Cancer is a heterogeneous disease with different combinations of genetic and epigenetic alterations driving the development of cancer in different individuals. While these alterations are believed to ...converge on genes in key cellular signaling and regulatory pathways, our knowledge of these pathways remains incomplete, making it difficult to identify driver alterations by their recurrence across genes or known pathways. We introduce Combinations of Mutually Exclusive Alterations (CoMEt), an algorithm to identify combinations of alterations de novo, without any prior biological knowledge (e.g. pathways or protein interactions). CoMEt searches for combinations of mutations that exhibit mutual exclusivity, a pattern expected for mutations in pathways. CoMEt has several important feature that distinguish it from existing approaches to analyze mutual exclusivity among alterations. These include: an exact statistical test for mutual exclusivity that is more sensitive in detecting combinations containing rare alterations; simultaneous identification of collections of one or more combinations of mutually exclusive alterations; simultaneous analysis of subtype-specific mutations; and summarization over an ensemble of collections of mutually exclusive alterations. These features enable CoMEt to robustly identify alterations affecting multiple pathways, or hallmarks of cancer. We show that CoMEt outperforms existing approaches on simulated and real data. Application of CoMEt to hundreds of samples from four different cancer types from TCGA reveals multiple mutually exclusive sets within each cancer type. Many of these overlap known pathways, but others reveal novel putative cancer genes. *Equal contribution.
What are the biases in my word embedding? Swinger, Nathaniel; De-Arteaga, Maria; Heffernan, Neil Thomas ...
arXiv (Cornell University),
06/2019
Paper, Journal Article
Odprti dostop
This paper presents an algorithm for enumerating biases in word embeddings. The algorithm exposes a large number of offensive associations related to sensitive features such as race and gender on ...publicly available embeddings, including a supposedly "debiased" embedding. These biases are concerning in light of the widespread use of word embeddings. The associations are identified by geometric patterns in word embeddings that run parallel between people's names and common lower-case tokens. The algorithm is highly unsupervised: it does not even require the sensitive features to be pre-specified. This is desirable because: (a) many forms of discrimination--such as racial discrimination--are linked to social constructs that may vary depending on the context, rather than to categories with fixed definitions; and (b) it makes it easier to identify biases against intersectional groups, which depend on combinations of sensitive features. The inputs to our algorithm are a list of target tokens, e.g. names, and a word embedding. It outputs a number of Word Embedding Association Tests (WEATs) that capture various biases present in the data. We illustrate the utility of our approach on publicly available word embeddings and lists of names, and evaluate its output using crowdsourcing. We also show how removing names may not remove potential proxy bias.
Discussions of rural development policy are for the most part focused on the tenurial, institutional, technical, infrastructural, and economic aspects of agricultural development. In contrast, ...nonfarm activities in agricultural regions receive little attention, and a number of models of agrarian economies with nonfarm activities have even predicted a decline of such activities with agricultural development. It is shown here that nonfarm activities in agricultural regions expand quite rapidly in response to agricultural development and merit special attention in the design of rural and urban development strategies.The extent and importance of nonfarm activities in rural areas and towns is examined from the viewpoint of their contribution to the output, employment, and earnings of the rural labor force. Nonfarm activities become increasingly concentrated in rural towns in response to infrastructure improvements and the growth of markets. Besides being of benefit to the activities themselves, the process appears to stimulate a degree of decentralization of urban growth, providing added employment and earnings opportunities fr the out-migrants from agriculture as agricultural productivity rises. Nonfarm activities in rural areas and towns are thus an essential element in the process of economic development and structural change from rural-agricultural to urban-industrial economies. Appendices.
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