA and 2,190 patients with SLE were enrolled from the Taichung Veterans General Hospital cohort of the Taiwan Precision Medicine Initiative. Genome-wide single nucleotide polymorphism (SNP) data were obtained using Taiwan Biobank version 2 array. The ML methods used were logistic regression (LR), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and extreme gradient boosting (XGB). SHapley Additive exPlanation (SHAP) values were calculated to clarify the contribution of each SNPs. Human leukocyte antigen (HLA) imputation was performed using the HLA Genotype Imputation with Attribute Bagging package. Results Compared with LR (area under the curve [AUC] = 0.8247), the RF approach (AUC = 0.9844), SVM (AUC = 0.9828), GTB (AUC = 0.9932), and XGB (AUC = 0.9919) exhibited significantly better prediction performance. The top 20 genes by feature importance and SHAP values included HLA class II alleles. We found that imputed HLA-DQA1*05:01, DQB1*0201 and DRB1*0301 were associated with SLE; HLA-DQA1*03:03, DQB1*0401, DRB1*0405 were more frequently observed in patients with RA. Conclusions We established ML methods for genomic prediction of RA and SLE. Genetic variations at HLA-DQA1, HLA-DQB1, and HLA-DRB1 were crucial for differentiating RA from SLE. Future studies are required to verify our results and explore their mechanistic explanation.

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Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study

Cited by 19 publications

References 40 publications

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

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