Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Methods (6)
- Humans (5)
- Algorithms (4)
- Genetics (4)
- Models (4)
-
- Statistical (4)
- Computer simulation (3)
- Gene expression profiling (3)
- Genetic (3)
- Chemistry (2)
- Computational biology (2)
- Diagnosis (2)
- Dna (2)
- Oligonucleotide array sequence analysis (2)
- Promoter regions (2)
- Protein binding (2)
- Sequence analysis (2)
- Statistics & numerical data (2)
- Transcription factors (2)
- Amino acid motifs (1)
- Automated (1)
- Base sequence (1)
- Binding sites (1)
- Biomarkers (1)
- Blood (1)
- Cerebrovascular circulation (1)
- Chondrocytes (1)
- Chromosome mapping (1)
- Cluster analysis (1)
- Computer-assisted (1)
Articles 1 - 10 of 10
Full-Text Articles in Life Sciences
Application Of Cycle-By-Cycle Analysis To Eeg Data From Individuals With Phelan-Mcdermid Syndrome, Naomi Miller
Application Of Cycle-By-Cycle Analysis To Eeg Data From Individuals With Phelan-Mcdermid Syndrome, Naomi Miller
ENGS 88 Honors Thesis (AB Students)
This study aimed to analyze a novel method of processing data from electroencephalography (EEG) recordings, which implements time-domain cycle-by-cycle analysis. This "bycycle" method, developed by the Cole & Voytek laboratory, was implemented on a EEG dataset of children with and without Phelan-McDermid Syndrome in the hopes of uncovering network-level explanations for the genetic disorder. A supplemental Python pipeline was developed to organize and visualize the data. This led to the discovery of group-level differences in measures of cycle symmetry in alpha band waves over the sensorimotor electrodes. Through the same pipeline, the bycycle tool was validated as a sound EEG …
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 …
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.
Effects Of Socioeconomic Status On Brain Development, And How Cognitive Neuroscience May Contribute To Levelling The Playing Field, Rajeev Raizada, Mark M. Kishiyama
Effects Of Socioeconomic Status On Brain Development, And How Cognitive Neuroscience May Contribute To Levelling The Playing Field, Rajeev Raizada, Mark M. Kishiyama
Dartmouth Scholarship
The study of socioeconomic status (SES) and the brain finds itself in a circumstance unusual for Cognitive Neuroscience: large numbers of questions with both practical and scientific importance exist, but they are currently under-researched and ripe for investigation. This review aims to highlight these questions, to outline their potential significance, and to suggest routes by which they might be approached. Although remarkably few neural studies have been carried out so far, there exists a large literature of previous behavioural work. This behavioural research provides an invaluable guide for future neuroimaging work, but also poses an important challenge for it: how …
Mechanistic Home Range Models And Resource Selection Analysis: A Reconciliation And Unification, Paul R. Moorcroft, Alex Barnett
Mechanistic Home Range Models And Resource Selection Analysis: A Reconciliation And Unification, Paul R. Moorcroft, Alex Barnett
Dartmouth Scholarship
In the three decades since its introduction, resource selection analysis (RSA) has become a widespread method for analyzing spatial patterns of animal relocations obtained from telemetry studies. Recently, mechanistic home range models have been proposed as an alternative framework for studying patterns of animal space-use. In contrast to RSA models, mechanistic home range models are derived from underlying mechanistic descriptions of individual movement behavior and yield spatially explicit predictions for patterns of animal space-use. In addition, their mechanistic underpinning means that, unlike RSA, mechanistic home range models can also be used to predict changes in space-use following perturbation. In this …
Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross
Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross
Dartmouth Scholarship
The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach.
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 …
Knowing When To Draw The Line: Designing More Informative Ecological Experiments, Kathryn L. Cottingham, Jay T. Lennon, Bryan L. Brown
Knowing When To Draw The Line: Designing More Informative Ecological Experiments, Kathryn L. Cottingham, Jay T. Lennon, Bryan L. Brown
Dartmouth Scholarship
Linear regression and analysis of variance (ANOVA) are two of the most widely used statistical techniques in ecology. Regression quantitatively describes the relationship between a response variable and one or more continuous independent variables, while ANOVA determines whether a response variable differs among discrete values of the independent variable(s). Designing experiments with discrete factors is straightforward because ANOVA is the only option, but what is the best way to design experiments involving continuous factors? Should ecologists prefer experiments with few treatments and many replicates analyzed with ANOVA, or experiments with many treatments and few replicates per treatment analyzed with regression? …