Resting frontal gamma power at 16, 24 and 36 months predicts individual differences in language and cognition at 4 and 5 years.

Gamma activity has been linked to a variety of different cognitive processes and exists in both transient and persistent forms. Across studies, different brain regions have been suggested to contribute to gamma activity. Multiple studies have shown that the function of gamma oscillations may be related to temporal binding of early sensory information to relevant top-down processes.

On the Accuracy of Sequence-Based Computational Inference of Protein Residues Involved in Interactions with DNA.

Methods for computational inference of DNA-binding residues in DNA-binding proteins are usually developed using classification techniques trained to distinguish between binding and non-binding residues on the basis of known examples observed in experimentally determined high-resolution structures of protein-DNA complexes. What degree of accuracy can be expected when a computational methods is applied to a particular novel protein remains largely unknown. We test the utility of classification methods on the example of Kernel Logistic Regression (KLR) predictors of DNA-binding residues.

Early cognitive and language skills are linked to resting frontal gamma power across the first 3 years.

High-frequency cortical activity in humans and animals has been linked to a wide variety of higher cognitive processes. This research suggests that specific changes in neuronal synchrony occur during cognitive processing, distinguished by emergence of fast oscillations in the gamma frequency range. To determine whether the development of high-frequency brain oscillations can be related to the development of cognitive abilities, we studied the power spectra of resting EEG in children 16, 24 and 36 months of age.

DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins.

Unlabelled: This article describes DP-Bind, a web server for predicting DNA-binding sites in a DNA-binding protein from its amino acid sequence. The web server implements three machine learning methods: support vector machine, kernel logistic regression and penalized logistic regression. Prediction can be performed using either the input sequence alone or an automatically generated profile of evolutionary conservation of the input sequence in the form of PSI-BLAST position-specific scoring matrix (PSSM).

Using evolutionary and structural information to predict DNA-binding sites on DNA-binding proteins.

Proteins that interact with DNA are involved in a number of fundamental biological activities such as DNA replication, transcription, and repair. A reliable identification of DNA-binding sites in DNA-binding proteins is important for functional annotation, site-directed mutagenesis, and modeling protein-DNA interactions. We apply Support Vector Machine (SVM), a supervised pattern recognition method, to predict DNA-binding sites in DNA-binding proteins using the following features: amino acid sequence, profile of evolutionary conservation of sequence positions, and low-resolution structural information.

The infant as a prelinguistic model for language learning impairments: predicting from event-related potentials to behavior.

Associations between efficient processing of brief, rapidly presented, successive stimuli and language learning impairments (LLI) in older children and adults have been well documented. In this paper we examine the role that impaired rapid auditory processing (RAP) might play during early language acquisition. Using behavioral measures we have demonstrated that RAP abilities in infancy are critically linked to later language abilities for both non-speech and speech stimuli.

A canonical correlation neural network for multicollinearity and functional data.

We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets.