title: HIPred: an integrative approach to predicting haploinsufficient genes.
abstract: BACKGROUND
  A major cause of autosomal dominant disease is haploinsufficiency, whereby a single copy of a gene is not sufficient to maintain the normal function of the gene. A large proportion of existing methods for predicting haploinsufficiency incorporate biological networks, e.g. protein-protein interaction networks that have recently been shown to introduce study bias. As a result, these methods tend to perform best on well-studied genes, but underperform on less studied genes. The advent of large genome sequencing consortia, such as the 1000 genomes project, NHLBI Exome Sequencing Project and the Exome Aggregation Consortium creates an urgent need for unbiased haploinsufficiency prediction methods.
  
  
  RESULTS
  Here, we describe a machine learning approach, called HIPred, that integrates genomic and evolutionary information from ENSEMBL, with functional annotations from the Encyclopaedia of DNA Elements consortium and the NIH Roadmap Epigenomics Project to predict haploinsufficiency, without the study bias described earlier. We benchmark HIPred using several datasets and show that our unbiased method performs as well as, and in most cases, outperforms existing biased algorithms.
  
  
  METHODS
  HIPred scores for all gene identifiers are available at: https://github.com/HAShihab/HIPred .
  
  
  BACKGROUND
  h.shihab@bristol.ac.uk or tom.gaunt@bristol.ac.uk.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.
authors.forename: Hashem A
authors.initials: HA
authors.lastname: Shihab
authors.affiliation: Intelligent Systems Laboratory, University of Bristol, Bristol, UK.
authors.forename: Mark F
authors.initials: MF
authors.lastname: Rogers
authors.affiliation: Intelligent Systems Laboratory, University of Bristol, Bristol, UK.
authors.forename: Colin
authors.initials: C
authors.lastname: Campbell
authors.affiliation: MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.
authors.forename: Tom R
authors.initials: TR
authors.lastname: Gaunt
mesh_terms: D002843:Chromatin / Q000378:metabolism; D044127:Epigenesis, Genetic; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D057895:Haploinsufficiency; D006657:Histones / Q000378:metabolism; D006801:Humans; D000069550:Machine Learning; D060066:Protein Interaction Maps / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods; D017423:Sequence Analysis, RNA / Q000379:methods
chemical_list: D002843:Chromatin; D006657:Histones