A Classification Model to Predict the Rate of Decline of Kidney Function.

The African American Study of Kidney Disease and Hypertension (AASK), a randomized double-blinded treatment trial, was motivated by the high rate of hypertension-related renal disease in the African-American population and the scarcity of effective therapies. This study describes a pattern-based classification approach to predict the rate of decline of kidney function using surface-enhanced laser desorption ionization/time of flight proteomic data from rapid and slow progressors classified by rate of change in glomerular filtration rate. An accurate classification model consisting of 7 out of 5,751 serum proteomic features is constructed by applying the logical analysis of data (LAD) methodology.

Comprehensive peroxidase-based hematologic profiling for the prediction of 1-year myocardial infarction and death.

Background: Recognition of biological patterns holds promise for improved identification of patients at risk for myocardial infarction (MI) and death. We hypothesized that identifying high- and low-risk patterns from a broad spectrum of hematologic phenotypic data related to leukocyte peroxidase-, erythrocyte- and platelet-related parameters may better predict future cardiovascular risk in stable cardiac patients than traditional risk factors alone.

Methods And Results: Stable patients (n=7369) undergoing elective cardiac evaluation at a tertiary care center were enrolled.

Logical Analysis of Data (LAD) model for the early diagnosis of acute ischemic stroke.

Background: Strokes are a leading cause of morbidity and the first cause of adult disability in the United States. Currently, no biomarkers are being used clinically to diagnose acute ischemic stroke. A diagnostic test using a blood sample from a patient would potentially be beneficial in treating the disease.

Breast cancer prognosis by combinatorial analysis of gene expression data.

Introduction: The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors.

Method: Data were analyzed using the combinatorics and optimization-based method of LAD, recently shown to provide highly accurate diagnostic and prognostic systems in cardiology, cancer proteomics, hematology, pulmonology, and other disciplines.

A computational approach to predicting cell growth on polymeric biomaterials.

A predictive model that can correlate the chemical composition of a biomaterial with the biological response of cells that are in contact with that biomaterial would represent a major advance and would facilitate the rational design of new biomaterials. As a first step toward this goal, we report here on the use of Logical Analysis of Data (LAD) to model the effect of selected polymer properties on the growth of two different cell types, rat lung fibroblasts (RLF, a transformed cell line), and normal foreskin fibroblasts (NFF, nontransformed human cells), on 112 surfaces obtained from a combinatorially designed library of polymers. LAD is a knowledge extraction methodology, based on using combinatorics, optimization, and Boolean logic.

Ovarian cancer detection by logical analysis of proteomic data.

A new type of efficient and accurate proteomic ovarian cancer diagnosis systems is proposed. The system is developed using the combinatorics and optimization-based methodology of logical analysis of data (LAD) to the Ovarian Dataset 8-7-02 (http://clinicalproteomics.steem.

Use of the logical analysis of data method for assessing long-term mortality risk after exercise electrocardiography.

Background: Logical Analysis of Data is a methodology of mathematical optimization on the basis of the systematic identification of patterns or "syndromes." In this study, we used Logical Analysis of Data for risk stratification and compared it to regression techniques.

Methods And Results: Using a cohort of 9454 patients referred for exercise testing, Logical Analysis of Data was applied to identify syndromes based on 20 variables.