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Full-Text Articles in Computational Biology
A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes
A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes
Graduate Industrial Research Symposium
The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute it into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects, but does not necessarily indicate the initial point of interference within the network. The objective of this project is to take advantage of large scale and genome-wide perturbational datasets by using them to train a tuned machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of …
Modeling Nonsegmented Negative-Strand Rna Virus (Nnsv) Transcription With Ejective Polymerase Collisions And Biased Diffusion, Felipe-Andres Piedra
Modeling Nonsegmented Negative-Strand Rna Virus (Nnsv) Transcription With Ejective Polymerase Collisions And Biased Diffusion, Felipe-Andres Piedra
Research Symposium
Background: The textbook model of NNSV transcription predicts a gene expression gradient. However, multiple studies show non-gradient gene expression patterns or data inconsistent with a simple gradient. Regarding the latter, several studies show a dramatic decrease in gene expression over the last two genes of the respiratory syncytial virus (RSV) genome (a highly studied NNSV). The textbook model cannot explain these phenomena.
Methods: Computational models of RSV and vesicular stomatitis virus (VSV – another highly studied NNSV) transcription were written in the Python programming language using the Scientific Python Development Environment. The model code is freely available on GitHub: …
Model-Free Identification Of Relevant Variables From Response Data, Alan Veliz-Cuba, David Murrugarra
Model-Free Identification Of Relevant Variables From Response Data, Alan Veliz-Cuba, David Murrugarra
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Statistical Inference Of Adaptation At Multiple Genomic Scales Using Supervised Classification And A Hidden Markov Model, Lauren A. Sugden
Statistical Inference Of Adaptation At Multiple Genomic Scales Using Supervised Classification And A Hidden Markov Model, Lauren A. Sugden
Biology and Medicine Through Mathematics Conference
No abstract provided.
Sdrap: An Annotation Pipeline For Highly Scrambled Genomes, Jasper Braun
Sdrap: An Annotation Pipeline For Highly Scrambled Genomes, Jasper Braun
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Network Structure And Dynamics Of Biological Systems, Deena R. Schmidt
Network Structure And Dynamics Of Biological Systems, Deena R. Schmidt
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Loop Homology Of Bi-Secondary Structures, Andrei Bura
Loop Homology Of Bi-Secondary Structures, Andrei Bura
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Topology And Dynamics Of Gene Regulatory Networks: A Meta-Analysis, Claus Kadelka
Topology And Dynamics Of Gene Regulatory Networks: A Meta-Analysis, Claus Kadelka
Biology and Medicine Through Mathematics Conference
No abstract provided.
A Novel Pathway-Based Distance Score Enhances Assessment Of Disease Heterogeneity In Gene Expression, Yunqing Liu, Xiting Yan
A Novel Pathway-Based Distance Score Enhances Assessment Of Disease Heterogeneity In Gene Expression, Yunqing Liu, Xiting Yan
Yale Day of Data
Distance-based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and the relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. In this study, we developed a novel computational method to assess the biological differences based on pathways by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both …
Using Mathematical Models Of Biological Processes In Genome-Wide Association Studies Of Psychiatric Disorders, Amy Cochran
Using Mathematical Models Of Biological Processes In Genome-Wide Association Studies Of Psychiatric Disorders, Amy Cochran
Biology and Medicine Through Mathematics Conference
No abstract provided.