Despite advancements in understanding the structure and functions of the Calcium Sensing Receptor (CaSR), gaps persist in our knowledge of the specific functions of its residues. In this study, we used phylogeny-based techniques to identify functionally equivalent orthologs of CaSR, predict residue significance, and compute specificity-determining position (SDP) scores to understand its evolutionary basis. The analysis revealed exceptional conservation of the CaSR subfamily, emphasizing the critical role of residues with high SDP scores in receptor activation and pathogenicity. To further enhance the findings, gradient-boosting trees were applied to differentiate between gain- and loss-of-function mutations responsible for hypocalcemia and hypercalcemia. Lastly, we investigated the importance of these mutations in the context of receptor activation dynamics. In summary, through comprehensive exploration of the evolutionary history of the CaSR subfamily, coupled with innovative phylogenetic methodologies, we identified activating and inactivating residues, providing valuable insights into the regulation of calcium homeostasis and its connections to associated disorders.

Predictive performance of machine learning algorithms on related problems can be improved using multitask learning approaches. Rather than performing survival analysis on each data set to predict survival times of cancer patients, we developed a novel multitask approach based on multiple kernel learning (MKL). Our multitask MKL algorithm both works on multiple cancer data sets and integrates cancer-related pathways/gene sets into survival analysis. We tested our algorithm, which is named as Path2MSurv, on the Cancer Genome Atlas data sets analyzing gene expression profiles of 7,655 patients from 20 cancer types together with cancer-specific pathway/gene set collections. Path2MSurv obtained better or comparable predictive performance when benchmarked against random survival forest, survival support vector machine, and single-task variant of our algorithm. Path2MSurv has the ability to identify key pathways/gene sets in predicting survival times of patients from different cancer types.

MOTIVATION: Survival analysis methods that integrate pathways/gene sets into their learning model could identify molecular mechanisms that determine survival characteristics of patients. Rather than first picking the predictive pathways/gene sets from a given collection and then training a predictive model on the subset of genomic features mapped to these selected pathways/gene sets, we developed a novel machine learning algorithm (Path2Surv) that conjointly performs these two steps using multiple kernel learning. RESULTS: We extensively tested our Path2Surv algorithm on 7655 patients from 20 cancer types using cancer-specific pathway/gene set collections and gene expression profiles of these patients. Path2Surv statistically significantly outperformed survival random forest (RF) on 12 out of 20 datasets and obtained comparable predictive performance against survival support vector machine (SVM) using significantly fewer gene expression features (i.e. less than 10% of what survival RF and survival SVM used). AVAILABILITY AND IMPLEMENTATION: Our implementations of survival SVM and Path2Surv algorithms in R are available at https://github.com/mehmetgonen/path2surv together with the scripts that replicate the reported experiments. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.