Landing Page | Data Revenue

Introduction

Are you analysing High Troughput Omics datasets with your team?
Do you need to speed up data-anlysis?
Is struggling with using machine learning correctly?

This webinar will teach you real-world insights on using machine learning for omics analysis:

What can you actually do with machine learning in Omics?
What are common misconceptions?
Concrete usecases and real world examples.

When & where

The webinar will take place on the 7th of July from 16:00 - 17:30 CET as a Webinar via GoToMeeting. You can dial in from your computer of phone.

Take part if you are

A CTO or CIO and you need to decide where to use AI and how.
A Heads of Translational Science and are looking for ways to speed up your analyses – without sacrificing reliability.
A IT Project Manager in Bioinformatics and you are looking for guidance on how to build Omics analytics tools with ML , and how to run ML learning projects without getting stuck in a PoC.

Take part if you are

Know what machine learning really is. No matter if you studied Biology, Chemistry, Business or IT – this is a no-hype, fully practical, view of what the tool can do and what it can not do.
Be prepared for the mistakes other companies are making. I and my team have seen what makes teams fail, and how they can succeed to build fully functioning solutions in as short a time as possible.
Make a plan how to speed of your process with machine learning. Learn how you can check if a problem can be solved with machine learning. And how to define the problem so that both your business and bioinformaticians have the same understanding.
Learn Omics specific tricks on using machine learning. Omics data is has high dimensionality and at the same time usually few samples. This makes it particularly tricky to use machine learning. But there are ways, such as feature selection, model limitation and stability selection that can remedy. Also: Your models need to be explainable. We show you the most recent techniques for drawing insights from models.
Questions. During and after the session you can ask any questions – and we will discuss them together.

Example Slides from the Presentation

About the Speaker

I’m Markus, CEO and Head of Data Science at Data Revenue. Our team has built over 30 production machine learning applications over the last 4 years. Some projects and testimonials.

I’m a Mathematician, turned developer and now mostly manage machine learning projects, with a focus on drug discovery and helping Pharma companies plan clinical trials. – However we also work in many other industries, from which we draw inspiration and new perspectives: Automative (with Volkswagen and Daimler), Energy (E.ON) and Internet (large AdNetworks and Ecommerce sites).

Ask me anything during the Webinar. I will give you honest, no buzzword, no honeycoating, advice on what we learned using machine learning for Omics and BioTech research.

90min Free Webinar – 9th July 2019

Webinar FAQ:

Can we really create a winning machine learning strategy in a day?

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Who attends the Strategy Day?

We’ll be bringing Markus Schmitt (CEO) and Alan Hong (CTO / ML Engineer). Between the two of us, we have the in-depth technical skill, academic knowledge, analysis and hands-on experience to identify and validate innovative solutions to your biggest problems.
‍
You should bring someone with extensive domain knowledge and high-level awareness of the product and company priorities— like a CIO or Product Manager. Also, someone familiar with any and all data sets that exist— like a data engineer.

Do I have enough data for machine learning? Do I have the right data?

What if I already have a high-level understanding of ML?

Why is a Strategy Day important?

Do you have any references I can speak with?