machine learning pharmacogenomics

Health Aff. : RNA splicing. Am. When all the cases were considered, the RFR algorithm achieved the best performance. Therefore, this analysis reflects the potential of ML techniques for predictions at extreme dose levels given their capabilities to assess patient characteristics under extreme dosing requirements. Disclaimer: The contents of this manuscript do not represent the views of the Veterans Affairs Caribbean Healthcare System, the Department of Veterans Affairs, the National Institutes of Health, or the United States Government. Inform. Similarly, after grouping patients by dose requirements (i.e.

Multiple ethno-specific variants occurring across warfarin-related pharmacogenes are generally overlooked and, consequently, the utility of existing prediction models is limited in patients with mixed ancestry. 56 (1), 27–34. : Big data: issues, challenges and techniques in business intelligence (2016). J. Med. 2, 1–24, Hu, Y. H., Wu, F., Lo, C. L., Tai, C. T. (2012). Not logged in Genet. Development of a pharmacogenetic-guided warfarin dosing algorithm for Puerto Rican patients. Method M. 2015, 560108. doi: 10.1155/2015/560108, Suykens, J. Accordingly, a relatively low number of patients were homozygous for the variant allele and just a few of them had unknown genotypes at these loci and, therefore, were excluded from subsequent analyses.

Frank, E., Hall, M. A., Witten, I. H. (2016). BMC Med. Pharmacogenetic variants previously found to be associated with warfarin dose requirements in Puerto Ricans (Ramos et al., 2012; Duconge et al., 2016; Claudio-Campos et al., 2017), individual ancestry proportions, as well as clinical and demographic data from all enrolled patients were considered in the corresponding analyses. Overall, the models generated for the subgroup with normal warfarin dose requirements performed better than those used to predict dosing among sensitives and resistant patients (Table 4).

360 (8), 753–764. This study compares seven ML methods to predict warfarin dosing in Caribbean Hispanics.

machine learning algorithms learn these rules from data, benefiting directly from the detail contained in large, complex, and heterogeneous datasets [4]. Previous studies have demonstrated a prediction accuracy of around 37–55% for the patients of warfarin stable dose.

AR-L provided the infrastructure, supervised the analysis of the data using bioinformatics tools, and participated in the writing of the manuscript. Nature, Lindblad-Toh, K., Garber, M., Zuk, O., Lin, M.F., Parker, B.J., et al.

ML algorithms were trained with the training set to obtain the models. MLR analysis routinely used to derive pharmacogenetic models is data driven and hence population dependent. Stat. A possible explanation for this observed superiority of ML models over the conventional algorithms is given by the fact that these applications of artificial intelligence (AI) provides systems the ability to automatically learn and improve predictability from experience (i.e., available data).

Med. A full description of this cohort as well as detailed information on the patient's recruitment process can be found elsewhere (Duconge et al., 2016). Childhood Obes. The model with best predictability was chosen for each of the ML-based algorithms tested, regardless of the number of added variants. Unlike previous reports, in this study we have included genetic markers for both sensitivity and resistance phenotypes, and admixture/ancestry estimates as critical covariates in model development (Klein et al., 2009; Ramos et al., 2012; Liu et al., 2015). The primary cohort, which corresponds to patients on warfarin, included 40 “normal,” 38 “sensitive,” and 17 “resistant” cases. (IEEE Xplore Digital Library), 392–397. Ramos, A. S., Seip, R. L., Rivera-Miranda, G., Felici-Giovanini, M. E., Garcia-Berdecia, R., Alejandro-Cowan, Y., et al. Comparison of the predictive abilities of pharmacogenetics-based warfarin dosing algorithms using seven mathematical models in Chinese patients.

However, RFR, SVR, and MARS algorithms had the best performance when the patients were grouped by dose range as “normal,” “sensitive,” “resistant,” respectively.

Med. Since ML techniques learn from existing data, the insufficient number of “resistant” cases in available dataset and, therefore, the limited amount of relevant data that can inform the model, may in part explain the poorer performance at this dose range.

Genet. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Front. There is promising research indicating that mathematical models other than linear regression may yield more predictive algorithms (Cosgun et al., 2011; Hu et al., 2012; Liu et al., 2015; Sharabiani et al., 2015; Duconge and Ruaño, 2018; Ma et al., 2018). Briefly, the Infinium™ Human OmniExpress-24 v1.2 BeadChip by Illumina, which provides a broad coverage of relevant markers for genome-wide association studies (GWAS), was used to perform the genetic testing of 95 warfarin patient from the A4070109 study cohort in iScan® system (Illumina, San Diego, CA).

“Resistant” demonstrated to have the highest variability in warfarin dose requirements among patients at any dosing range, suggesting that either current ML-based methods are not yet robust enough to optimally predict dosing in patients with a resistant phenotype or the lack of information from all predictors of resistance to warfarin in the model.

