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• W2998782948 abstract "•Mouse-transcriptome humanized by machine learning predicts human clinical outcomes•This system is referred to as humanized Mouse DataBase individualized, hMDB-i•hMDB-i predicts drug adverse events and therapeutic indications•hMDB-i is a bias-free and modality-independent system for virtual drug development Approximately 90% of pre-clinically validated drugs fail in clinical trials owing to unanticipated clinical outcomes, costing over several hundred million US dollars per drug. Despite such critical importance, translating pre-clinical data to clinical outcomes remain a major challenge. Herein, we designed a modality-independent and unbiased approach to predict clinical outcomes of drugs. The approach exploits their multi-organ transcriptome patterns induced in mice and a unique mouse-transcriptome database “humanized” by machine learning algorithms and human clinical outcome datasets. The cross-validation with small-molecule, antibody, and peptide drugs shows effective and efficient identification of the previously known outcomes of 5,519 adverse events and 11,312 therapeutic indications. In addition, the approach is adaptable to deducing potential molecular mechanisms underlying these outcomes. Furthermore, the approach identifies previously unsuspected repositioning targets. These results, together with the fact that it requires no prior structural or mechanistic information of drugs, illustrate its versatile applications to drug development process. Approximately 90% of pre-clinically validated drugs fail in clinical trials owing to unanticipated clinical outcomes, costing over several hundred million US dollars per drug. Despite such critical importance, translating pre-clinical data to clinical outcomes remain a major challenge. Herein, we designed a modality-independent and unbiased approach to predict clinical outcomes of drugs. The approach exploits their multi-organ transcriptome patterns induced in mice and a unique mouse-transcriptome database “humanized” by machine learning algorithms and human clinical outcome datasets. The cross-validation with small-molecule, antibody, and peptide drugs shows effective and efficient identification of the previously known outcomes of 5,519 adverse events and 11,312 therapeutic indications. In addition, the approach is adaptable to deducing potential molecular mechanisms underlying these outcomes. Furthermore, the approach identifies previously unsuspected repositioning targets. These results, together with the fact that it requires no prior structural or mechanistic information of drugs, illustrate its versatile applications to drug development process. Unexpected adverse events (AEs) and/or the lack of the expected efficacy in human subjects fails drug development. The average success rate of drug candidates through clinical trials is 13.8% (Wong et al., 2019Wong C.H. Siah K.W. Lo A.W. Estimation of clinical trial success rates and related parameters.Biostatistics. 2019; 20: 273-286Crossref PubMed Scopus (580) Google Scholar). This low success rate costs US$161M–1.8B per drug candidate on drug developers (e.g., pharmaceutical and biotech companies) (Morgan et al., 2011Morgan S. Grootendorst P. Lexchin J. Cunningham C. Greyson D. The cost of drug development: a systematic review.Health Policy. 2011; 100: 4-17Crossref PubMed Scopus (330) Google Scholar), leading to drug price hike and rising medical cost. Hence, effective prediction of clinical outcomes from the pre-clinical studies improves the success rate of drug development and reduces the drug price and medical cost. A major impediment in drug development is the unpredictability of human outcomes on the basis of the pre-clinical results using cells, organs, and/or animal models. To overcome this problem, numerous technological solutions, both experimental and computational approaches, are currently undertaken. The uses of human cells and organs in vitro and in vivo models are among such approaches. Human cells such as induced human pluripotent cells (human iPSCs) (Elitt et al., 2018Elitt M.S. Barbar L. Tesar P.J. Drug screening for human genetic diseases using iPSC models.Hum. Mol. Genet. 2018; 27: R89-R98Crossref PubMed Scopus (76) Google Scholar, Ko and Gelb, 2014Ko H.C. Gelb B.D. Concise review: drug discovery in the age of the induced pluripotent stem cell.Stem Cells Transl. Med. 2014; 3: 500-509Crossref PubMed Scopus (58) Google Scholar, Meseguer-Ripolles et al., 2018Meseguer-Ripolles J. Khetani S.R. Blanco J.G. Iredale M. Hay D.C. Correction to: pluripotent stem cell-derived human tissue: platforms to evaluate drug metabolism and safety.AAPS J. 2018; 20: 30Crossref PubMed Scopus (1) Google Scholar) and an organ(s)-on-a-chip consisting of human cells (Oleaga et al., 2016Oleaga C. Bernabini C. Smith A.S. Srinivasan B. Jackson M. McLamb W. Platt V. Bridges R. Cai Y. Santhanam N. et al.Multi-Organ toxicity demonstration in a functional human in vitro system composed of four organs.Sci. Rep. 2016; 6: 20030Crossref PubMed Scopus (286) Google Scholar, Rezaei Kolahchi et al., 2016Rezaei Kolahchi A. Khadem Mohtaram N. Pezeshgi Modarres H. Mohammadi M.H. Geraili A. Jafari P. Akbari M. Sanati-Nezhad A. Microfluidic-based multi-organ platforms for drug discovery.Micromachines (Basel). 