The role of data analysis and data stewardship in life science fields will only increase, so you need to be ready! These courses offer you the chance to become industry-reading by providing hands-on lessons in four major pillars of data analysis and data stewardship: FAIR data stewardship, statistics, omics data analysis, and machine learning.
The industry in Life Sciences is and will increasingly become a crucial piece of the economy for both Flanders and the Netherlands. Well trained individuals in the region are indispensable for the workforce, so the types of training available must be tailored to the needs of the surrounding companies. The volume of information and data will only increase as more developments are made in life sciences, a clear indication of the importance of data analysis and stewardship for companies. The Helis Academy has chosen data analysis and stewardship as one of their targets for training and broken it down to four specific topics: omics data analysis, statistics, FAIR data stewardship, and machine learning.
Omics data analysis facilitates understanding of processes in both healthy and diseased states in the human body. Through omics data analysis, targets for disease treatment can be found, making it a valuable tool for companies in life sciences. Companies also benefit from the knowledge of good statistical practices. Understanding of statistical methods and the proper presentation of data is critical for companies that will invest money in developing the outcomes from scientific studies. Further, FAIR data stewardship is necessary to ensure the (re)usability of data. Proper data curation, data preservation, and information on data provenance reduces costs of added experiments and allows the use of data for subsequent studies that may not have been considered at the time the data was collected. Machine learning is a valuable tool for spotting patterns in large data sets that would take humans years to sort through, making it a valuable tool when understanding the sheer volume of data available in life science fields.
The courses offered by the Helis Academy will be continuously adapted based on feedback from course participants and industry representatives. Each time a course is completed, it will be critically assessed to determine where improvement is possible. To ensure industry relevance, a questionnaire has been sent to companies to inquire about their specific training needs. In addition, we are actively reaching out to companies for interviews about their challenges and training needs and for an inventory of possible company trainers. Feedback will be continuously incorporated in subsequent versions of the data analysis and stewardship courses to be sure we continue to address the evolving needs of companies in the Flanders and south Netherlands area.
This training provides an introduction to Multi-Omics Factor Analysis (MOFA2) for the integration of different omic data sets in an unsupervised fashion. It will enable you to run MOFA2 on multi-omic data, identify and explore the major drivers of variations across omics and use the inferred factors in various downstream analyses. Find more information and register here.
Through an engaging mix of introductions to key concepts and technologies, business insights and examples, you'll explore the reality of data science technologies today and how they can be harnessed to support your work. Focusing on key data science technologies, such as machine learning, the program helps you understand to implications of these new technologies for the future of the LSH sector. Learn more about this training and register here.
This online program is unique as it provides ample opportunity for you to interact with experts of TU/e as well as with the experienced experts from industry and to get new insights first-hand from them. At the end of the training, you should be able to understand basic principles of machine learning and deep learning. Selected examples of applications for medical image analysis will also be discussed. Find more information and register here.
The first Helis FAIR data stewardship course was offered from May 27-29, 2019, in the Darwin Incubator in Niel, Belgium and the second edition took place from November 4-6, 2019 in Utrecht, the Netherlands. The courses introduced the trainees to important concepts of data stewardship. We introduced the data life cycle, the FAIR principles and data stewardship. Furthermore, we interactively presented the stages of the data life cycle in more detail. In 2021, an online, updated third edition was organised from March 17-31.
The Helis Academy also offered a course on Omics data analysis through data integration using biological pathways, networks, and linear models from 11-14 June, 2019 at the Darwin Incubator in Niel, Belgium. Attendees left the course with knowledge about transcriptomics data analysis and multi-omics data integration. More information can be found here.
On the 3rd of March 2020, Helis Academy will organize a course on Statistical Thinking in Ghent. The course mission is to improve researcher's statistical understanding by elaborately discussing misconceptions in common statistical tools, and replacing them by more intelligent and flexible use of statistics. At the end of the course the participant will see statistics as an intrinsic part of their research that allows for objective decision making. More information is available here.
The next edition of the Helis Academy Omics Data Analysis course will take place at Maastricht University on 30-31 March, 2020. The target audience for this course is life scientists in academia and industry who want to learn to understand and use the basics of pathway and network biology. This course will provide a basic introduction to molecular pathways, biological networks, and how to analyse omics data using pathways and networks. We will introduce you to molecular pathways, from what they are, to how to make your own, to how to use them in omics data analysis (transcriptomics, proteomics, metabolomics). The course will continue by zooming out to utilize your pathway analysis in a broader network analysis and to integrate data from other external databases (e.g. drug-drug target databases, transcription factors, microRNA) so you can see the bigger picture. More information can be found here.
Experimental research in life sciences can be complex. Understanding complicated biological processes, quantifying the influence of environmental parameters on the growth of an organism, getting to grips with simultaneous impacting factors and their interactions, optimizing research procedures or media recipes: all these require factorial experiments. Factorial experiments can be very efficient, but the number of possible combinations quickly gets out of hand. Research facilities have limited capacity and have all sorts of constraints that do not fit the textbook solutions to factorial experiments. In the course factorial design and analysis (May 18 & 19), realistic research cases form the starting point for which we will try to find practical solutions. We will tackle research questions by design tailor-made factorial experiments within the budget and lab constraints. Power analysis will be key to achieve this goal. Finally, we will analyse the expected output in a statistical sound manner, interpret the results and propose presentations. You can find more info here.
Multi-omics or integrative omics analysis tools combine multiple "-omes" data sets to analyze complex biological data and retrieve novel associations. On 09th of June 2020, a training will be provided on Multi-Omics Factor Analysis (MOFA), a tool that analyzes your omic data sets in an unsupervised fashion. It will identify and explore the major drivers of variations and use the inferred factors in various downstream analysis. More information can be found here.
In the context of the Helis Academy, and as part of its data analysis and stewardship program, Eindhoven University of Technology (TU/e) and its partners provide a series of training sessions for (aspiring) professionals in the LSH sector on various methods, techniques, tools and best-practices related to data science, including "statistics", "data Mining using (automated) machine learning", "process-aware data mining using process mining", "deep learning using neural networks" and "visual analytics". You can find more info here.
In the era of Big Data, the tsunami of massive ‘omics’ data is revolutionizing the way we do science. Life science researchers are no longer analyzing one data set at a time but are moving towards multi-disciplinary integrative biology. It has been demonstrated that integration of different ‘omics’ data types (such as on genomes, transcriptomes, proteomes, epigenomes, etc..), boosts biological discoveries and improves predictions of the underlying interactions and regulation among molecular entities. Integrating different ‘omics’ datasets is a challenging task that relies heavily on data mining and machine learning.
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