The Pathway Coexpression Network: Revealing pathway relationships.

A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer's Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/

Diagnosis of mediastinal and left adrenal abnormalities with endoscopic ultrasonography

Endoscopic ultrasound with fine-needle aspiration (EUS-FNA) is increasingly used for sampling of mediastinal or left adrenal abnormalities. We report two patients in whom EUS-FNA led to the diagnosis of malignancy. In one patient, left adrenal metastasis of a rectal. adenocarcinoma was diagnosed, while in the other patient EUS-FNA established a plasmocytoma in the right hilum and mediastinum after several non-diagnostic interventions. (c) 2004 Elsevier Ltd. All rights reserved

Endoscopic ultrasound-guided fine-needle aspiration in patients with mediastinal abnormalities and previous extrathoracic malignancy

Enlarged mediastinal lymph nodes inpatients with previous extrathoracic malignancy require pathological verification. However, surgical procedures lead to morbidity and (rarely) mortality. Endoscopic ultrasound with fine-needle aspiration (EUS-FNA) is a minimally invasive, outpatient procedure. We prospectively assessed its usefulness in patients with mediastinal abnormalities and previous extrathoracic malignancy. All patients underwent EUS-FNA prior to planned surgical procedures. Specimens were categorised as positive, negative, or inconclusive. Surgical procedures were cancelled after positive EUS-FNA. Twenty patients underwent EUS-FNA, being positive in eleven and providing an alternative diagnosis in one patient (a total of 60%). In 8 patients, EUS-FNA was negative or inconclusive, while surgery was positive in five and negative in three. Sensitivity and specificity of EUS-FNA were 69 and 100%, respectively. EUS-FNA is useful in the assessment of mediastinal abnormalities in patients with previous extrathoracic malignancy. Surgical diagnostic procedures were precluded in 60% of such patients. (C) 2003 Elsevier Ltd. All rights reserved.</p

On the design of optimally informative experiments for dynamic crystallization process modeling

In this paper, we present the challenging application of now well-established general and systematic procedures for model development, statistical discrimination, and validation to a published large-scale dynamic crystallization process model. Because of the model's size, this represents, to our knowledge, the first application of such statistical methods to such a large-scale dynamic model. For completeness, a brief review of both the model development procedures and the dynamic model are included in the paper. In reviewing the model development procedures, we cover such methods as parametric identifiability testing (to determine whether the parameters, as they appear in the model, can in fact be identified), as well as optimal design of dynamic experiments for both model discrimination among three crystallization models (differing in their kinetics only) and parameter precision improvement within the single "best" dynamic model. Because of the relatively large scale of the model, an optimization-based approach is used for testing of model parameter identifiability that involves semi-infinite programming (SIP) to ensure that the entire control (or input) space has been explored. The problem of designing dynamic experiments is cast as an optimal control problem that enables the calculation of optimal sampling points, experiment durations, fixed and variable external control profiles, and initial conditions of a dynamic experiment subject to general constraints on inputs and outputs. Within this framework, methods are presented to provide experiment design robustness, accounting for parametric uncertainty and subsequently model prediction uncertainty. The paper details the progression of the three crystallization models through the model development procedures and shows the Gahn and Mersmann model (Chem. Eng. Sci. 1999, 54, 1273) to be superior to its competitors