Modern technologies allow large, complex biomedical datasets to be collected from patient cohorts. These datasets are comprised of both continuous and categorical data (“Mixed Data”), and essential variables may be unobserved in this data due to the complex nature of biomedical phenomena. Causal inference algorithms can identify important relationships from biomedical data; however, handling the challenges of causal inference over mixed data with unmeasured confounders in a scalable way is still an open problem. Despite recent advances into causal discovery strategies that could potentially handle these challenges; individually, no study currently exists that comprehensively compares these approaches in this setting. In this paper, we present a comparative study that addresses this problem by comparing the accuracy and efficiency of different strategies in large, mixed datasets with latent confounders. We experiment with two extensions of the Fast Causal Inference algorithm: a maximum probability search procedure we recently developed to identify causal orientations more accurately, and a strategy which quickly eliminates unlikely adjacencies in order to achieve scalability to high-dimensional data. We demonstrate that these methods significantly outperform the state of the art in the field by achieving both accurate edge orientations and tractable running time in simulation experiments on datasets with up to 500 variables. Finally, we demonstrate the usability of the best performing approach on real data by applying it to a biomedical dataset of HIV-infected individuals.Electronic supplementary materialThe online version of this article (10.1007/s41060-018-0104-3) contains supplementary material, which is available to authorized users.

Studies were carried out to test the responsiveness of dispersed pars intermedia (PI) cells to a number of neurotransmitter substances known to be present in the PI. These substances were tested over an extended dose range. The catecholamines, adrenaline, noradrenaline, and dopamine inactivated PI ACTH at high concentrations; at lower concentrations, they were without effect. Histamine and carbachol had no effect on ACTH release. 5-hydroxytryptamine stimulated ACTH release in a dose-related manner, from 10^–6 to 10^–3M, while having no effect on ACTH release from the pars distalis (PD). We conclude that the release of ACTH from the PI may be controlled by direct serotonergic innervation.

Studies were made to test the responsiveness of dispersed pars intermedia (PI) cells to a number of secretagogues that are known to alter ACTH release from the pars distalis (PD) in vitro. In summary, (a) incubation in high [K+], which will increase ACTH release from the PD, did not alter ACTH release from the PI; (b) a crude extract of rat hypothalamus (HE) increased ACTH release from PD and PI; (c) the effect of HE was not due to its vasopressin content, since pretreatment of the extract with thioglycolic acid did not modify its ACTH-releasing activity and neither lysine nor arginine vasopressin stimulated ACTH release from the PI; and (d) a partially purified CRF preparation, which will stimulate ACTH release from the PD, did not alter ACTH release from the PI. We conclude that the hypothalamus contains a substance(s) that will stimulate ACTH release from the PI and that the ‘secretagogue’ is neither vasopressin nor the same CRF that will stimulate ACTH release from the PD.

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