Recent progress in high-throughput technology has generated vast amounts of data. Determining how to obtain valuable knowledge from such massive raw data is a challenging issue in biomedical research and consequently developing advanced data analysis tools for bioinformatics is an important research area. While a number of methods for extracting knowledge from data are available, there has been very little work on ensuring the robustness, reproducibility, or consistency of the results. This creates a lack of confidence in bioinformatics methods as well as a lack of the “bench to bedside” connection desired of biomedical and clinical research. Particularly, these reservations stem from a lack of testing on the reliability of the model. Guaranteeing reproducibility, rigor, and consistency analysis represents an important step toward better understanding of the critical noise versus signal issue in bioinformatics models. Our workshop aims to bring together researchers in data analysis and bioinformatics, as well as wet-lab biologists, to discuss the importance of reproducibility and consistency of biological data and methods to achieve it particularly in the context of network analysis.

The target audience of the workshop is bioinformaticians, biologists conducting “wet lab” or “at the bench” experiments, data analysts, and computational scientists who can collaborate together to improve and test the rigor and robustness of current methods in data analytics. We also hope to attract the attention of physicians and healthcare providers who are waiting patiently for decisive evidences to adopt results obtained from the Bioinformatics community. The workshop program will include a keynote talk, a panel, and peer-reviewed contributed papers.

The objective of our workshop is to promote awareness of the need for rigor in biological data and to highlight methods that can improve the correctness of data analysis. The workshop will address these issues with a particular focus on the following objectives:
1. Disseminate knowledge about the current state of the multidisciplinary field of network modeling in bioinformatics and biomedical informatics as it relates to current big data trends.
2. Promote the use of advanced network analysis in extracting knowledge from big data associated with -biomedical informatics and medical research
3. Encourage the development of a standard for big biomedical data research that utilizes existing science about complex systems including network theory, game theory, pattern formation, decision support and non-linear dynamics.


Potential topics for this workshop include:
- Pros and cons of machine learning methods for data analysis
- Anomaly detection in biological image processing
- High-throughput assay preparation, execution, and analysis of results
- Methods for multiple-hypothesis testing
- Accuracy and sensitivity of Next Generation sequencing and assembly methods
- Algorithmic transparency and communication standards
- Network modeling – creation, thresholds, and filtering
- “Omics” methods for data aggregation
- Biomedical data fusion
- Verification and validation methods for sorting biological noise from signal
- Trustworthiness of in silico methods without benchmarking studies

See our website for more details: https://sites.google.com/unomaha.edu/rrobin2018/