Chih-Hsu Lin1, Daniel M Konecki1, Meng Liu2, Stephen J Wilson3, Huda Nassar2, Angela D Wilkins4,5, David F Gleich2, Olivier Lichtarge1,3,4,5. 1. Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA. 2. Department of Computer Science, Purdue University, West Lafayette, IN, USA. 3. Department of Biochemistry and Molecular Biology, Houston, TX, USA. 4. Departments of Molecular and Human Genetics, and Pharmacology, Houston, TX, USA. 5. Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, USA.
Abstract
MOTIVATION: Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction. RESULTS: We study multimodal networks linking diseases, genes and chemicals (drugs) by applying three diffusion algorithms and varying information content. Ten-fold cross-validation shows that these networks are internally consistent, both within and across association types. Also, diffusion methods recovered missing edges, even if all the edges from an entire mode of association were removed. This suggests that information is transferable between these association types. As a realistic validation, time-stamped experiments simulated the predictions of future associations based solely on information known prior to a given date. The results show that many future published results are predictable from current associations. Moreover, in most cases, using more association types increases prediction coverage without significantly decreasing sensitivity and specificity. In case studies, literature-supported validation shows that these predictions mimic human-formulated hypotheses. Overall, this study suggests that diffusion over a more comprehensive multimodal network will generate more useful hypotheses of associations among diseases, genes and chemicals, which may guide the development of precision therapies. AVAILABILITY AND IMPLEMENTATION: Code and data are available at https://github.com/LichtargeLab/multimodal-network-diffusion. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction. RESULTS: We study multimodal networks linking diseases, genes and chemicals (drugs) by applying three diffusion algorithms and varying information content. Ten-fold cross-validation shows that these networks are internally consistent, both within and across association types. Also, diffusion methods recovered missing edges, even if all the edges from an entire mode of association were removed. This suggests that information is transferable between these association types. As a realistic validation, time-stamped experiments simulated the predictions of future associations based solely on information known prior to a given date. The results show that many future published results are predictable from current associations. Moreover, in most cases, using more association types increases prediction coverage without significantly decreasing sensitivity and specificity. In case studies, literature-supported validation shows that these predictions mimic human-formulated hypotheses. Overall, this study suggests that diffusion over a more comprehensive multimodal network will generate more useful hypotheses of associations among diseases, genes and chemicals, which may guide the development of precision therapies. AVAILABILITY AND IMPLEMENTATION: Code and data are available at https://github.com/LichtargeLab/multimodal-network-diffusion. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: David S Wishart; Craig Knox; An Chi Guo; Savita Shrivastava; Murtaza Hassanali; Paul Stothard; Zhan Chang; Jennifer Woolsey Journal: Nucleic Acids Res Date: 2006-01-01 Impact factor: 16.971
Authors: Marco Carraro; Alexander Miguel Monzon; Luigi Chiricosta; Francesco Reggiani; Maria Cristina Aspromonte; Mariagrazia Bellini; Kymberleigh Pagel; Yuxiang Jiang; Predrag Radivojac; Kunal Kundu; Lipika R Pal; Yizhou Yin; Ivan Limongelli; Gaia Andreoletti; John Moult; Stephen J Wilson; Panagiotis Katsonis; Olivier Lichtarge; Jingqi Chen; Yaqiong Wang; Zhiqiang Hu; Steven E Brenner; Carlo Ferrari; Alessandra Murgia; Silvio C E Tosatto; Emanuela Leonardi Journal: Hum Mutat Date: 2019-07-03 Impact factor: 4.878