Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Several roads lead to international norms, but few via international socialization. A case study of the European Commission

Can an international organization socialize those who work within it? The European Commission of the European Union is a crucial case because it is an autonomous international organization with a vocation to defend supranational norms. If this body cannot socialize its members, which international organization can? I develop theoretical expectations about how time, organizational structure, alternative processes of preference formation, and national socialization affect international socialization. To test these expectations for the European Commission, I use two surveys of top permanent Commission officials, conducted in 1996 and 2002. The analysis shows that support for supranational norms is relatively high, but that this is more because of national socialization than socialization in the Commission. National norms, originating in prior experiences in national ministries, loyalty to national political parties, or experience with one's country's organization of authority, decisively shape top officials' views on supranational norms. There are, then, several roads to international norms. © 2005 by The IO Foundation

T-Crowd: Effective Crowdsourcing for Tabular Data

Crowdsourcing employs human workers to solve computer-hard problems, such as data cleaning, entity resolution, and sentiment analysis. When crowdsourcing tabular data, e.g., the attribute values of an entity set, a worker's answers on the different attributes (e.g., the nationality and age of a celebrity star) are often treated independently. This assumption is not always true and can lead to suboptimal crowdsourcing performance. In this paper, we present the T-Crowd system, which takes into consideration the intricate relationships among tasks, in order to converge faster to their true values. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is also used to guide task allocation to workers. Finally, T-Crowd seamlessly supports categorical and continuous attributes, which are the two main datatypes found in typical databases. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods in terms of truth inference and reducing the cost of crowdsourcing