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Ivan Iossifov

Publications and source records attributed to Ivan Iossifov.

6 recordsLinked to original sources
PubMed2006

Imitating manual curation of text-mined facts in biomedicine.

Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted) of individual facts--to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations), we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95). Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine.

Abstracting and Indexing↗
PubMed2006

Microparadigms: chains of collective reasoning in publications about molecular interactions.

We analyzed a very large set of molecular interactions that had been derived automatically from biological texts. We found that published statements, regardless of their verity, tend to interfere with interpretation of the subsequent experiments and, therefore, can act as scientific "microparadigms," similar to dominant scientific theories [Kuhn, T. S. (1996) The Structure of Scientific Revolutions (Univ. Chicago Press, Chicago)]. Using statistical tools, we measured the strength of the influence of a single published statement on subsequent interpretations. We call these measured values the momentums of the published statements and treat separately the majority and minority of conflicting statements about the same molecular event. Our results indicate that, when building biological models based on published experimental data, we may have to treat the data as highly dependent-ordered sequences of statements (i.e., chains of collective reasoning) rather than unordered and independent experimental observations. Furthermore, our computations indicate that our data set can be interpreted in two very different ways (two "alternative universes"): one is an "optimists' universe" with a very low incidence of false results (<5%), and another is a "pessimists' universe" with an extraordinarily high rate of false results (>90%). Our computations deem highly unlikely any milder intermediate explanation between these two extremes.

Computer Simulation↗
PubMed2004

Probabilistic inference of molecular networks from noisy data sources.

Information on molecular networks, such as networks of interacting proteins, comes from diverse sources that contain remarkable differences in distribution and quantity of errors. Here, we introduce a probabilistic model useful for predicting protein interactions from heterogeneous data sources. The model describes stochastic generation of protein-protein interaction networks with real-world properties, as well as generation of two heterogeneous sources of protein-interaction information: research results automatically extracted from the literature and yeast two-hybrid experiments. Based on the domain composition of proteins, we use the model to predict protein interactions for pairs of proteins for which no experimental data are available. We further explore the prediction limits, given experimental data that cover only part of the underlying protein networks. This approach can be extended naturally to include other types of biological data sources.

Algorithms↗
PubMed2004

GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data.

The immense growth in the volume of research literature and experimental data in the field of molecular biology calls for efficient automatic methods to capture and store information. In recent years, several groups have worked on specific problems in this area, such as automated selection of articles pertinent to molecular biology, or automated extraction of information using natural-language processing, information visualization, and generation of specialized knowledge bases for molecular biology. GeneWays is an integrated system that combines several such subtasks. It analyzes interactions between molecular substances, drawing on multiple sources of information to infer a consensus view of molecular networks. GeneWays is designed as an open platform, allowing researchers to query, review, and critique stored information.

Artificial Intelligence↗
PubMed2002

Of truth and pathways: chasing bits of information through myriads of articles.

Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarily hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.

Algorithms↗