Discovering Content through Text Mining for a Synthetic Biology Knowledge System.

Scientific articles contain a wealth of information about experimental methods and results describing biological designs. Due to its unstructured nature and multiple sources of ambiguity and variability, extracting this information from text is a difficult task. In this paper, we describe the development of the synthetic biology knowledge system (SBKS) text processing pipeline. The pipeline uses natural language processing techniques to extract and correlate information from the literature for synthetic biology researchers. Specifically, we apply named entity recognition, relation extraction, concept grounding, and topic modeling to extract information from published literature to link articles to elements within our knowledge system. Our results show the efficacy of each of the components on synthetic biology literature and provide future directions for further advancement of the pipeline.

Effects of data and entity ablation on multitask learning models for biomedical entity recognition.

MOTIVATION: Training domain-specific named entity recognition (NER) models requires high quality hand curated gold standard datasets which are time-consuming and expensive to create. Furthermore, the storage and memory required to deploy NLP models can be prohibitive when the number of tasks is large. In this work, we explore utilizing multi-task learning to reduce the amount of training data needed to train new domain-specific models. We evaluate our system across 22 distinct biomedical NER datasets and evaluate the extent to which transfer learning helps task performance using two forms of ablation. RESULTS: We found that multitasking models generally do not improve performance, but in many cases perform on par compared to single-task models. However, we show that in some cases, new unseen tasks can be trained as a single model using less data by starting with weights from a multitask model and improve performance. AVAILABILITY: The software underlying this article are available in: https://github.com/NLPatVCU/multitasking_bert-1.

Synthetic Biology Knowledge System.

The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.

Selective enrichment of microRNAs in extracellular matrix vesicles produced by growth plate chondrocytes.

Matrix vesicles (MVs) are membrane organelles found in the extracellular matrix of calcifying cells, which contain matrix processing enzymes and regulate the extracellular environment via action of these enzymes. It is unknown whether MVs are also exosomic mediators of cell-cell communication via transfer of RNA material, and specifically, microRNA (miRNA). We investigated the presence of RNA in MVs isolated from cultures of costochondral growth zone chondrocytes. Our results showed that the average yield of MV RNA was 1.93±0.78ng RNA/10(4) cells, which was approximately 0.1% of the parent cell's total RNA. MV RNA was well-protected from RNase by the lipid membrane and was highly enriched in small RNA molecules compared to cells. Moreover, coding and non-coding small RNAs in MVs were in proportions that differed from parent cells. Enrichment of specific miRNAs was consistently observed in all three miRNA detection platforms that we used, suggesting that miRNAs are selectively packaged into MVs. MV-enriched miRNAs were related to different signaling pathways associated with bone formation. This study suggests a significant role for MVs as "matrisomes" in cell-cell communication in cartilage and bone development via transfer of specific miRNAs.