Affiliation: University of Pittsburgh
- Bayesian rule learning for biomedical data miningVanathi Gopalakrishnan
Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Avenue Suite M 183, Pittsburgh, PA 15260, USA
Bioinformatics 26:668-75. 2010..This article describes an approach that uses a Bayesian score to evaluate rule models...
- Knowledge-based variable selection for learning rules from proteomic dataJonathan L Lustgarten
Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M 183, Pittsburgh, PA, USA
BMC Bioinformatics 10:S16. 2009..In particular, we use the Empirical Proteomics Ontology Knowledge Base (EPO-KB) that contains previously identified and validated proteomic biomarkers to select m/zs in a proteomic dataset prior to analysis to increase performance...
- Application of an efficient Bayesian discretization method to biomedical dataJonathan L Lustgarten
Department of Biomedical Informatics and the Intelligent Systems Program, University of Pittsburgh, Suite M 183 Vale, Parkvale Building, 200 Meyran Avenue, Pittsburgh, PA 15260, USA
BMC Bioinformatics 12:309. 2011..We compared the performance of EBD to Fayyad and Irani's (FI) discretization method, which is commonly used for discretization...
- Discovery and verification of amyotrophic lateral sclerosis biomarkers by proteomicsHenrik Ryberg
Department of Pathology, University of Pittsburgh School of Medicine, BST S 420, 200 Lothrop Street, Pittsburgh, Pennsylvania 15261, USA
Muscle Nerve 42:104-11. 2010..CRP levels were increased in the CSF of ALS patients, and cystatin C level correlated with survival in patients with limb-onset disease. Our biomarker panel predicted ALS with an overall accuracy of 82%...
- Transfer learning of classification rules for biomarker discovery and verification from molecular profiling studiesPhilip Ganchev
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA
J Biomed Inform 44:S17-23. 2011..It also improves performance over using the union of the data sets...
- Measuring stability of feature selection in biomedical datasetsJonathan L Lustgarten
University of Pittsburgh Department of Biomedical Informatics, Pittsburgh, PA, USA
AMIA Annu Symp Proc 2009:406-10. 2009..We demonstrate the application of this measure on a biomedical dataset...
- Context-sensitive markov models for peptide scoring and identification from tandem mass spectrometryHimanshu Grover
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania 15206 3701, USA
OMICS 17:94-105. 2013..IO-HMMs offer a scalable and flexible framework with several modeling choices to learn complex patterns embedded in MS/MS data...
- Efficient Processing of Models for Large-scale Shotgun Proteomics DataHimanshu Grover
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206 3701 USA
Int Conf Collab Comput 2012:591-596. 2012..We also identify important challenges and opportunities that are relevant specifically to the task of peptide and protein identification, and more generally to similar multi-step problems that are inherently parallelizable...
- Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression dataHenry A Ogoe
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
BMC Bioinformatics 16:226. 2015..The goal of this study was to test our hypothesis that "an integrative model obtained via the TRL-FM approach outperforms traditional models based on single gene expression data sources"...
- On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissueArturo López Pineda
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA
BMC Cancer 16:184. 2016..To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue...
- Application of Bayesian logistic regression to mining biomedical dataViji R Avali
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA
AMIA Annu Symp Proc 2014:266-73. 2014..With informative prior probabilities, we conjecture that the performance of B-LR will improve. ..
- A multiplexed serum biomarker immunoassay panel discriminates clinical lung cancer patients from high-risk individuals found to be cancer-free by CT screeningWilliam L Bigbee
Mass Spectrometry Platform, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
J Thorac Oncol 7:698-708. 2012..Clinical decision making in the setting of computed tomography (CT) screening could benefit from accessible biomarkers that help predict the level of lung cancer risk in high-risk individuals with indeterminate pulmonary nodules...
- Protein fold recognition using segmentation conditional random fields (SCRFs)Yan Liu
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
J Comput Biol 13:394-406. 2006..Applying our prediction model to the Uniprot Database, we identify previously unknown potential beta-helices...
- Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosisSrikanth Ranganathan
Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
J Neurochem 95:1461-71. 2005..We validated the SELDI-TOF-MS results for transthyretin and cystatin C by immunoblot and immunohistochemistry using commercially available antibodies. These findings identify a panel of CSF protein biomarkers for ALS...
- Machine-learning techniques for macromolecular crystallization dataVanathi Gopalakrishnan
Intelligent Systems Laboratory, University of Pittsburgh, Pittsburgh, PA 15260, USA
Acta Crystallogr D Biol Crystallogr 60:1705-16. 2004..Computational experiments indicate that such a system can retain and present informative rules pertaining to protein crystallization that warrant further confirmation via experimental techniques...
- Comparison of probabilistic combination methods for protein secondary structure predictionYan Liu
Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA15213, USA
Bioinformatics 20:3099-107. 2004..e. combining the scores or assignments from single or multiple prediction systems under the constraint of a whole sequence, as a target for improvement in protein secondary structure prediction...