2016; 7: 162Crossref PubMed Scopus (33) Google Scholar) are currently utilized as drug screening and in vitro toxicity assays. As in vivo models, partially humanized mouse models, such as those where the liver is nearly 100% composed of human cells, is exploited (Tateno et al., 2004Tateno C. Yoshizane Y. Saito N. Kataoka M. Utoh R. Yamasaki C. Tachibana A. Soeno Y. Asahina K. Hino H. et al.Near completely humanized liver in mice shows human-type metabolic responses to drugs.Am. J. Pathol. 2004; 165: 901-912Abstract Full Text Full Text PDF PubMed Scopus (453) Google Scholar). In addition to such experimental approaches, computational tools are also invented and used. In particular, applications of machine learning algorithms for predicting clinical outcomes are in fashion (Shah et al., 2019Shah P. Kendall F. Khozin S. Goosen R. Hu J. Laramie J. Ringel M. Schork N. Artificial intelligence and machine learning in clinical development: a translational perspective.NPJ Digit. Med. 2019; 2: 69Crossref PubMed Scopus (180) Google Scholar, Vamathevan et al., 2019Vamathevan J. Clark D. Czodrowski P. Dunham I. Ferran E. Lee G. Li B. Madabhushi A. Shah P. Spitzer M. et al.Applications of machine learning in drug discovery and development.Nat. Rev. Drug Discov. 2019; 18: 463-477Crossref PubMed Scopus (716) Google Scholar). They exploit the big-data sets representing structural and functional features of drugs and their target information. Although both of such experimental and computational approaches have shown some success and promise, there are certain limitations with these existing approaches. The existing machine learning approaches require prior knowledge about the characteristics (such as structure) of drugs and their mechanisms of actions (such as target molecules). In addition, many of the computational approaches are specialized for the drugs of specific modality such as small-molecule compounds (i.e., mixed structures and mechanisms). Hence, they have difficulty dealing with the mixture of compounds. The existing experimental approaches are often “biased”, as they can design assay system according to what the testers want to examine. For example, the testers need to know which organ(s) or phenotype(s) to examine for the drug effect(s) prior to designing the experiment(s). Furthermore, these presently available experimental and computational approaches fail to recapitulate the organismal level body-wide drug effects in human. Hence, an approach that recapitulates organismal biology but does not require any prior knowledge about the drug structure or mechanisms (i.e., modality-independent and unbiased) is expected to advance and facilitate the drug development process. At the whole-body level, drugs could act on not only one or a few specific organ(s), but also multiple organs showing complex effects (Berger and Iyengar, 2011Berger S.I. Iyengar R. Role of systems pharmacology in understanding drug adverse events.Wiley Interdiscip. Rev. Syst. Biol. Med. 2011; 3: 129-135Crossref PubMed Scopus (86) Google Scholar). Drugs could also act on one organ that influences the functions of other organs via inter-organ cross talks (Droujinine and Perrimon, 2013Droujinine I.A. Perrimon N. Defining the interorgan communication network: systemic coordination of organismal cellular processes under homeostasis and localized stress.Front. Cell. Infect. Microbiol. 2013; 3: 82Crossref PubMed Scopus (30) Google Scholar, Droujinine and Perrimon, 2016Droujinine I.A. Perrimon N. Interorgan communication pathways in physiology: focus on Drosophila.Annu. Rev. Genet. 2016; 50: 539-570Crossref PubMed Scopus (111) Google Scholar). Hence, measuring the drug effects on large number of organs may provide useful features that could be exploited in predicting the drug effects at the whole-body level. However, this assay is only possible with animal models but not with human subjects. We previously conducted multi-organ multi-model transcriptome studies and identified model-specific and organ-specific transcriptome patterns in mice (Kozawa et al., 2018Kozawa S. Ueda R. Urayama K. Sagawa F. Endo S. Shiizaki K. Kurosu H. Maria de Almeida G. Hasan S.M. Nakazato K. et al.The body-wide transcriptome landscape of disease models.iScience. 