References
- Nebert DW , ZhangG , VesellES. From Human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions. Drug Metab. Rev.40(2), 187–224 (2008).
- Caudle K , KleinT , HoffmanJet al. Incorporation of pharmacogenomics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline Development Process. Curr. Drug Metab.15(2), 209–217 (2014).
- Xia X . Bioinformatics and drug discovery. Curr. Opin. Biotechnol.5(6), 648–653 (2017).
- Ji Y , SiY , McMillinGA , LyonE. Clinical pharmacogenomics testing in the era of next generation sequencing: challenges and opportunities for precision medicine. Expert Rev. Mol. Diagn.18(5), 441–421 (2018).
- Kurdyukov S , BullockM. DNA methylation analysis: choosing the right method. Biology (Basel)5(1), pii:E3(2016).
- Romanov V , DavidoffSN , MilesAR , GraingerDW , GaleBK , BrooksBD. A critical comparison of protein microarray fabrication technologies. R. Soc. Chem.139(6), 1303–1326 (2014).
- Petrov A , ShamsS. Microarray image processing and quality control. J. VLSI Signal Process.38, 211–226 (2004).
- Hemingway H , AsselbergsFW , DaneshJ et al. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur. Heart J.39(16), 1481–1495 (2017).
- Caudle KE , DunnenbergerHM , FreimuthRRet al. Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC). Genet. Med.19(2), 215–223 (2017).
- Goodwin S , McPhersonJD , McCombieWR. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet.17(6), 333–351 (2016).
- Caboche S , AudebertC , LemoineY , HotD. Comparison of mapping algorithms used in high-throughput sequencing: application to Ion Torrent data. BMC Genomics15(1), 1–16 (2014).
- Numanagic I , MalikicS , PrattVM , SkaarTC , FlockhartDA , SahinalpSC. Cypiripi: exact genotyping of CYP2D6 using high-throughput sequencing data. Bioinformatics31(12), i27–i34 (2015).
- Meynert AM , BicknellLS , HurlesME , JacksonAP , TaylorMS. Quantifying single nucleotide variant detection sensitivity in exome sequencing. BMC Bioinformatics14(1), (2013).
- Mahamdallie S , RuarkE , YostS et al. The Quality Sequencing Minimum (QSM): providing comprehensive, consistent, transparent next generation sequencing data quality assurance. Wellcome Open Res.3, 37 (2018).
- Jennings LJ , ArcilaME , CorlessCet al. Guidelines for validation of next-generation sequencing–based oncology panels: a joint consensus recommendation of the Association for Molecular Pathology and College of American Pathologists. J. Mol. Diagnostics19(3), 341–365 (2017).
- Clark MJ , ChenR , LamHYKet al. Performance comparison of exome DNA sequencing technologies. Nat. Biotechnol.29(10), 72–108 (2014).
- Zhou Y , Ingelman-SundbergM , LauschkeV. Worldwide distribution of cytochrome P450 alleles: a meta-analysis of population-scale sequencing projects. Clin. Pharmacol. Ther.102(4), (2017).
- Niinuma Y , SaitoT , TakahashiMet al. Functional characterization of 32 CYP2C9 allelic variants. Pharmacogenomics J.14(2), 107–114 (2013).
- Dong Z , JiangL , YangCet al. A robust approach for blind detection of balanced chromosomal rearrangements with whole-genome low-coverage sequencing. Hum. Mutat.35(5), 625–636 (2014).
- Suzuki T , TsurusakiY , NakashimaMet al. Precise detection of chromosomal translocation or inversion breakpoints by whole-genome sequencing. J. Hum. Genet.59(12), 649–654 (2014).
- Derouault P , ParfaitB , MoulinasRet al. “COV’COP” allows to detect CNVs responsible for inherited diseases among amplicons sequencing data. Bioinformatics33(10), 1586–1588 (2017).
- Irizarry RA , HobbsB , CollinF , Beazer-barclayYD , AntonellisKJ , SpeedTP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics May, 249–264 (2003).
- Zhang Y , LiuT , MeyerCAet al. Model-based analysis of ChIP-Seq (MACS). Genome Biol.9(9), R137 (2008).
- Whirl-Carrillo M , McDonoghE , HerbetJet al. Pharmacogenomics knowledge for personlized medicine. Clin. Pharmacol. Therpeutics92(4), 414–417 (2012).
- Rees MG , Seashore-ludlowB , CheahJHet al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat. Chem. Biol.12(2), 109–116 (2016).
- Seashore-ludlow B , ReesMG , CheahJHet al. Harnessing connectivity in a large-scale small-molecule sensitivity database. Cancer Discov.5(11), 1210–1223 (2015).
- Basu A , BodycombeNE , CheahJHet al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell154(5), 1151–1161 (2013).
- Kent WJ , SugnetCW , FureyTSet al. The human genome browser at UCSC. Genome Res.12(6), 996–1006 (2002).
- Zerbino DR , AchuthanP , AkanniWet al. Ensembl 2018. Nucleic Acids Res.46(D1), D754–D761 (2018).
