Date: Wed, May 20, 2020 at 12:43 AM
Bio-systems of the gut-centric body & butterfly effect. : RS

Cross pollinate 15% traditional method on 500 to 2000 sample gene sequences,
Re sharpen resultant GCN with a SVN network mesh

https://www.worldcommunitygrid.org/about_us/viewNewsArticle.do?articleId=625

Bio-systems of the gut-centric body & butterfly effect.

Reviewed and approved,
Proof provides the satisfaction that neural networks work; Classification of gut microbes ..
The fauna & living components of gut biology is a complex system,
The world community teams; Are delving into the most important part's of the ecosystem of the human and animal world..

Proving that the system works is a statistical average of thousands of years of record's & biology study,
Chemical Chemistry, Bio systems & balance, Creating meta data for biological record,
Solving the function of the system; Into simple systems of interacting complexity,
The genetic revolution & evolution of the evolving system; Ecosystem's of worlds in biology.

(c)QE https://science.n-helix.com

Abstract - https://www.biorxiv.org/content/10.1101/786236v1.full

"Recent massive increases in the number of sequences available in public databases challenges current experimental approaches to determining protein function.
These methods are limited by both the large scale of these sequences databases and the diversity of protein functions.

We present a deep learning Graph Convolutional Network (GCN) trained on sequence and structural data and evaluate it on ~40k proteins with known structures and functions from the Protein Data Bank (PDB). Our GCN predicts functions more accurately than Convolutional Neural Networks trained on sequence data alone and competing methods.

Feature extraction via a language model removes the need for constructing multiple sequence alignments or feature engineering.

Our model learns general structure-function relationships by robustly predicting functions of proteins with ≤ 30% sequence identity to the training set.

Using class activation mapping, we can automatically identify structural regions at the residue-level that lead to each function prediction for every protein confidently predicted, advancing site-specific function prediction.

De-noising inherent in the trained model allows an only minor drop in performance when structure predictions are used, including multiple de novo protocols. We use our method to annotate all proteins in the PDB,

Making several new confident function predictions spanning both fold and function trees."