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Motivation A significant portion of molecular biology investigates signalling pathways and thus depends on an up-to-date and complete resource of functional protein–protein associations (PPAs) that constitute such pathways. Despite extensive curation efforts, major pathway databases are still notoriously incomplete. Relation extraction can help to gather such pathway information from biomedical publications. Current methods for extracting PPAs typically rely exclusively on rare manually labelled data which severely limits their performance. Results We propose PPA Extraction with Deep Language (PEDL), a method for predicting PPAs from text that combines deep language models and distant supervision. Due to the reliance on distant supervision, PEDL has access to an order of magnitude more training data than methods solely relying on manually labelled annotations. We introduce three different datasets for PPA prediction and evaluate PEDL for the two subtasks of predicting PPAs between two proteins, as well as identifying the text spans stating the PPA. We compared PEDL with a recently published state-of-the-art model and found that on average PEDL performs better in both tasks on all three datasets. An expert evaluation demonstrates that PEDL can be used to predict PPAs that are missing from major pathway databases and that it correctly identifies the text spans supporting the PPA. Availability and implementation PEDL is freely available at https://github.com/leonweber/pedl. The repository also includes scripts to generate the used datasets and to reproduce the experiments from this article. Supplementary information Supplementary data are available at Bioinformatics online.

The innate immune response of Anopheles gambiae involves the transcriptional upregulation of effector genes. Therefore, the cis-regulatory sequences and their cognate binding factors play essential roles in the mosquito's immune response. However, the genetic control of the mosquito's innate immune response is not yet fully understood. To gain further insight on the elements, the factors and the potential mechanisms involved, an open chromatin profiling was carried out on A. gambiae-derived immune-responsive cells. Here, we report the identification of cis-regulatory sites, immunity-related transcription factor binding sites, and cis-regulatory modules. A de novo motif discovery carried out on this set of cis-regulatory sequences identified immunity-related motifs and cis-regulatory modules. These modules contain motifs that are similar to binding sites for REL-, STAT-, lola- and Deaf1-type transcription factors. Sequence motifs similar to the binding sites for GAGA were found within a cis-regulatory module, together with immunity-related transcription factor binding sites. The presence of Deaf1- and lola-type binding sites, along with REL- and STAT-type binding sites, suggests that the immunity function of these two factors could have been conserved both in Drosophila and Anopheles gambiae.