Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data

Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data.

Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant's molecular effect. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, wi...

Liquid-liquid equilibrium data prediction using large margin nearest neighbor

Guanidine hydrochloride has been widely used in the initial recovery steps of active protein from the inclusion bodies in aqueous two-phase system (ATPS). The knowledge of the guanidine hydrochloride effects on the liquid-liquid equilibrium (LLE) phase diagram behavior is still inadequate and no comprehensive theory exists for the prediction of the experimental trends. Therefore the effect the ...

Prediction and large scale data

Access control in ultra-large-scale systems using a data-centric middleware

  The primary characteristic of an Ultra-Large-Scale (ULS) system is ultra-large size on any related dimension. A ULS system is generally considered as a system-of-systems with heterogeneous nodes and autonomous domains. As the size of a system-of-systems grows, and interoperability demand between sub-systems is increased, achieving more scalable and dynamic access control system becomes an im...

Large Scale Disease Prediction

The objective of this thesis is to present the foundation of an automated large-scale disease prediction system. Unlike previous work that has typically focused on a small self-contained dataset, we explore the possibility of combining a large amount of heterogenous data to perform gene selection and phenotype classification. First, a subset of publicly available microarray datasets was downloa...