ArticleMethods in molecular biology (Clifton, N.J.)2015
GENN: a GEneral Neural Network for learning tabulated data with examples from protein structure prediction.
Article in Methods in molecular biology (Clifton, N.J.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- AlphaFold2, SPINE-X, and Seder on Four Hard CASP Targets.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.Human genomics · 2024Article
- A Hybrid Levenberg-Marquardt Algorithm on a Recursive Neural Network for Scoring Protein Models.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Article
- Protein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach.BMC bioinformatics · 2016Article
- Accurate single-sequence prediction of solvent accessible surface area using local and global features.Proteins · 2014Article
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Authors and funding
2 authors.
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Abstract
We present a GEneral Neural Network (GENN) for learning trends from existing data and making predictions of unknown information. The main novelty of GENN is in its generality, simplicity of use, and its specific handling of windowed input/output. Its main strength is its efficient handling of the input data, enabling learning from large datasets. GENN is built on a two-layered neural network and has the option to use separate inputs-output pairs or window-based data using data structures to efficiently represent input-output pairs. The program was tested on predicting the accessible surface area of globular proteins, scoring proteins according to similarity to native, predicting protein disorder, and has performed remarkably well. In this paper we describe the program and its use. Specifically, we give as an example the construction of a similarity to native protein scoring function that was constructed using GENN. The source code and Linux executables for GENN are available from Research and Information Systems at http://mamiris.com and from the Battelle Center for Mathematical Medicine at http://mathmed.org. Bugs and problems with the GENN program should be reported to EF.
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