Brown University

About: Brown University is a education organization based out in Providence, Rhode Island, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 35778 authors who have published 90896 publications receiving 4471489 citations. The organization is also known as: brown.edu & Brown.

Latent semantic models for collaborative filtering

Abstract: Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, that is, a database of available user preferences. In this article, we describe a new family of model-based algorithms designed for this task. These algorithms rely on a statistical modelling technique that introduces latent class variables in a mixture model setting to discover user communities and prototypical interest profiles. We investigate several variations to deal with discrete and continuous response variables as well as with different objective functions. The main advantages of this technique over standard memory-based methods are higher accuracy, constant time prediction, and an explicit and compact model representation. The latter can also be used to mine for user communitites. The experimental evaluation shows that substantial improvements in accucracy over existing methods and published results can be obtained.

The Immunological Genome Project: networks of gene expression in immune cells

Abstract: nology is an ideal field for the application of systems approaches, with its detailed descriptions of cell types (over 200 immune cell types are defined in the scope of the Immunological Genome Project (ImmGen)), wealth of reagents and easy access to cells. Thanks to the broad and robust approaches allowed by gene-expression microarrays and related techniques, the transcriptome is probably the only ‘-ome’ that can be reliably tackled in its entirety. Generating a complete perspective of gene expression in the immune system

Gastroenteropancreatic neuroendocrine tumours.

Abstract: Gastroenteropancreatic (GEP) neuroendocrine tumours (NETs) are fairly rare neoplasms that present many clinical challenges. They secrete peptides and neuroamines that cause distinct clinical syndromes, including carcinoid syndrome. However, many are clinically silent until late presentation with mass effects. Investigation and management should be highly individualised for a patient, taking into consideration the likely natural history of the tumour and general health of the patient. Management strategies include surgery for cure (which is achieved rarely) or for cytoreduction, radiological intervention (by chemoembolisation and radiofrequency ablation), chemotherapy, and somatostatin analogues to control symptoms that result from release of peptides and neuroamines. New biological agents and somatostatin-tagged radionuclides are under investigation. The complexity, heterogeneity, and rarity of GEP NETs have contributed to a paucity of relevant randomised trials and little or no survival increase over the past 30 years. To improve outcome from GEP NETs, a better understanding of their biology is needed, with emphasis on molecular genetics and disease modeling. More-reliable serum markers, better tumour localisation and identification of small lesions, and histological grading systems and classifications with prognostic application are needed. Comparison between treatments is currently very difficult. Progress is unlikely to occur without development of centers of excellence, with dedicated combined clinical teams to coordinate multicentre studies, maintain clinical and tissue databases, and refine molecularly targeted therapeutics.

Global Burden of Hypertension and Systolic Blood Pressure of at Least 110 to 115 mm Hg, 1990-2015

Abstract: Importance Elevated systolic blood (SBP) pressure is a leading global health risk. Quantifying the levels of SBP is important to guide prevention policies and interventions. Objective To estimate the association between SBP of at least 110 to 115 mm Hg and SBP of 140 mm Hg or higher and the burden of different causes of death and disability by age and sex for 195 countries and territories, 1990-2015. Design A comparative risk assessment of health loss related to SBP. Estimated distribution of SBP was based on 844 studies from 154 countries (published 1980-2015) of 8.69 million participants. Spatiotemporal Gaussian process regression was used to generate estimates of mean SBP and adjusted variance for each age, sex, country, and year. Diseases with sufficient evidence for a causal relationship with high SBP (eg, ischemic heart disease, ischemic stroke, and hemorrhagic stroke) were included in the primary analysis. Main Outcomes and Measures Mean SBP level, cause-specific deaths, and health burden related to SBP (≥110-115 mm Hg and also ≥140 mm Hg) by age, sex, country, and year. Results Between 1990-2015, the rate of SBP of at least 110 to 115 mm Hg increased from 73 119 (95% uncertainty interval [UI], 67 949-78 241) to 81 373 (95% UI, 76 814-85 770) per 100 000, and SBP of 140 mm Hg or higher increased from 17 307 (95% UI, 17 117-17 492) to 20 526 (95% UI, 20 283-20 746) per 100 000. The estimated annual death rate per 100 000 associated with SBP of at least 110 to 115 mm Hg increased from 135.6 (95% UI, 122.4-148.1) to 145.2 (95% UI 130.3-159.9) and the rate for SBP of 140 mm Hg or higher increased from 97.9 (95% UI, 87.5-108.1) to 106.3 (95% UI, 94.6-118.1). Loss of disability-adjusted life-years (DALYs) associated with SBP of at least 110 to 115 mm Hg increased from 148 million (95% UI, 134-162 million) to 211 million (95% UI, 193-231 million), and for SBP of 140 mm Hg or higher, the loss increased from 95.9 million (95% UI, 87.0-104.9 million) to 143.0 million (95% UI, 130.2-157.0 million). The largest numbers of SBP-related deaths were caused by ischemic heart disease (4.9 million [95% UI, 4.0-5.7 million]; 54.5%), hemorrhagic stroke (2.0 million [95% UI, 1.6-2.3 million]; 58.3%), and ischemic stroke (1.5 million [95% UI, 1.2-1.8 million]; 50.0%). In 2015, China, India, Russia, Indonesia, and the United States accounted for more than half of the global DALYs related to SBP of at least 110 to 115 mm Hg. Conclusions and Relevance In international surveys, although there is uncertainty in some estimates, the rate of elevated SBP (≥110-115 and ≥140 mm Hg) increased substantially between 1990 and 2015, and DALYs and deaths associated with elevated SBP also increased. Projections based on this sample suggest that in 2015, an estimated 3.5 billion adults had SBP of at least 110 to 115 mm Hg and 874 million adults had SBP of 140 mm Hg or higher.

Opportunities and obstacles for deep learning in biology and medicine.

Abstract: Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.