Workshop on Future Directions in Network Biology
June 12-14, 2022
Morris Inn, University of Notre Dame (ND), Indiana, USA
Workshop summary
Network biology, a field in the intersection of computer science and biology, is critical for deepening understanding of cellular functioning and disease. While the field is relatively young, there have already been rapid changes to it and new computational/algorithmic challenges have arisen, owing to many factors, including increasing data complexity. So, research directions in the field need to evolve accordingly. This workshop, funded by NSF award CCF-1941447, aims to identify pressing challenges in this field and propose solutions to the challenges. Thus, the workshop will help shape the short- and long-term vision for algorithmic research in network biology.
The workshop is a targeted meeting of scientists (primarily faculty or equivalent researchers in industry or government) who are doing active and state-of-the-art research in various aspects of network biology. The workshop will be held in person. The in-person component is critical for the planned highly interactive nature of the workshop, which is why we have waited for two years to hold this workshop, which was originally planned for 2020. Note that because of the pandemic-related uncertainty and changing policies related to international travel, we have been unable to invite for in-person participation those located outside of the United States. Yet, to allow for participation of international network biology researchers and others who have been unable to travel to the workshop venue, an online component of the workshop will be offered as well (details will be sent to confirmed online workshop participants). The in-person workshop participants will coordinate into teams corresponding to several key scientific topics in the field of network biology, and present their views of important current and future research directions on the topics. All (in-person and online workshop participants) will then have a chance to engage into lively discussions on any and all of the topics. A goal is to understand how the field of computer science and algorithms in particular is benefiting the field of network biology, and vice versa, and how to strengthen this synergy even further.
Organizers
Tijana Milenkovic, Frank M. Freimann Collegiate Professor of Engineering, University of Notre Dame
Jinbo Xu, Professor, Toyota Technological Institute at Chicago
Marinka Zitnik, Assistant Professor of Biomedical Informatics, Harvard Medical School
Schedule
In-person workshop participants
Serdar Bozdag, Associate Professor, Computer Science and Engineering, University of North Texas
Danny Chen, Professor, Computer Science and Engineering, University of Notre Dame
Kapil Devkota, PhD. Candidate, Computer Science, Tufts University
Anthony Gitter, Associate Professor, Biostatistics and Medical Informatics, University of Wisconsin-Madison
Kimberly Glass, Assistant Professor, Medicine, Harvard Medical School
Sara Gosline, Team Lead, Molecular Analytics, Pacific Northwest National Laboratory
Pengfei Gu, Ph.D. Student, Computer Science and Engineering, University of Notre Dame
Heng Huang, Professor, Electrical and Computer Engineering, University of Pittsburg
Meng Jiang, Assistant Professor, Computer Science and Engineering, University of Notre Dame
Arjun Krishnan, Assistant Professor, Computational Mathematics, Science and Engineering & Biochemistry and Molecular Biology, Michigan State University
Michelle Li, Ph.D. Candidate, Biomedical Informatics, Harvard Medical School
Gang Liu, Ph.D. Student, Computer Science and Engineering, University of Notre Dame
Tijana Milenkovic, Professor, Computer Science and Engineering, University of Notre Dame
Deisy Morselli Gysi, Associate Research Scientist, Network Science Institute, Northeastern University
T. M. Murali, Professor, Computer Science, Virginia Tech
Ziynet Nesibe Kesimoglu, Ph.D. Candidate, Computer Science and Engineering, University of North Texas
Michael Pfrender, Professor, Biology, University of Notre Dame
Alex Pico, Core Director, Bioinformatics, Gladstone Institutes
Teresa Przytycka, Senior Investigator, National Library of Medicine, National Institutes of Health
Predrag Radivojac, Professor, Computer Science, Northeastern University
Ben Raphael, Professor, Computer Science, Princeton University
Anna Ritz, Associate Professor, Biology, Reed College
Sushmita Roy, Associate Professor, Biostatistics and Medical Informatics, University ofWisconsin-Madison
