# A tutorial on KNIME Deeplearning4J Integration

An interesting blog post

https://www.knime.com/blog/learning-deep-learning

The aim of this blog post is to highlight some of the key features of the KNIME Deeplearning4J (DL4J) integration, and help newcomers to either Deep Learning or KNIME to be able to take their first steps with Deep Learning in KNIME Analytics Platform.

# Accessing Jupyter Notebook model from Vortex

I've become a great fan of Jupyter Notebooks as a way of modelling cheminformatics data, and I've published some of the notebooks here.

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.

In the predicting AMES activity notebook I also looked at the use of pickle to store the predictive model and then access it using a Jupyter notebook without the need to rebuild the model. Whilst a notebook is a nice way to access the predictive model it might also be useful to be able to access it from other applications or from the command line.

In this tutorial we look at providing command line access to the model and then incorporating it into a Vortex script.

# Data-driven Advice for Applying Machine Learning to Bioinformatics Problems

A very useful paper https://arxiv.org/abs/1708.05070

Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems.

Good to see my preferred method Random Forest close to the top of the ranking based on performance over 165 datasets.

The rankings show the strength of ensemble-based tree algorithms in generating accurate models: The first, second, and fourth-ranked algorithms belong to this class of algorithms.

All 13 ML algorithms were used as implemented in scikit-learn, a popular ML library implemented in Python.

# Weekend Reading

A couple of things for your weekend reading ;-)

What makes Python super popular

Machine Learning in Python tutorial

# Free machine learning and data science ebooks

An interesting post By Matthew Mayo, KDnuggets.

Here is a quick collection of such books to start your fair weather study off on the right foot. The list begins with a base of statistics, moves on to machine learning foundations, progresses to a few bigger picture titles, has a quick look at an advanced topic or 2, and ends off with something that brings it all together. A mix of classic and contemporary titles, hopefully you find something new (to you) and of interest here.

# Predicting AMES activity Jupyter Notebook

I've been experimenting with the use of Jupyter Notebooks (aka iPython Notebooks) as an electronic lab notebook but also a means to share computational models. The aim would be to see how easy it would be to share a model together with the associated training data together with an explanation of how the model was built and how it can be used for novel molecules.

The Ames test is a widely employed method that uses bacteria to test whether a given chemical can cause mutations in the DNA of the test organism. More formally, it is a biological assay to assess the mutagenic potential of chemical compounds. PNAS. 70 (8): 2281–5. doi

In this first notebook a random forest model to predict AMES activity is described….