Network embedding learns the vector representations of nodes. Most real world networks are heterogeneous and evolve over time. There are, however, no network embedding approaches designed for dynamic heterogeneous networks so far. Addressing this research gap is beneficial for analyzing and mining real world networks. We develop a novel representation learning method, change2vec, which considers a dynamic heterogeneous network as snapshots of networks with different time stamps. Instead of processing the whole network at each time stamp, change2vec models changes between two consecutive static networks by capturing newly-added and deleted nodes with their neighbour nodes as well as newly-formed or deleted edges that caused core structural changes known as triad closure or open processes. Change2vec leverages metapath based node embedding and change modeling to preserve both heterogeneous and dynamic features of a network. Experimental results show that change2vec outperforms two state-of-the-art methods in terms of clustering performance and efficiency.

Computational models in biomedicine rely on biological and clinical assumptions. The selection of these assumptions contributes substantially to modeling success or failure. Assumptions used by experts at the cutting edge of research, however, are rarely explicitly described in scientific publications. One can directly collect and assess some of these assumptions through interviews and surveys. Here we investigate diversity in expert views about a complex biological phenomenon, the process of cancer metastasis. We harvested individual viewpoints from 28 experts in clinical and molecular aspects of cancer metastasis and summarized them computationally. While experts predominantly agreed on the definition of individual steps involved in metastasis, no two expert scenarios for metastasis were identical. We computed the probability that any two experts would disagree on k or fewer metastatic stages and found that any two randomly selected experts are likely to disagree about several assumptions. Considering the probability that two or more of these experts review an article or a proposal about metastatic cascades, the probability that they will disagree with elements of a proposed model approaches 1. This diversity of conceptions has clear consequences for advance and deadlock in the field. We suggest that strong, incompatible views are common in biomedicine but largely invisible to biomedical experts themselves. We built a formal Markov model of metastasis to encapsulate expert convergence and divergence regarding the entire sequence of metastatic stages. This model revealed stages of greatest disagreement, including the points at which cancer enters and leaves the bloodstream. The model provides a formal probabilistic hypothesis against which researchers can evaluate data on the process of metastasis. This would enable subsequent improvement of the model through Bayesian probabilistic update. Practically, we propose that model assumptions and hunches be harvested systematically and made available for modelers and scientists.

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them.

UNLABELLED: The BioText Search Engine is a freely available Web-based application that provides biologists with new ways to access the scientific literature. One novel feature is the ability to search and browse article figures and their captions. A grid view juxtaposes many different figures associated with the same keywords, providing new insight into the literature. An abstract/title search and list view shows at a glance many of the figures associated with each article. The interface is carefully designed according to usability principles and techniques. The search engine is a work in progress, and more functionality will be added over time. AVAILABILITY: http://biosearch.berkeley.edu.

SUMMARY: BioIE is a rule-based system that extracts informative sentences relating to protein families, their structures, functions and diseases from the biomedical literaturE. Based on manual definition of templates and rules, it aims at precise sentence extraction rather than wide recall. After uploading source text or retrieving abstracts from MEDLINE, users can extract sentences based on predefined or user-defined template categories. BioIE also provides a brief insight into the syntactic and semantic context of the source-text by looking at word, N-gram and MeSH-term distributions. Important Applications of BioIE are in, for example, annotation of microarray data and of protein databases. AVAILABILITY: http://umber.sbs.man.ac.uk/dbbrowser/bioie/

SUMMARY: METIS is a web-based integrated annotation tool. From single query sequences, the PRECIS component allows users to generate structured protein family reports from sets of related Swiss-Prot entries. These reports may then be augmented with pertinent sentences extracted from online biomedical literature via support vector machine and rule-based sentence classification systems. AVAILABILITY: http://umber.sbs.man.ac.uk/dbbrowser/metis/