Individual and Collective Prognostic Prediction

The prediction of survival time or recurrence time is an important learning problem in medical domains. The Recurrence Surface Approximation (RSA) method is a natural, effective method for predicting recurrence times using censored input data. This paper introduces the Survival Curve RSA (SC-RSA), an extension to the RSA approach which produces accurate predicted rates of recurrence, while maintaining accuracy on individual predicted recurrence times. The method is applied to the problem of breast cancer recurrence using two different dataset

Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets

Hasenfuss A, Hammer B, Rossi F. Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets. In: Prevost L, Marinai S, Schwenker F, eds. Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064. Berlin: Springer; 2008: 1-12

Survival Prediction and Gene Identification with Penalized Global AUC Maximization

Identifying genes (biomarkers) and predicting the clinical outcomes with censored survival times are important for cancer prognosis and pathogenesis. In this article, we propose a novel method with L1 penalized global AUC summary maximization (L1GAUCS). The L1GAUCS method is developed for simultaneous gene (feature) selection and survival prediction. L1 penalty shrinks coefficients and produces some coefficients that are exactly zero, and therefore selects a small subset of genes (features). It is a well-known fact that many genes are highly correlated in gene expression data and the highly correlated genes may function together. We, therefore, define a correlation measure to identify those genes such that their expression level may be low but they are highly correlated with the downstream highly expressed genes selected with L1GAUCS. Partial pathways associated with the correlated genes are identified with DAVID (http://david.abcc.ncifcrf.gov/). Experimental results with chemotherapy and gene expression data demonstrate that the proposed procedures can be used for identifying important genes and pathways that are related to time to death due to cancer and for building a parsimonious model for predicting the survival of future patients. Software is available upon request from the first author

Widened KRIMP : Better Performance through Diverse Parallelism

We demonstrate that the previously introduced Widening framework is applicable to state-of-the-art Machine Learning algorithms. Using Krimp, an itemset mining algorithm, we show that parallelizing the search finds better solutions in nearly the same time as the original, sequential/greedy algorithm. We also introduce Reverse Standard Candidate Order (RSCO) as a candidate ordering heuristic for Krimp