Yang Shen, Associate Professor, Electrical and Computer Engineering, Texas A&M University
Mona Singh, Professor, Computer Science, Princeton University
Donna Slonim, Professor, Computer Science, Tufts University
Hanghang Tong, Associate Professor, Computer Science, University of Illinois Urbana-Champaign
Xinan Holly Yang, Research Associate Professor, Pediatrics, University of Chicago
Byung-Jun Yoon, Associate Professor, Electrical and Computer Engineering, Texas A&M University
Haiyuan Yu, Professor, Computational Biology, Cornell University
Marinka Zitnik, Assistant Professor, Biomedical Informatics, Harvard Medical School
Online workshop participants
Gary Bader, Professor, Donnelly Centre, University of Toronto
Mitra Basu, Program Director, Division of Computing and Communication Foundations (CISE/CCF), National Science Foundation
Anais Baudot, Networks and Systems Biology for Diseases, Marseille Medical Genetics
Carlo Cannistraci, Professor, Laboratory of Brain and Intelligence, Tsinghua University
Lenore Cowen, Professor, Computer Science, Tufts University
Pietro Guzzi, Associate Professor, Medical and Surgical Sciences, University of Catanzaro
Mehmet Koyuturk, Professor, Computer and Data Sciences, Case Western Reserve University
Jian Ma, Professor, Computer Science, Carnegie Mellon University
Roded Sharan, Professor, Computer Science, Tel-Aviv University
Scientific sessions, presentation slides, and workshop videos
Representative fundamental, influential, or recent papers (this is not an exhaustive list)
- Higher-order network analysis:
- Uri Alon, Network motifs: theory and experimental approaches. 2007.
- Tijana Milenkovic and Natasa Przulj, Uncovering biological network function via graphlet degree signatures. 2008.
- Tomaz Hocevar and Janez Demsar. A combinatorial approach to graphlet counting. 2014.
- Yuriy Hulovatyy et al., Exploring the structure and function of temporal networks with dynamic graphlets. 2015.
- Ugur Dogrusoz et al., Algorithms for effective querying of compound graph-based pathway databases. 2009.
- Steffen Klamt et al., Hypergraphs and cellular networks. 2009.
- Nicholas Franzese et al., Hypergraph-based connectivity measures for signaling pathway topologies. 2019.
- Jian Xu et al., Representing higher-order dependencies in networks. 2016.
- Elena Kuzmin et al., Systematic analysis of complex genetic interactions. 2018.
- Istvan Kovacs et al., Network-based prediction of protein interactions. 2019.
- Emily Alsentzer et al., Subgraph neural networks. 2020.
- Christian Wiwie et al., Comparing the performance of biomedical clustering methods. 2015.
- Santo Fortunato. Community detection in graphs. 2010.
- Hao Yin et al., Higher-order clustering in networks. 2018.
- Multimodal networks and integration:
- Yuxiao Dong et al., metapath2vec: scalable representation learning for heterogeneous networks. 2017.
- Bo Wang et al., Similarity network fusion for aggregating data types on a genomic scale. 2014.
- Tongxin Wang et al., MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. 2021.
- Vladimir Gligorijevic and Natasa Przulj, Methods for biological data integration: perspectives and challenges. 2015.
- Teresa Przytycka, Network integration meets network dynamics. 2010.
- Koyel Mitra et al., Integrative approaches for finding modular structure in biological networks. 2013.
- Casey Greene et al., Understanding multicellular function and disease with human tissue-specific networks. 2015.
- Daniel Scott Himmelstein et al., Systematic integration of biomedical knowledge prioritizes drugs for repurposing. 2017.
- Marinka Zitnik et al., Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. 2019.
- Pisanu Buphamalai et al., Network analysis reveals rare disease signatures across multiple levels of biological organization. 2021.
- Jingchao Ni, Inside the atoms: ranking on a network of networks. 2014.
- Shawn Gu et al., Modeling multi-scale data via a network of networks. 2022.
- Inference and comparison of biological networks:
- Florian Markowetz and Rainer Spang, Inferring cellular networks – a review. 2007.
- Van Anh Huynh-Thu et al., Inferring regulatory networks from expression data using tree-based methods. 2010.
- Riet De Smet and Kathleen Marchal, Advantages and limitations of current network inference methods. 2010.
- Daniel Marbach et al., Wisdom of crowds for robust gene network inference. 2012.
- Shao-Shan Carol Huang and Ernest Fraenkel, Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. 2009.
- Janusz Dutkowski et al., A gene ontology inferred from molecular networks. 2013.
- Michael Costanzo et al., A global genetic interaction network maps a wiring diagram of cellular function. 2016.
- Alireza Fotuhi Siahpirani et al., Integrative approaches for inference of genome-scale gene regulatory networks. 2019.
- Thalia Chan et al., Gene regulatory network inference from single-cell data using multivariate information measures. 2017.
- Mark Fiers et al., Mapping gene regulatory networks from single-cell omics data. 2018.
- Shahin Mohammadi et al., Reconstruction of cell-type-specific interactomes at single-cell resolution. 2019.
- Aditya Pratapa et al., Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. 2020.
- Deisy Morselli Gysi et al., Whole transcriptomic network analysis using co-expression differential network analysis (CoDiNA). 2020.
- Bo Wang et al., Network enhancement as a general method to denoise weighted biological networks. 2018.
- Lenore Cowen et al., Network propagation: a universal amplifier of genetic associations. 2017.
- Fabio Vandin et al., Algorithms for detecting significantly mutated pathways in cancer. 2011.
- Mark Leiserson et al., Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. 2015.
- Amir Ghasemian et al., Stacking models for nearly optimal link prediction in complex networks. 2020.
- Natasa Przulj, Biological network comparison using graphlet degree distribution. 2007.
- Roded Sharan, Comparative analysis of protein networks: hard problems, practical solutions. 2012.
- Frank Emmert-Streib et al., Fifty years of graph matching, network alignment and network comparison. 2016.
- Pietro Guzzi and Tijana Milenkovic, Survey of local and global biological network alignment: the need for reconciling the two sides of the same coin. 2018.
- Xinan Yang et al., Detecting critical transition signals from single-cell transcriptomes to infer lineage-determining transcription factors. 2022.
- Machine learning on networks:
- Will Hamilton et al., Representation learning on graphs: methods and applications. 2017.
- Will Hamilton et al., Inductive representation learning on large graphs. 2017.
- Yukuo Cen et al., Representation learning for attributed multiplex heterogeneous network. 2019.
- Petar Velickovic et al., Graph attention networks. 2018.
- Thomas Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. 2017.
- Aditya Grover and Jure Leskovec. node2vec: scalable feature learning for networks. 2016.
- Nicola De Cao and Thomas Kipf, MolGAN: an implicit generative model for small molecular graphs. 2018.
- Walter Nelson et al., To embed or not: Network embedding as a paradigm in computational biology. 2019.
- Jianzhu Ma et al., Using deep learning to model the hierarchical structure and function of a cell. 2018.
- Marinka Zitnik et al., Modeling polypharmacy side effects with graph convolutional networks. 2018.
- Haitham Elmarakeby et al., Biologically informed deep neural network for prostate cancer discovery. 2021.
- Michelle Li et al., Graph representation learning in biomedicine. 2021.
- Network-based personalized medicine in and outside of clinic:
- Trey Ideker and Roded Sharan. Protein networks in disease. 2008.
- Albert-Laszlo Barabasi et al., Network medicine: a network-based approach to human disease. 2011.
- Edwin Silverman and Joseph Loscalzo, Network medicine approaches to the genetics of complex diseases. 2012.
- Matan Hofree et al., Network-based stratification of tumor mutations. 2013.
- Emre Guney et al., Network-based in silico drug efficacy screening. 2016.
- Feixiong Cheng et al., Network-based prediction of drug combinations. 2019.
- Camilo Ruiz et al., Identification of disease treatment mechanisms through the multiscale interactome. 2021.
- Yiheng Zhu et al., TGSA: protein–protein association-based twin graph neural networks for drug response prediction with similarity augmentation. 2022.
- Kirsten Smith and Nicholas Christakis, Social networks and health, 2008.
- Shikang Liu et al., Heterogeneous network approach to predict individuals' mental health. 2021.
- Marylyn Ritchie et al., The foundation of precision medicine: integration of electronic health records with genomics through basic, clinical, and translational research. 2015.
- Jiansong Fang et al., Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease. 2021.
- Other, more general network biology papers:
- COVID-19-specific network biology papers:
University of Notre Dame's policy on sexual harassment, other forms of harassment, and sexual assault
The Notre Dame's policy on sexual and other forms of harassment will apply to all workshop participants. The policy and reporting procedures are available as follows: