There is an increasing interest in integrating literature information on molecular-level processes (such as published research results on molecular interactions between genes, proteins and other entities) with other types/sources of information (such as genetic linkage information). Given the ever-growing size of the biomedical literature, much research has been devoted to automate the parsing and extraction of research results from scientific articles. I will first discuss our experience with working on an automated text mining pipeline for extracting molecular network information from the literature. In a second part, I will be presenting our research on network-based data modeling and analysis: Using Bayesian inference models and Markov Chain Monte Carlo (MCMC) techniques to explore properties of molecular network information, and developing graph-based approaches for pinpointing (genetic linkage-derived) disease genes in molecular networks.