Legal determinants of external finance revisited : the inverse relationship between investor protection and societal well-being

This paper investigates relationships between corporate governance traditions and quality of life as measured by a number of widely reported indicators. It provides an empirical analysis of indicators of societal health in developed economies using a classification based on legal traditions. Arguably the most widely cited work in the corporate governance literature has been the collection of papers by La Porta et al. which has shown, inter alia, statistically significant relationships between legal traditions and various proxies for investor protection. We show statistically significant relationships between legal traditions and various proxies for societal health. Our comparative evidence suggests that the interests of investors may not be congruent with the interests of wider society, and that the criteria for judging the effectiveness of approaches to corporate governance should not be restricted to financial metrics

Machine learning and feature selection for drug response prediction in precision oncology applications

In-depth modeling of the complex interplay among multiple omics data measured from cancer cell lines or patient tumors is providing new opportunities toward identification of tailored therapies for individual cancer patients. Supervised machine learning algorithms are increasingly being applied to the omics profiles as they enable integrative analyses among the high-dimensional data sets, as well as personalized predictions of therapy responses using multi-omics panels of response-predictive biomarkers identified through feature selection and cross-validation. However, technical variability and frequent missingness in input “big data” require the application of dedicated data preprocessing pipelines that often lead to some loss of information and compressed view of the biological signal. We describe here the state-of-the-art machine learning methods for anti-cancer drug response modeling and prediction and give our perspective on further opportunities to make better use of high-dimensional multi-omics profiles along with knowledge about cancer pathways targeted by anti-cancer compounds when predicting their phenotypic responses.Peer reviewe