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Articles 1 - 3 of 3
Full-Text Articles in Life Sciences
Planning Combinatorial Disulfide Cross-Links For Protein Fold Determination, Fei Xiong, Alan M Friedman, Chris Bailey-Kellogg
Planning Combinatorial Disulfide Cross-Links For Protein Fold Determination, Fei Xiong, Alan M Friedman, Chris Bailey-Kellogg
Dartmouth Scholarship
Fold recognition techniques take advantage of the limited number of overall structural organizations, and have become increasingly effective at identifying the fold of a given target sequence. However, in the absence of sufficient sequence identity, it remains difficult for fold recognition methods to always select the correct model. While a native-like model is often among a pool of highly ranked models, it is not necessarily the highest-ranked one, and the model rankings depend sensitively on the scoring function used. Structure elucidation methods can then be employed to decide among the models based on relatively rapid biochemical/biophysical experiments.
Additive Functions In Boolean Models Of Gene Regulatory Network Modules, Christian Darabos, Ferdinando Ferdinando Di Cunto, Marco Tomassini, Jason H. Moore
Additive Functions In Boolean Models Of Gene Regulatory Network Modules, Christian Darabos, Ferdinando Ferdinando Di Cunto, Marco Tomassini, Jason H. Moore
Dartmouth Scholarship
Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome’s evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We …
Evolving Hard Problems: Generating Human Genetics Datasets With A Complex Etiology, Daniel S Himmelstein, Casey S Greene, Jason H Moore
Evolving Hard Problems: Generating Human Genetics Datasets With A Complex Etiology, Daniel S Himmelstein, Casey S Greene, Jason H Moore
Dartmouth Scholarship
BackgroundA goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models.