An ontological modeling approach to cerebrovascular disease studies: The NEUROWEB case
- Gianluca Colombo,
- Daniele Merico,
- Giorgio Boncoraglio,
- Flavio De Paoli,
- John Ellul,
- Giuseppe Frisoni,
- Zoltan Nagy,
- Aad van der Lugt,
- István Vassányi,
- Marco Antoniotti
The NEUROWEB project supports cerebrovascular researchers' association studies, intended as the search for statistical correlations between a feature (e.g., a genotype) and a phenotype. In this project the phenotype refers to the patients' pathological ...
Comparing risks of alternative medical diagnosis using Bayesian arguments
This paper explains the role of Bayes Theorem and Bayesian networks arising in a medical negligence case brought by a patient who suffered a stroke as a result of an invasive diagnostic test. The claim of negligence was based on the premise that an ...
Background adjustment of cDNA microarray images by Maximum Entropy distributions
Many empirical studies have demonstrated the exquisite sensitivity of both traditional and novel statistical and machine intelligence algorithms to the method of background adjustment used to analyze microarray datasets. In this paper we develop a ...
Mining connections between chemicals, proteins, and diseases extracted from Medline annotations
The biomedical literature is an important source of information about the biological activity and effects of chemicals. We present an application that extracts terms indicating biological activity of chemicals from Medline records, associates them with ...
Authoring and verification of clinical guidelines: A model driven approach
Objectives: The goal of this research is to provide a framework to enable authoring and verification of clinical guidelines. The framework is part of a larger research project aimed at improving the representation, quality and application of clinical ...
Drug target identification in sphingolipid metabolism by computational systems biology tools: Metabolic control analysis and metabolic pathway analysis
Sphingolipids regulate cellular processes that are critically important in cell's fate and function in cancer development and progression. This fact underlies the basics of the novel cancer therapy approach. The pharmacological manipulation of the ...
Unraveling complex temporal associations in cellular systems across multiple time-series microarray datasets
Unraveling the temporal complexity of cellular systems is a challenging task, as the subtle coordination of molecular activities cannot be adequately captured by simple mathematical concepts such as correlation. This paper addresses the challenge with a ...
Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values
Distance based clustering algorithms can group genes that show similar expression values under multiple experimental conditions. They are unable to identify a group of genes that have similar pattern of variation in their expression values. Previously ...
Exploring the ncRNA-ncRNA patterns based on bridging rules
ncRNAs play an important role in the regulation of gene expression. However, many of their functions have not yet been fully discovered. There are complicated relationships between ncRNAs in different categories. Finding these relationships can ...
Learning predictive models that use pattern discovery-A bootstrap evaluative approach applied in organ functioning sequences
An important problem in the Intensive Care is how to predict on a given day of stay the eventual hospital mortality for a specific patient. A recent approach to solve this problem suggested the use of frequent temporal sequences (FTSs) as predictors. ...
UMLS content views appropriate for NLP processing of the biomedical literature vs. clinical text
Identification of medical terms in free text is a first step in such Natural Language Processing (NLP) tasks as automatic indexing of biomedical literature and extraction of patients' problem lists from the text of clinical notes. Many tools developed ...
Selecting information in electronic health records for knowledge acquisition
Knowledge acquisition of relations between biomedical entities is critical for many automated biomedical applications, including pharmacovigilance and decision support. Automated acquisition of statistical associations from biomedical and clinical ...
The development and validation of a simulation tool for health policy decision making
Computer simulations have been used to model infectious diseases to examine the outcomes of alternative strategies for managing their spread. Methicillin resistant Staphylococcus aureus (MRSA) skin and soft tissue infections have become prominent in ...
A network-theoretic approach for decompositional translation across Open Biological Ontologies
Biological ontologies are now being widely used for annotation, sharing and retrieval of the biological data. Many of these ontologies are hosted under the umbrella of the Open Biological Ontologies Foundry. In order to support interterminology mapping, ...
Learning Bayesian networks from survival data using weighting censored instances
Different survival data pre-processing procedures and adaptations of existing machine-learning techniques have been successfully applied to numerous fields in clinical medicine. Zupan et al. (2000) proposed handling censored survival data by assigning ...
Automatic indexing and retrieval of encounter-specific evidence for point-of-care support
Evidence-based medicine relies on repositories of empirical research evidence that can be used to support clinical decision making for improved patient care. However, retrieving evidence from such repositories at local sites presents many challenges. ...
Proclets in healthcare
Healthcare processes can be characterized as weakly-connected interacting light-weight workflows coping with different levels of granularity. Classical workflow notations fall short in supporting these kind of processes. Although these notations are ...
Methodological Review: Text mining for traditional Chinese medical knowledge discovery: A survey
Extracting meaningful information and knowledge from free text is the subject of considerable research interest in the machine learning and data mining fields. Text data mining (or text mining) has become one of the most active research sub-fields in ...