2018; 2: 238-268Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar). Furthermore, weighted correlation network analysis (WGCNA) of the gene expression across multiple organs identified a number of putative organ-to-organ cross talks, some of which we genetically validated (Kozawa et al., 2018Kozawa S. Ueda R. Urayama K. Sagawa F. Endo S. Shiizaki K. Kurosu H. Maria de Almeida G. Hasan S.M. Nakazato K. et al.The body-wide transcriptome landscape of disease models.iScience. 2018; 2: 238-268Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar). On the basis of this previous study, we considered that such multi-organ transcriptome patterns could be exploited as the unique features of drugs at the whole-body level to predict its clinical outcomes. Furthermore, if successful, the approach provides a modality-independent and unbiased system requiring no prior knowledge of the structures/mechanisms of drugs, as the only requirement is that the drugs can be administered to mouse. In the current study, we develop and show the performance of such system. We design a hybrid approach of experimental and machine-learning methods to utilize the multi-organ transcriptome patterns induced by drugs in mice to predict their clinical outcomes in human. We evaluate its prediction performance for both AEs and therapeutic indications by cross-validation. In addition, we also test whether the approach could be adapted to deducing molecular and cellular mechanisms underlying such predictions. Furthermore, we illustrate a possibility of applying this approach to identifying drug repositioning targets. The concept of our approach is described in Figure 1. First, we generate transcriptome data across 24 organs from the mice to which we administer each drug with known clinical outcomes. Next, for each human clinical criterion (e.g., AE, therapeutic indication), we train a machine learning model with the 24-organ transcriptome data induced by the drug in mice and its known outcomes in human. The outcome data are classified according to the demographic profiles (e.g., sex, age) of the individuals. Consequently, the drug-induced 24-organ mouse transcriptome patterns are associated with the individualized human clinical outcomes in the machine learning model/database, hence referred to as “humanized Mouse-DataBase, individualized (hMDB-i).” To predict clinical outcomes of a new drug candidate X, we generate the 24-organ transcriptome data from the mice to which X is administered. Such data are used as the input data into hMDB-i to predict X's putative individualized clinical outcomes (i.e., the output). We generated the 24-organ transcriptome datasets with 15 drugs of various modalities (i.e., small molecules, antibody, and peptide) with diverse known therapeutic indications (see Transparent Methods, Figure 1 and Table S1). The incidence of AEs reported for each drug in each sex and each age group were compiled from US Food and Drug (FDA) Adverse Event Reporting System (FAERS). For each AE (a total of 5,519 AEs is reported for one or more of the 15 drugs), we train a support vector machine (SVM) algorithm with the drug-induced 24-organ transcriptome patterns to predict the individualized outcomes. We performed cross-validation to evaluate the effectiveness of this approach to predict the outcomes of the AEs (see Transparent Methods, Figure 2A). We first train an SVM algorithm with the 14 drug-induced 24-organ mouse transcriptome data. We then input the 24-organ mouse transcriptome data of the omitted drug as “an unseen drug data” (see Transparent Methods, Figure 2A). We performed such evaluations for all 15 drugs by omitting one drug at a time from the training data. We used an SVM classifier to predict whether each AE would be observed by each drug for each sex and age-group (see Transparent Methods). The scores of accuracy, precision, recall, and F-measure of the prediction result of the AEs reported for each sex and age group for each drug are summarized in Figure 2B and Table S2. The result shows that the accuracy scores are over 0.7 for the 200 of 264 sex/age-groups across the 15 drugs and over 0.9 for the 52 sex/age-groups. This indicates that more than 70% of the AEs predicted to either appear or not appear for the drug are indeed reported or not reported, respectively, in these sex/age-groups. The precision scores are over 0.5 for the 156 of the 264 sex/age-groups, indicating that more than 50% of the AEs predicted to appear for the drug are indeed reported in these sex/age-groups. If the question is whether an AE predicted to appear for a drug is indeed reported in at least one or more of the sex/age group, 13 drugs (alendronate, acetaminophen, aripiprazole, cisplatin, clozapine, doxycycline, empagliflozin, lenalidomide, olanzapine, evolocumab [Repatha], risedronate, sofosbuvir, teriparatide) show the precision scores of over 0.5 (see “All” row in each drug in Figure 2B), indicating that more