Publications

Abstract

This article provides a first overview of some striking grammatical structures in Türk Idotscedilaret Dili (Turkish Sign Language, TID), the sign language used by the Deaf community in Turkey. The data are described with a typological perspective in mind, focusing on aspects of TID grammar that are typologically unusual across sign languages. After giving an overview of the historical, sociolinguistic and educational background of TID and the language community using this sign language, five domains of TID grammar are investigated in detail. These include a movement derivation signalling completive aspect, three types of nonmanual negation — headshake, backward head tilt, and puffed cheeks — and their distribution, cliticization of the negator NOT to a preceding predicate host sign, an honorific whole-entity classifier used to refer to humans, and a question particle, its history and current status in the language. A final evaluation points out the significance of these data for sign language research and looks at perspectives for a deeper understanding of the language and its history.

Abstract

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.