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- Bioinformatics (1)
- Biomedical signal processing (1)
- Cancer genomics (1)
- Crossing hazards (1)
- Crystallization (1)
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- Data mining (1)
- Epitopes (1)
- Feature selection (1)
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- High-throughput "omics" (1)
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- Kullback-Leibler information divergence (1)
- Large-scale biological data analysis (1)
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- Non-negative matrix factorization (1)
- Non-proportional hazards (1)
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- Protein crystallization; protein thermodynamics; crystallization mechanism; surface entropy; datamining; structural genomics (1)
- Proteins (1)
- Text mining (1)
- Thermodynamic stability (1)
- Time-varying hazards (1)
- Yang-Prentice model (1)
Articles 1 - 4 of 4
Full-Text Articles in Biometry
Bisc 504: Biometry, Jason Hoeksema
Unified Methods For Feature Selection In Large-Scale Genomic Studies With Censored Survival Outcomes, Lauren Spirko-Burns, Karthik Devarajan
Unified Methods For Feature Selection In Large-Scale Genomic Studies With Censored Survival Outcomes, Lauren Spirko-Burns, Karthik Devarajan
COBRA Preprint Series
One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the disease's process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous data sets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards …
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
COBRA Preprint Series
Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …
Understanding The Physical Properties That Control Protein Crystallization By Analysis Of Largescale Experimental Data, W. Nicholson Price Ii, Yang Chen, Samuel K. Handelman, Helen Neely, Philip Manor, Richard Karlin, Rajesh Nair, Jinfeng Liu, Michael Baran, John Everett, Saichiu N. Tong, Farhad Forouhar, Swarup S. Swaminathan, Thomas Acton, Rong Xiao, Joseph R. Luft, Angela Lauricella, George T. Detitta, Burkhard Rost, Gaetano T. Montelione, John T. Hunt
Understanding The Physical Properties That Control Protein Crystallization By Analysis Of Largescale Experimental Data, W. Nicholson Price Ii, Yang Chen, Samuel K. Handelman, Helen Neely, Philip Manor, Richard Karlin, Rajesh Nair, Jinfeng Liu, Michael Baran, John Everett, Saichiu N. Tong, Farhad Forouhar, Swarup S. Swaminathan, Thomas Acton, Rong Xiao, Joseph R. Luft, Angela Lauricella, George T. Detitta, Burkhard Rost, Gaetano T. Montelione, John T. Hunt
Law Faculty Scholarship
Crystallization is the most serious bottleneck in high-throughput protein-structure determination by diffraction methods. We have used data mining of the large-scale experimental results of the Northeast Structural Genomics Consortium and experimental folding studies to characterize the biophysical properties that control protein crystallization. This analysis leads to the conclusion that crystallization propensity depends primarily on the prevalence of well-ordered surface epitopes capable of mediating interprotein interactions and is not strongly influenced by overall thermodynamic stability. We identify specific sequence features that correlate with crystallization propensity and that can be used to estimate the crystallization probability of a given construct. Analyses of …