Mathaeus Dejori

Extraction of semantic biomedical relations from text using conditional random fields

BackgroundThe increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.ResultsWe benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph.ConclusionWe extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining. Current work is focused on improving the accuracy of detection of entities as well as entity boundaries, which will also greatly improve the relation extraction performance.

Hierarchical Bayesian Models for Collaborative Tagging Systems

Collaborative tagging systems with user generated content have become a fundamental element of websites such as Delicious, Flickr or CiteULike. By sharing common knowledge, massively linked semantic data sets are generated that provide new challenges for data mining. In this paper, we reduce the data complexity in these systems by finding meaningful topics that serve to group similar users and serve to recommend tags or resources to users. We propose a well-founded probabilistic approach that can model every aspect of a collaborative tagging system. By integrating both user information and tag information into the well-known Latent Dirichlet Allocation framework, the developed models can be used to solve a number of important information extraction and retrieval tasks.

Hunting drug targets by systems-level modeling of gene expression profiles

Structural learning of Bayesian networks applied to sets of genome-wide expression patterns has been recently discovered as a potentially useful tool for the systems-level statistical description of gene interactions. We train and analyze Bayesian networks with the goal of inferring biological aspects of gene function. Our two-component approach focuses on supporting the drug discovery process by identifying genes with central roles for the network operation, which could act as drug targets. The first component, referred to as scale-free analysis, uses topological measures of the network-related to a high-traffic load of genes-as estimators for their functional importance. The second component, referred to as generative inverse modeling, is a method of estimating the effect of a simulated drug treatment or mutation on the global state of the network, as measured in the expression profile. We show for a dataset from acute lymphoblastic leukemia patients that both approaches are suitable for finding genes with central cellular functions. In addition, generative inverse modeling correctly identifies a known oncogene in a purely data-driven way.

Development of Data Infrastructure for the Long Term Bridge Performance Program

The growth of the National Bridge Inventory database, the availability of new data from embedded sensors, in-situ tests and live load tests together with additional bridge related data, e.g. geospatial and weather data, represents an immense source of information helpful for a better understanding of bridge performance and deterioration. However, in order to efficiently exploit this overwhelming amount of information a new generation of data management and data analysis tools is needed. In this paper we describe an open, scalable, and extensible data management and data analysis infrastructure which will be established within the framework of the Long Term Bridge Performance Program (LTBP). MOTIVATION Highway bridges play an important role in the national transportation network. Like any other infrastructure asset, bridges deteriorate with time and require regular maintenance to continue operating at an acceptable level. Often, funding constraints makes it impossible for the bridge owners to perform all maintenance activities that should be carried out at a given time, and they must face the difficult decision of selecting a small subset of maintenance activities that could be performed within available resources, while maximizing the return on investment. Making educated decisions on maintenance activities require bridge owners and other stakeholders to better understand bridge performance. More specifically, given the current condition of a bridge, owners must understand how each of the recommended maintenance activities may impact the overall bridge function, and which activities provide the best costbenefit tradeoff. However, understanding bridge performance is non-trivial and requires an indepth analysis of how bridges function and behave under various complex and interrelated factors and stresses, including but not limited to traffic volumes, overall load, weather conditions, environmental assaults, age, materials, design and prior maintenance history of the bridge.