Publications

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.

Abstract

Neural correlates of lexical stress were studied using the mismatch negativity (MMN) component in event-related potentials. The MMN responses were expected to reveal the encoding of stress information into long-term memory and the contributions of prosodic features such as fundamental frequency (F0) and intensity toward lexical access. In a passive oddball paradigm, neural responses to changes in F0, intensity, and in both features together were recorded for words and pseudowords. The findings showed significant differences not only between words and pseudowords but also between prosodic features. Early processing of prosodic information in words was indexed by an intensity-related MMN and an F0-related P200. These effects were stable at right-anterior and mid-anterior regions. At a later latency, MMN responses were recorded for both words and pseudowords at the mid-anterior and posterior regions. The P200 effect observed for F0 at the early latency for words developed into an MMN response. Intensity elicited smaller MMN for pseudowords than for words. Moreover, a larger brain area was recruited for the processing of words than for the processing of pseudowords. These findings suggest earlier and higher sensitivity to prosodic changes in words than in pseudowords, reflecting a language-related process. The present study, therefore, not only establishes neural correlates of lexical stress but also confirms the presence of long-term memory traces for prosodic information in the brain.

Abstract

The Corpus NGT