Catálogo de publicaciones - libros

Low- and moderate-income households who use alternative financial service providers pay a high price to convert their income into cash, pay their bills, and obtain credit, and they lack a regular means to save. The high cost of alternative financial services undermines key income redistribution policies for the poor, including the EITC. Existing banking products are often not well designed to meet the needs of the poor, and few banks compete with alternative financial services providers for low-income customers, particularly in low-income neighborhoods. The cost to individual financial institutions of research, product development, account administration, staff training, marketing and financial education with respect to new financial products for the poor, relative to their expected financial return, means that the market is unlikely to change quickly on its own. In addition, network externalities in electronic payments systems and distribution networks suggest that net social benefit could be obtained through further expansion.

Financial and technological innovation has been a hallmark of U.S. financial markets. Financial institutions can harness that innovation to meet the needs of low-income Americans. Governmental incentives appear to be important to catalyze private sector efforts to use financial and technological progress to expand access to financial services for low- and moderate-income families. By helping these families to enter the financial services mainstream, the policies outlined here can help to transform financial services for low-income persons. Such a transformation is a key to promoting greater economic opportunities for low-income households.

Protein domains are the building blocks of proteins, and their interactions are crucial in forming stable protein-protein interactions (PPI) and take part in many cellular processes and biochemical events. Prediction of protein domain-domain interactions (DDI) is an emerging problem in computational biology. Different from early works on DDI prediction, which exploit only a single protein database, we introduce in this paper an integrative approach to DDI prediction that exploits multiple genome databases using inductive logic programming (ILP). The main contribution to biomedical knowledge discovery of this work are a newly generated database of more than 100,000 ground facts of the twenty predicates on protein domains, and various DDI findings that are evaluated to be significant. Experimental results show that ILP is more appropriate to this learning problem than several other methods. Also, many predictive rules associated with domain sites, conserved motifs, protein functions and biological pathways were found.