Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore
Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore
Dartmouth Scholarship
Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes …
Modeling Neurovascular Coupling From Clustered Parameter Sets For Multimodal Eeg-Nirs, M. Tanveer Talukdar, H. Robert Frost, Solomon G. G. Diamond
Modeling Neurovascular Coupling From Clustered Parameter Sets For Multimodal Eeg-Nirs, M. Tanveer Talukdar, H. Robert Frost, Solomon G. G. Diamond
Dartmouth Scholarship
Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from …
Gene And Protein Sequence Optimization For High-Level Production Of Fully Active And Aglycosylated Lysostaphin In Pichia Pastoris, Hongliang Zhao, Kristina Blazanovic, Yoonjoo Choi, Chris Bailey-Kellogg, Karl E. Griswold
Gene And Protein Sequence Optimization For High-Level Production Of Fully Active And Aglycosylated Lysostaphin In Pichia Pastoris, Hongliang Zhao, Kristina Blazanovic, Yoonjoo Choi, Chris Bailey-Kellogg, Karl E. Griswold
Dartmouth Scholarship
Lysostaphin represents a promising therapeutic agent for the treatment of staphylococcal infections, in particular those of methicillin-resistant Staphylococcus aureus (MRSA). However, conventional expression systems for the enzyme suffer from various limitations, and there remains a need for an efficient and cost-effective production process to facilitate clinical translation and the development of nonmedical applications. While Pichia pastoris is widely used for high-level production of recombinant proteins, there are two major barriers to the production of lysostaphin in this industrially relevant host: lack of expression from the wild-type lysostaphin gene and aberrant glycosylation of the wild-type protein sequence. The first barrier can …
Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit
Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit
Dartmouth Scholarship
There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.
Failure To Replicate A Genetic Association May Provide Important Clues About Genetic Architecture, Casey S. Greene, Nadia M. Penrod, Scott M. Williams, Jason H. Moore
Failure To Replicate A Genetic Association May Provide Important Clues About Genetic Architecture, Casey S. Greene, Nadia M. Penrod, Scott M. Williams, Jason H. Moore
Dartmouth Scholarship
Replication has become the gold standard for assessing statistical results from genome-wide association studies. Unfortunately this replication requirement may cause real genetic effects to be missed. A real result can fail to replicate for numerous reasons including inadequate sample size or variability in phenotype definitions across independent samples. In genome-wide association studies the allele frequencies of polymorphisms may differ due to sampling error or population differences. We hypothesize that some statistically significant independent genetic effects may fail to replicate in an independent dataset when allele frequencies differ and the functional polymorphism interacts with one or more other functional polymorphisms. To …
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Dartmouth Scholarship
The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.
Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota
Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota
Dartmouth Scholarship
The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD …