Fast and interpretable genomic data analysis using multiple approximate kernel learning.

MOTIVATION: Dataset sizes in computational biology have been increased drastically with the help of improved data collection tools and increasing size of patient cohorts. Previous kernel-based machine learning algorithms proposed for increased interpretability started to fail with large sample sizes, owing to their lack of scalability. To overcome this problem, we proposed a fast and efficient multiple kernel learning (MKL) algorithm to be particularly used with large-scale data that integrates kernel approximation and group Lasso formulations into a conjoint model. Our method extracts significant and meaningful information from the genomic data while conjointly learning a model for out-of-sample prediction. It is scalable with increasing sample size by approximating instead of calculating distinct kernel matrices. RESULTS: To test our computational framework, namely, Multiple Approximate Kernel Learning (MAKL), we demonstrated our experiments on three cancer datasets and showed that MAKL is capable to outperform the baseline algorithm while using only a small fraction of the input features. We also reported selection frequencies of approximated kernel matrices associated with feature subsets (i.e. gene sets/pathways), which helps to see their relevance for the given classification task. Our fast and interpretable MKL algorithm producing sparse solutions is promising for computational biology applications considering its scalability and highly correlated structure of genomic datasets, and it can be used to discover new biomarkers and new therapeutic guidelines. AVAILABILITY AND IMPLEMENTATION: MAKL is available at https://github.com/begumbektas/makl together with the scripts that replicate the reported experiments. MAKL is also available as an R package at https://cran.r-project.org/web/packages/MAKL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning as a clinical decision support tool for patients with acromegaly.

OBJECTIVE: To develop machine learning (ML) models that predict postoperative remission, remission at last visit, and resistance to somatostatin receptor ligands (SRL) in patients with acromegaly and to determine the clinical features associated with the prognosis. METHODS: We studied outcomes using the area under the receiver operating characteristics (AUROC) values, which were reported as the performance metric. To determine the importance of each feature and easy interpretation, Shapley Additive explanations (SHAP) values, which help explain the outputs of ML models, are used. RESULTS: One-hundred fifty-two patients with acromegaly were included in the final analysis. The mean AUROC values resulting from 100 independent replications were 0.728 for postoperative 3 months remission status classification, 0.879 for remission at last visit classification, and 0.753 for SRL resistance status classification. Extreme gradient boosting model demonstrated that preoperative growth hormone (GH) level, age at operation, and preoperative tumor size were the most important predictors for early remission; resistance to SRL and preoperative tumor size represented the most important predictors of remission at last visit, and postoperative 3-month insulin-like growth factor 1 (IGF1) and GH levels (random and nadir) together with the sparsely granulated somatotroph adenoma subtype served as the most important predictors of SRL resistance. CONCLUSIONS: ML models may serve as valuable tools in the prediction of remission and SRL resistance.

PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning.

BACKGROUND: Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meaningful knowledge from the data simultaneously during the learning process is a difficult task considering the high-dimensional and highly correlated nature of genomic datasets. Thus, there is a need for models that not only predict tumour volume from gene expression data of patients but also use prior information coming from pathway/gene sets during the learning process, to distinguish molecular mechanisms which play crucial role in tumour progression and therefore, disease prognosis. RESULTS: In this study, instead of initially choosing several pathways/gene sets from an available set and training a model on this previously chosen subset of genomic features, we built a novel machine learning algorithm, PrognosiT, that accomplishes both tasks together. We tested our algorithm on thyroid carcinoma patients using gene expression profiles and cancer-specific pathways/gene sets. Predictive performance of our novel multiple kernel learning algorithm (PrognosiT) was comparable or even better than random forest (RF) and support vector regression (SVR). It is also notable that, to predict tumour volume, PrognosiT used gene expression features less than one-tenth of what RF and SVR algorithms used. CONCLUSIONS: PrognosiT was able to obtain comparable or even better predictive performance than SVR and RF. Moreover, we demonstrated that during the learning process, our algorithm managed to extract relevant and meaningful pathway/gene sets information related to the studied cancer type, which provides insights about its progression and aggressiveness. We also compared gene expressions of the selected genes by our algorithm in tumour and normal tissues, and we then discussed up- and down-regulated genes selected by our algorithm while learning, which could be beneficial for determining new biomarkers.

Effect of metformin on thyroid cancer risk in patients with acromegaly: A preliminary observational study.

PURPOSE: To evaluate the role of metformin on thyroid cancer risk in patients with acromegaly. METHODS: Medical charts of 534 patients with acromegaly that were followed-up between 1983 and 2019 were reviewed. Patients with follow-up duration at least 6 months were included. Cohort entry was defined as first visit date. The date of each case's thyroid cancer diagnosis was defined as index date. Patients were followed until the index date, death, or last visit date, whichever came first. Nested case-control study design was selected to evaluate the association between metformin and the thyroid cancer risk in patients with acromegaly. RESULTS: 291 patients with acromegaly were included into final analysis. The mean age at acromegaly diagnosis was 42.3 ± 1.3 years. The median follow-up duration was 76 [34-132] months. Among 291 patients, 13 patients (4.5%) had thyroid cancer. Thirty-one percent (n = 92) of the patients used metformin for 6 months or longer. One standard deviation (SD) increase in average growth hormone increased the odds of having thyroid cancer by 1.164 folds (p = 0.017). One SD increase of the average insulin-like growth factor 1 to upper limit of normal ratio increased the odds of having thyroid cancer by 1.201 folds (p = 0.004). If a patient used metformin for at least 6 months, the odds to have thyroid cancer was decreased, multiplied by 0.62 with a 95% confidence interval of [0.47, 0.83] (p = 0.0013). The risk of thyroid cancer decreased with increasing duration of metformin use. CONCLUSION: Metformin may decrease the thyroid cancer risk in patients with acromegaly.