Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Thrombophilia in cancer.

Abstract: Malignancy is an acquired thrombophilic condition associated with a significant risk of thrombosis. Venous and arterial thromboembolism is a common complication for patients with cancer, who also present with a hypercoagulable state, even in the absence of manifest thrombosis. Furthermore, clotting activation may play a role in tumor progression. The pathogenesis of thrombosis in cancer is multifactorial; however, a relevant role is attributed to the tumor cell capacity to interact with and activate the host hemostatic system. Among other factors, the prothrombotic action of antitumor therapies is also important. Thrombotic events can influence the morbidity and mortality of the underlying disease. Therefore, preventing these complications in cancer patients is a clinically relevant issue. Recently, new approaches to the prevention and cure of thrombosis in cancer have been investigated, and the hypothesis that strategies to inhibit clotting mechanism may favorably affect malignant disease is gaining increasing interest. In this article, the various aspects of the complex relationship between thrombosis and cancer, from pathophysiology to therapy, are reviewed.

Explore/Exploit Schemes for Web Content Optimization

Abstract: We propose novel multi-armed bandit (explore/exploit) schemes to maximize total clicks on a content module published regularly on Yahoo! Intuitively, one can ``explore'' each candidate item by displaying it to a small fraction of user visits to estimate the item's click-through rate (CTR), and then ``exploit'' high CTR items in order to maximize clicks. While bandit methods that seek to find the optimal trade-off between explore and exploit have been studied for decades, existing solutions are not satisfactory for web content publishing applications where dynamic set of items with short lifetimes, delayed feedback and non-stationary reward (CTR) distributions are typical. In this paper, we develop a Bayesian solution and extend several existing schemes to our setting. Through extensive evaluation with nine bandit schemes, we show that our Bayesian solution is uniformly better in several scenarios. We also study the empirical characteristics of our schemes and provide useful insights on the strengths and weaknesses of each. Finally, we validate our results with a ``side-by-side'' comparison of schemes through live experiments conducted on a random sample of real user visits to Yahoo!

Personalized click prediction in sponsored search

Abstract: Sponsored search is a multi-billion dollar business that generates most of the revenue for search engines. Predicting the probability that users click on ads is crucial to sponsored search because the prediction is used to influence ranking, filtering, placement, and pricing of ads. Ad ranking, filtering and placement have a direct impact on the user experience, as users expect the most useful ads to rank high and be placed in a prominent position on the page. Pricing impacts the advertisers' return on their investment and revenue for the search engine. The objective of this paper is to present a framework for the personalization of click models in sponsored search. We develop user-specific and demographic-based features that reflect the click behavior of individuals and groups. The features are based on observations of search and click behaviors of a large number of users of a commercial search engine. We add these features to a baseline non-personalized click model and perform experiments on offline test sets derived from user logs as well as on live traffic. Our results demonstrate that the personalized models significantly improve the accuracy of click prediction.

Potent and selective inhibitors of the Met [hepatocyte growth factor/scatter factor (HGF/SF) receptor] tyrosine kinase block HGF/SF-induced tumor cell growth and invasion.

Abstract: The hepatocyte growth factor/scatter factor (HGF/SF) receptor, Met, mediates various cellular responses on activation with its ligand, including proliferation, survival, motility, invasion, and tubular morphogenesis. Met expression is frequently up-regulated in sarcomas and carcinomas. Experimental evidence suggests that Met activation correlates with poor clinical outcome and the likelihood of metastasis. Therefore, inhibitors of Met tyrosine kinase may be useful for the treatment of a wide variety of cancers that have spread from the primary site. We have discovered potent and selective pyrrole-indolinone Met kinase inhibitors and characterized them for their ability to inhibit HGF/SF-induced cellular responses in vitro. These compounds inhibit HGF/SF-induced receptor phosphorylation in a dose-dependent manner. They also inhibit the HGF/SF-induced motility and invasion of epithelial and carcinoma cells. Therefore, these compounds represent a class of prototype small molecules that selectively inhibit the Met kinase and could lead to identification of compounds with potential therapeutic utility in treatment of cancers.

Branch and Bound for Semi-Supervised Support Vector Machines

Abstract: Semi-supervised SVMs (S3VM) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The associated optimization problem is non-convex. To examine the full potential of S3VMs modulo local minima problems in current implementations, we apply branch and bound techniques for obtaining exact, globally optimal solutions. Empirical evidence suggests that the globally optimal solution can return excellent generalization performance in situations where other implementations fail completely. While our current implementation is only applicable to small datasets, we discuss variants that can potentially lead to practically useful algorithms.