Biomedical subjects

Andreas Persidis

Publications and source records attributed to Andreas Persidis.

4 recordsLinked to original sources

PubMed2006

Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach.

OBJECTIVE: The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. MATERIALS AND METHODS: This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. RESULTS: We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. CONCLUSION: We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.

Animals↗

PubMed2005

Novel biological networks modulated by complement.

The almost complete deciphering of the human genome has paved the way for the application of new technology platforms in understanding the contribution of complex biological pathways to human pathophysiology and disease. In the post-genomic era, the concept of systems biology has gained significant momentum and biomedical research is now being conducted on an integrated and cross-disciplinary platform that pulls together its resources from diverse fields such as computational biology, bioinformatics, functional genomics, structural biology, and proteomics. In this perspective, the identity of established biologic systems is being re-examined in the light of novel findings that suggest novel associations between otherwise unrelated pathways and individual proteins. Complement exemplifies such a system that, transcending its innate immune identity, has forged functional associations with multiple pathways and networks in modulating basic biologic processes. In the present article, we provide a global overview of these unusual system associations of complement with the aid of a powerful and high-throughput bioinformatics platform. Using a novel approach called systems literature analysis that allows the rapid extraction of text-based associations between genes and pathways from the ever expanding scientific article database, we have selected a broad range of biologic processes modulated by complement proteins and have constructed an integrated map of complement-mediated networks that incorporates well over 85 diverse biologic pathways. Expanding the complement cascade beyond its approximately 35 designated components, we discuss protein-protein interactions involving novel ligands and associations with signaling cascades and cellular networks that affect both inflammatory and non-inflammatory processes. This integrated consideration of complement within a unified 'systems biology' framework underscores the concept that innate immunity goes well beyond the protection of 'self' extending links to critical developmental, homeostatic, and metabolic processes.

Complement System Proteins↗

PubMed2005

Identification of echocardiographic indices for the early detection of left-ventricular systolic dysfunction in beta-thalassaemia via Self-Organizing Maps: a data-exploration study.

Congestive heart failure (CHF) remains the primary cause of death in patients suffering from beta-thalassaemia major. Its early detection allows the prompt initiation of aggressive chelation therapy, when the condition can still be reversed. We aimed at identifying echocardiographic indices for the early detection of left ventricular (LV) systolic dysfunction, the physiological abnormality underlying CHF, in these patients. We used Self-Organizing Maps (SOMs)--an artificial neural network--for identifying novel correlations within our Electronic Healthcare Record (EHCR) database on beta-thalassaemia. We sought echocardiographic parameters that are correlated to future deterioration of the LV ejection fraction and therefore constitute early signs of LV systolic dysfunction. At the same time, we evaluated SOMs as tools for exploring clinical datasets and make recommendations on the setup of the SOM algorithm that is appropriate for such tasks. We found that high values of the LV end-systolic diameter index and of the E/A ratio are early indications of LV systolic dysfunction. From a technical point of view, zero-mean unit-variance normalization of the input data, a large initial neighbourhood radius and a rectangular SOM grid produced optimal maps for the purpose of detecting clinical correlations. We have successfully used SOMs for exploring a clinical dataset and for creating novel medical hypotheses. A clinical study has been launched to confirm these hypotheses, and initial results are encouraging.

Adult↗

PubMed2004

Systems literature analysis.

Systems literature analysis (SLA) is the literature-driven version of systems biology. It treats collections of scientific literature as a system of millions of interconnections between research parameters, such as genes, diseases, tissues, cell events, model organisms, experiment types, and reagents. SLA aims to replace the traditional keyword-based querying of literature databases, which return sorted lists of papers, with a systems-based approach that returns integrated networks of relationships. Major applications include literature-based discovery of novel targets that link, for example, previously unlinked diseases and phenotypes to common genes and cellular events, and experiment design.

Databases, Bibliographic↗