- Ogata H , GotoS , SatoK , FujibuchiW , BonoH , KanehisaM. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res.27(1), 29–34 (1999).
- Kanehisa M , FurumichiM , TanabeM , SatoY , MorishimaK. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res.45(D1), D353–D361 (2017).
- Stark C , BreitkreutzB , RegulyT , BoucherL , BreitkreutzA , TyersM. BioGRID: a general repository for interaction datasets. Nucleic Acids Res.34, 535–539 (2006).
- Kim RS , GoossensN , HoshidaY. Use of big data in drug development for precision medicine. Expert Rev. Precis. Med. Drug Dev.1(3), 245–253 (2016).
- Tian Z , HwangT , KuangR. A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge. Bioinformatics25(21), 2831–2838 (2009).
- Dhruba SR , RahmanR , MatlockK , GhoshS , PalR. Application of transfer learning for cancer drug sensitivity prediction. BMC Bioinformatics19(Suppl.17), 497 (2018).
- Lek M , KarczewskiKJ , MinikelEVet al. Analysis of protein-coding genetic variation in 60,706 humans. Nature536(7616), 285–291 (2016).
- Braschi B , DennyP , GrayK et al. Genenames.org: the HGNC and VGNC resources in 2019. Nucleic Acids Res.47, 786–792 (2019).
- Database of Single Nucleotide Polymorphisms (dbSNP) . Bethesda (MD):National Center for Biotechnology Information, National Library of Medicine. www.ncbi.nlm.nih.gov/SNP/.
- Kalman LV , BlackJL , ClinicM , SwS , BellGC. Pharmacogenetic Allele Nomenclature: International Workgroup recommendations for test result reporting.99(2), 172–185 (2016).
- Browning SR , ThompsonEA. Detecting rare variant associations by identity-by-descent mapping in case–control studies. Genetics.190( April), 1521–1531 (2012).
- Xu H , GuanY. Detecting local haplotype sharing and haplotype association. Genetics197( July), 823–838 (2014).
- Giacomini KM , YeeSW , MushirodaT , WeinshilboumRM , RatainMJ , KuboM. Genome-wide association studies of drug response and toxicity: an opportunity for genome medicine. Nat Rev Drug Discov.16(1), 1–8 (2017).
- Hodge VJ , AustinJ. A survey of outlier detection methodologies. Artif. Intell. Rev.22, 85–126 (2004).
- Markou M , SinghS. Novelty detection: a review – part 1: statistical approaches. Signal Processing83, 2481–2497 (2003).
- Hanson C , CairnsJ , WangL , SinhaS. Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation. Genome Res.28(8), 1207–1216 (2018).
- Richardson S , TsengGC , SunW. Statistical methods in integrative genomics. Annu. Rev. Stat. Its Appl.3(1), 181–209 (2016).
- Ma’ayan A , RouillardAD , ClarkNR , WangZ , DuanQ , KouY. Lean big data integration in systems biology and systems pharmacology. Trends Pharmacol. Sci.35(9), 450–460 (2014).
- Chen J , ZhangS. Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics.32, 1724–1732 (2016).
- Giuliani A . The application of principal component analysis to drug discovery and biomedical data. Drug Discov. Today22(7), 1069–1076 (2017).
- Bielinski S , OlsonJ , PathakJet al. Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol. Mayo Clin Proc.89(1), 25–33 (2015).
- Wu Z , LiW , LiuG , TangY. Network-based methods for prediction of drug-target interactions. 9, 1–14 (2018).
- Iniesta R , HodgsonK , StahlD et al. Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables. Sci. Rep.8(1), 5530 (2018).
- Lutz W , SchwartzB , HofmannSG , FisherAJ , HusenK , RubelJA. Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof- of-concept study. Sci. Rep.8(1), 7819 (2018).
- Regan-Fendt KE , XuJ , DivincenzoM et al. Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes. NPJ Syst. Biol. Appl.5(6), doi:10.1038/s41540-019-0085-4 (2019).
- Huang H-H , DaiJ-G , LiangY. Clinical drug response prediction by using a Lq penalized network-constrained logistic regression method. Cell Physiol Biochem.51(5), 2073–2084 (2018).
- Li Q , ShiR , IdFL. Drug sensitivity prediction with high- dimensional mixture regression. PLoS One14(2), e0212108 (2019).
- Ali M , AittokallioT. Machine learning and feature selection for drug response prediction in precision oncology applications. Biophys. Rev.39, 31–39 (2019).
- Yang M , SimmJ , LamCCet al. Linking drug target and pathway activation for effective therapy using multi-task learning. Sci. Rep.8(1), 1–10 (2018).
- Nie L , LeeKY , VerdunN , DeClaro RA , SridharaR. Dose finding in late-phase drug development. Ther. Innov. Regul. Sci.51(6), 738–743 (2017).
- Gamazon ER , TrendowskiMR , WenYet al. Gene and MicroRNA perturbations of cellular response to pemetrexed implicate biological networks and enable imputation of response in lung adenocarcinoma. Sci. Rep.8(1), 1–13 (2018).