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Jaroslaw Meller

Researcher at University of Cincinnati

Publications -  79
Citations -  5490

Jaroslaw Meller is an academic researcher from University of Cincinnati. The author has contributed to research in topics: Protein structure prediction & Context (language use). The author has an hindex of 29, co-authored 74 publications receiving 4940 citations. Previous affiliations of Jaroslaw Meller include University of Cincinnati Academic Health Center & Cincinnati Children's Hospital Medical Center.

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fw2.2: a quantitative trait locus key to the evolution of tomato fruit size.

TL;DR: Alterations in fruit size, imparted by fw2.2 alleles, are most likely due to changes in regulation rather than in the sequence and structure of the encoded protein.
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Prediction-based fingerprints of protein-protein interactions.

TL;DR: The authors demonstrate that RSA prediction‐based fingerprints of protein interactions significantly improve the discrimination between interacting and noninteracting sites, compared with evolutionary conservation, physicochemical characteristics, structure‐derived and other features considered before.
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Combining prediction of secondary structure and solvent accessibility in proteins.

TL;DR: It is concluded that an increase in the 3‐state classification accuracy may be achieved when combining RSA with a state‐of‐the‐art protocol utilizing evolutionary profiles, as well as for prediction protocols that implicitly account for RSA in other ways.
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The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations

Alexandra B Keenan, +107 more
- 29 Nov 2017 - 
TL;DR: The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders.
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Accurate prediction of solvent accessibility using neural networks-based regression.

TL;DR: A novel method for improved prediction of relative solvent accessibilities (RSAs) of amino acid residues in proteins using nonlinear regression, with several feed forward and recurrent neural networks, which are then combined into a consensus predictor.