Probable inference, the law of succession and statistical inference. The study dataset was prepared using information from patients of the A4070109 study cohort (N = 95), but also included data from another 95 patients in the secondary cohort (A4070416 protocol), for a total of 190 patients. To improve patient quality of life, researchers have developed predictive pharmacogenetic dosing algorithms for warfarin in multiple ethnicities (Cosgun et al., 2011; Hu et al., 2012; Liu et al., 2015; Sharabiani et al., 2015; Li et al., 2015; Ma et al., 2018).

Med. J. diplotypes, conditions, co-medications, etc.).

Among these 190 patients, 96.8% were aged 50 years or older. A Novel Admixture-Based Pharmacogenetic Approach to Refine Warfarin Dosing in Caribbean Hispanics. Predicting warfarin dosage from clinical data: A supervised learning approach. Pritchard, J.K., Przeworski, M.: Linkage disequilibrium in humans: models and data. 10:1550. doi: 10.3389/fphar.2019.01550. The studies involving human participants were reviewed and approved by Human Research Subjects Protection Office (HRSPO) affiliated to the University of Puerto Rico Medical Sciences Campus, (IORG000223; Federal-wise Assurance #FWA00005561).

Ohno-Machado, L., et al. The paper also discusses issues related to application of machine learning in genomic data.


Both metrics (i.e., MAE and percentage within 20%) were compared among the ML models independently and after dividing patients into the above-mentioned three categories based on their warfarin dose requirements (i.e., “normal,” “sensitive,” and “resistant”).

In addition, machine learning (ML) algorithms in pharmacogenetic warfarin dosing have been reported (Liu et al., 2015). (2015) and our study were compared, the best result for all cases was obtained with the use of the RFR technique in our dataset of Caribbean Hispanics (i.e. doi: 10.1016/j.artmed.2012.04.001, Klein, T. E., Altman, R. B., Eriksson, N., Gage, B. F., Kimmel, S. E., Lee, M. T., et al.

Deep learning is one of the most successful types of machine learning techniques that has transformed many important subfields of AI over the last decade. CR and IS'A performed the statistics of this study and contributed to the manuscript preparation. Of note is that no significant differences in overall performances of various ML-based algorithms were reported by others when used as a prediction tool for stable warfarin dose estimations in a multi-ethnic cohort. (1927). This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Least Squares Support Vector Machine Classifiers.

However, the combination of relevant polymorphisms in both pharmacogenes accounted for approx. Additionally, the Infinium™ Multi-Ethnic Global AMR/AFR BeadChip was used in the A4070416 cohort of patients on clopidogrel.

tors with machine learning to achieve improved prediction perfor-mance, they were limited by lack of replication across multiple trials and multiple depression rating scales, in addition to lack of signifi-cance from a pharmacogenomics perspective. 19, 9. doi: 10.1101/gr.094052.109, Auton, A., Abecasis, G., Altshuler, D., the 1000 Genomes Project Consortium. Accessed November 14, 2019.

J. William, C., Knowler, M.D., et al. MAE = 4.73 mg/week and 80.56% cases within ±20% of ideal doses). 8–11 In the present study, we used a machine-learning workflow

Adv. Pharmacogenomics 19 (11), 875–881.

Impact Factor 4.225 | CiteScore 5.0More on impact ›, Pharmacogenetics Research and Clinical Applications: An International Landscape of the Accomplishments, Challenges, and Opportunities Ledford, H.: End of cancer atlas prompts rethink: Geneticists debate whether focus should shift from sequencing genomes to analysing function.

All these models were based on multivariate linear regression analyses. Nature, Marx, V.: The big challenges of big data.

Hollands, G.J., et al. Grossi, E., Podda, G. M., Pugliano, M., Gabba, S., Verri, A., Carpani, G., et al. doi: 10.1038/nature15393, PubMed Abstract | CrossRef Full Text | Google Scholar. Table 2 Frequency distributions of relevant genotypes in the Caribbean Hispanic patients included in this study. Med. : A method and server for predicting damaging missense mutations. Instance-based learning algorithms. Diabetes, Visscher, P.M., et al. : Scalable and accurate deep learning for electronic health records. Some of these algorithms have been compared in racially diverse groups, however Caribbean Hispanic populations have not been included. Additional data from participants in a multicenter case–control study of Puerto Rican Hispanic patients receiving antiplatelet therapy with clopidogrel, who were recruited between January 2018 and March 2019, were also included in this secondary analysis ( identifier NCT03419325). MAEs and percentage of predictions within ±20% revealed that models generated by using RFR, MARS, and SVR ML algorithms showed significantly better predictions of warfarin dosing in our cohort of participants than other algorithms. However, better predictors do not really translate into a real clinical utility to this “normal” subgroup as patients in this class are least likely to benefit from pharmacogenetics (Klein et al., 2009). This service is more advanced with JavaScript available, Evolution in Computational Intelligence : Genomics and electronic health record systems. J. Hum. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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