than 50% of the AEs predicted to appear for these drugs are indeed reported at some sex/age groups. Furthermore, for nine drugs (alendronate, acetaminophen, aripiprazole, cisplatin, clozapine, doxycycline, lenalidomide, olanzapine, teriparatide), the precision scores of 0.9–1.0 are found at least in one or more the sex/age groups. The recall score indicates how many of the reported AEs are indeed predicted for the drug in each sex/age group. The recall scores are relatively lower across all drugs and sex/age groups, indicating that the approach misses many of the reported AEs. However, for such drugs (asenapine and lurasidone), the recall scores of 0.7–1.0 are found for certain sex/age groups. Sample size differences among different sex/age groups and the number of the reports for each AE may significantly impact on the accuracy, precision, and recall scores. In fact, the number of the reported AEs for asenapine and lurasidone is significantly smaller than those of the other drugs and their precision is lower. In FAERS, many of the AEs (e.g., diarrhea, abdominal pain) are common among many drugs. The outcomes of rare but serious AEs (SAEs) are often more important to know in advance to clinical trials. Hence, we assessed the prediction performance on SAEs. The top of such SAE hierarchy is “death event” (Sonawane et al., 2018Sonawane K.B. Cheng N. Hansen R.A. Serious adverse drug events reported to the FDA: analysis of the FDA adverse event reporting system 2006-2014 database.J. Manag. Care Spec. Pharm. 2018; 24: 682-690PubMed Google Scholar). The result shows that the correct outcomes of death event are predicted for 148 of 204 sex/age groups across the 15 drugs (Figure 2C). The death events are reported in a total of 141 sex/age groups, and our prediction approach missed its incidence only in 12 groups (Figure 2C). To examine potential influences of reference-specific errors, biases, and/or cofounders that are often found in any real-world data such as FAERS (Harpaz et al., 2013Harpaz R. DuMouchel W. LePendu P. Bauer-Mehren A. Ryan P. Shah N.H. Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system.Clin. Pharmacol. Ther. 2013; 93: 539-546Crossref PubMed Scopus (185) Google Scholar, Tatonetti et al., 2012Tatonetti N.P. Ye P.P. Daneshjou R. Altman R.B. Data-driven prediction of drug effects and interactions.Sci. Transl. Med. 2012; 4: 125ra131Crossref Scopus (456) Google Scholar), we validated our prediction with another reference, Side Effect Resource (SIDER) (http://sideeffects.embl.de). Several differences between FAERS and SIDER must be noted. SIDER4.1 version does not include evolocumab (Repatha). The outcomes reported in SIDER4.1 are not classified according to sex/age groups. The SEs and AEs are not necessarily represented by the same terms. Considering these differences, we examined the outcome predictions for 14 drugs (i.e., 15 drugs minus evolocumab) without the sex/age group classifications. In addition, the SEs/AEs common to both reference databases were analyzed. The result, 0.677–0.827 accuracy, 0.023–0.523 precision, and 0.571–0.963 recall, is comparable with that with FAERS (Figure 2D, the full and raw data for the confusion matrix is available as Table S3), further supporting the effectiveness of our method. Next, we evaluated the necessity of multiple organ transcriptome. Figure 3 shows the SAEs that the 24-organ transcriptome correctly predicts the outcome for each drug (Figure 3 and Table S4). The predictions of these SAEs by individual-organ transcriptome data in the cross-validation scheme show variable results among different organs (Figure 3). Although this result indicates that some of the individual-organ datasets are sufficient to predict the correct outcomes, which individual-organ dataset(s) is(are) necessary varies among which SAEs to be predicted (Figure 3). Hence, it appears beneficial to collect all 24-organ transcriptome data. We tested another machine learning algorithm, random forest (RF), to predict the AE outcomes. We compared the prediction results for death event outcomes (Figure 4 and Table S5). The result shows that both SVM and RF provides the same predictions for all sex/age groups (Figure 4A). The calculations of accuracy, precision, recall, and F-measure scores by both algorithms show the similar results for all 15 drugs (Figure 4B). For all drugs except asenapine, empagliflozin, and lurasidone, all scores of accuracy/precision/recall are 0.5–1.0. Although the accuracy and the precision scores for asenapine, empagliflozin, and lurasidone are 0.2–0.4, the recall scores for these three drugs by both SVM and RF are 1.0 (asenapine, empagliflozin) and 0.667 (lurasidone), indicating that the occurrence of death event by these three drugs are efficiently predicted across all sex/age groups (i.e., not missing the possible occurrence of