title: Complementary feature selection from alternative splicing events and gene expression for phenotype prediction.
abstract: A central task of bioinformatics is to develop sensitive and specific means of providing medical prognoses from biomarker patterns. Common methods to predict phenotypes in RNA-Seq datasets utilize machine learning algorithms trained via gene expression. Isoforms, however, generated from alternative splicing, may provide a novel and complementary set of transcripts for phenotype prediction. In contrast to gene expression, the number of isoforms increases significantly due to numerous alternative splicing patterns, resulting in a prioritization problem for many machine learning algorithms. This study identifies the empirically optimal methods of transcript quantification, feature engineering and filtering steps using phenotype prediction accuracy as a metric. At the same time, the complementary nature of gene and isoform data is analyzed and the feasibility of identifying isoforms as biomarker candidates is examined.
  
  
  
  Isoform features are complementary to gene features, providing non-redundant information and enhanced predictive power when prioritized and filtered. A univariate filtering algorithm, which selects up to the N highest ranking features for phenotype prediction is described and evaluated in this study. An empirical comparison of pipelines for isoform quantification is reported by performing cross-validation prediction tests with datasets from human non-small cell lung cancer (NSCLC) patients, human patients with chronic obstructive pulmonary disease (COPD) and amyotrophic lateral sclerosis (ALS) transgenic mice, each including samples of diseased and non-diseased phenotypes.
  
  
  
  https://github.com/clabuzze/Phenotype-Prediction-Pipeline.git
  
  
  
  clabuzze@iastate.edu, antoniom@bc.edu, watsondk@musc.edu, andersonpe2@cofc.edu.
authors: Department of Mathematics, Iowa State University, Ames, IA 50011, USA.
authors: Charles J
authors: CJ
authors: Labuzzetta
authors: Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.
authors: Margaret L
authors: ML
authors: Antonio
authors: Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, NC 29425, USA.
authors: Patricia M
authors: PM
authors: Watson
authors: Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, NC 29425, USA.
authors: Robert C
authors: RC
authors: Wilson
authors: Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA.
authors: Lauren A
authors: LA
authors: Laboissonniere
authors: Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA.
authors: Jeffrey M
authors: JM
authors: Trimarchi
authors: Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
authors: Baris
authors: B
authors: Genc
authors: Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
authors: P Hande
authors: PH
authors: Ozdinler
authors: Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, NC 29425, USA.
authors: Dennis K
authors: DK
authors: Watson
authors: Department of Computer Science, College of Charleston, Charleston, SC 29424, USA.
authors: Paul E
authors: PE
authors: Anderson
mesh_terms: D000465:Algorithms; D017398:Alternative Splicing; D000690:Amyotrophic Lateral Sclerosis; D000818:Animals; D002289:Carcinoma, Non-Small-Cell Lung; D006801:Humans; D008175:Lung Neoplasms; D000069550:Machine Learning; D008822:Mice, Transgenic; D010641:Phenotype; D029424:Pulmonary Disease, Chronic Obstructive