Neural Networks and Deep Learning

A book online

Neural Networks and Deep Learning is a free online book. The book will teach you about:

  • Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
  • Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

For more details about the approach taken in the book, see here. Or you can jump directly to Chapter 1 and get started.


Human gut microbiome aging clock

Abstract

The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host’s age based on the gut community composition.

Here we developed a method of predicting hosts’ age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers.

The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative.

FULL TEXT (PDF): iScience


Neural networks and deep learning

Patterns + layers

Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We’ll talk about how the math of these networks work and how using many hidden layers allows us to do deep learning. Neural networks are really powerful at finding patterns in data which is why they’ve become one of the most dominant machine learning technologies used today.



Deep learning for predicting pharmacological properties

Abstract

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. We demonstrate how deep neural networks trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles.

We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to twelve therapeutic use categories derived from MeSH. To train the deep neural network, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours.

In both pathway and gene level classification, the deep neural network achieved high classification accuracy and convincingly outperformed the support vector machine model on every multiclass classification problem, however, models based on pathway level data performed significantly better.

For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

FULL TEXT: Molecular Pharmaceutics