death event). The results indicate that both algorithms are equivalent in predicting AE outcomes. Although the performance with two different algorithms (SVM and RF) were equivalent (Figure 4), we obtained differential results with the 24-organ and the individual-organ approaches (Figures 2 and 3). In the case of the individual-organ approach, which individual-organ dataset(s) to be used was critical for predicting the correct outcomes (Figure 3). Hence, it may be beneficial to conduct predictions with both the 24-organ and all individual-organs to maximize the effectiveness of the hMDB-i approach. For this purpose, we designed a majority decision framework and evaluated its effectiveness for predicting death event outcomes with alendronate and lenalidomide in female/20s group where the SVM approach with the 24-organ transcriptome dataset alone failed to predict its occurrence (see Methods for the detailed description, Figure 4C). The result shows that this majority decision framework effectively predicts the correct outcomes (Figure 4C). Next, we tested the power of hMDB-i for the prediction of AE outcomes in a different framework. The relationship between the adverse effects and therapeutic indications has previously been exploited for clinical outcome predictions (Zhang et al., 2013Zhang P. Wang F. Hu J. Sorrentino R. Exploring the relationship between drug side-effects and therapeutic indications.AMIA Annu. Symp. Proc. 2013; 2013: 1568-1577PubMed Google Scholar). Hence, we examined whether such data could be utilized to enhance the prediction capability of hMDB-i. For this purpose, we devised a link-prediction (LP) framework as shown in Figure 5A (see Transparent Methods for the detailed description). Three training datasets are used: (1) the 24-organ transcriptome data of each of the 15 drugs, (2) AEs reported for each of all drugs in FAERS, and (3) all AEs reported in FAERS for each of all indications in FAERS (Figure 5A). In the LP framework, one-class SVM algorithm is used to train the models and the presence/absence of a link of an untrained drug (Drug Candidate X in Figure 5A), based on its 24-organ transcriptome pattern, to each AE is determined (see Transparent Methods for the detailed description). The LP prediction of all AEs for three drugs, alendronate, clozapine, and evolocumab (Repatha), was conducted, and the result is summarized as a confusion matrix (Figure 5B, Table S6). Although the accuracy and precision scores are slightly better with hMDB-i alone (SVM), the LP framework (LP) improves hMDB-i in the recall scores (Figure 5B). This result indicates two properties of these two approaches: (1) The AE outcomes predicted by hMDB-i alone is more likely to be observed than those with the LP framework; (2) The LP framework is superior to hMDB-i alone in not missing the occurrence of potential AEs. The examples of such superior property of the LP framework are shown in Figure 5C listing some of the reported SAEs that are missed by the hMDB-i alone but captured by the LP framework. The full list of such can be found in Table S6. Insights into the biological mechanisms underlying AEs provide opportunities for designing the strategies to reduce the incidence of AEs during the drug development processes. For this purpose, RF algorithm is useful as it calculates feature importance of organ-gene datasets for the correct outcome predictions. Tables 1 and 2 show organs and cellular/molecular pathways that were found important for predicting the correct outcomes of the death event for males at 50s for aripiprazole. Top 5 and Top 8 organ-pathway combinations identified by REACTOME (Table 1) and KEGG (Table 2), respectively, are shown. The results for this and other drugs and age/sex groups are summarized in Tables S7 and S8. The result indicates a possibility that the hMDB-i is also useful for deducing mechanisms underlying the predicted AE outcomes.Table 1Enriched Pathways in REACTOMEOrganPathwayEntities FDRStomachDetoxification of reactive oxygen species0.00038AdrenalGPPARA activates gene expression0.00062AdrenalGRegulation of lipid metabolism by Peroxisome proliferator-activated receptor alpha (PPARalpha)0.00062AdrenalGLipophagy0.00282WATChaperone mediated autophagy0.00282The top 5-organ pathways for aripiprazole/male/50s are shown. Open table in a new tab Table 2Enriched Pathways in KEGGOrganPathwayP, AdjustedSpleenGlycosphingolipid biosynthesis—ganglio series0.00174EyeSalivary secretion0.00904AdrenalGPPAR signaling pathway0.01008StomachGlutathione metabolism0.01032StomachThyroid hormone synthesis0.01032StomachArachidonic acid metabolism0.01032ThyroidGProteasome0.01066ThyroidGEpstein-Barr virus infection0.02666The top 8-organ pathways for aripiprazole/male/50s are shown. Open table in a new tab The top 5-organ pathways for aripiprazole/male/50s are shown. The top 8-organ pathways for aripiprazole/male/50s are shown. Information regarding the quantitative differences of the AE occurrence among the sex/age group(s) would be beneficial for designing efficient clinical trials. It allows for selecting target subjects with less chance of observing an AE in the trial. Hence, we next evaluated the performance for predicting quantitative changes for the outcome incidences of AEs (see Transparent Methods for the detailed description, Figure 6, Table S9). For this purpose, we applied support vector regressor (SVR) and random forest regressor (RFR) algorithms to the hMDB-i framework. The number of death event reports changes over age to variable degrees for each drug (“Reported” in Figure 6). There also appears that the sex difference exists (F versus M in Figure 6). The comparison of the predictions by both algorithms (SVR and RFR) to the reported (Reported) incidence (death/all reported AEs) shows that the predictions by both algorithms efficiently capture the general trends of the quantitative changes of death event incidence over age for drugs/sex such as alendronate/female, doxycycline/male, aripiprazole/female (Figure 6, Table S9). On the other hand, the predictions for other drugs (e.g., clozapine: both sexes, empagliflozin:female, teriparatide:female) appear less efficient (Figure 6, Table S9). Although further improvements are necessary, the result suggests that the approach could be exploited to select specific sex/age groups as target subjects in clinical trials of at least some drugs. Identifying an appropriate therapeutic indication(s) (TIs) is also critical in drug development. Hence, we evaluated the utility of the multi-organ transcriptome datasets for predicting TIs. For this purpose, we adapted the LP framework (Figure 7A). Three training datasets are used: (1) the 24-organ transcriptome data of each drug of the 15 drugs, (2) all indications reported for each of the 15 drugs in FAERS, and (3) incidence of all AEs reported in FAERS for each of indications in FAERS (Figure 7A). In the LP framework, one-class SVM algorithm is used to train the models and the presence/absence of a link of an untrained drug (Drug Candidate X in Figure 7A), based on its 24-organ transcriptome data, to each TI is determined (see Transparent Methods for the detailed description, Figure 7A). We evaluated the performance of this framework by omitting the data of one drug from all training datasets at a time and repeating this for all 15 drugs to evaluate the performance of predicting potential TIs of each omitted drug (Figure 7B). The result is shown as a confusion matrix (Figure 7B), and the full list is available as Table S10. The accuracy scores are high (>0.78) for all 15 drugs (Figure 7B), indicating that more than 78% of the indications or non-indications predicted are indeed reported or not to be reported as the indications for each drug, respectively. The recall scores are also high (>0.8) for alendronate, aripiprazole, asenapine, clozapine, empagliflozin, lurasidone, olanzapine, evolocumab (Repatha), risedronate, sofosbuvir, and teriparatide (Figure 7B), indicating that over 80% of the reported indications are predicted for these 11 drugs by the method. The recall score of doxycycline is 0.527 (Figure 7B), indicating approximately 50% of the reported indications are predicted for this drug by the method. The recall scores are low for acetaminophen (0.141), cisplatin (0), and lenalidomide (0) (Figure 7B), indicating that this method fails to capture many reported indications for these drugs, as expressed by relatively smaller number of false-negatives (FNs) as compared with that of true-positives (TPs) for these drugs (Figure 7B). Only acetaminophen (APAP) shows high precision score (1.000), and all the others show low precision scores (<0.35). Both cisplatin and lenalidomide show 0 TP and 0 FN, thus the precision and the F-measure scores were unable to be calculated (Figure 7B). Such low precision scores for many drugs are mainly due to a large number of false-positives (FPs) as compared with that of TPs (Figure 7B). The result illustrates a possibly useful application of this LP framework to the prediction of drug TIs (see further in Discussion). We also evaluated the utility of the multi-organ transcriptome datasets for drug repositioning. In the scheme described in Figure 7B, the FP TIs (yellow highlight in Figure 7B) (the full list as Table S10) could include repositioning targets for the drugs (see further in Discussion). However, in drug repositioning, drug X does not exist. Hence, we used the same LP framework but did not omit the data of any drugs, i.e., the datasets of the drug of the prediction target is also included in the training datasets (Figure 7C). The result is shown as a confusion matrix (Figure 7C) and the full list is available as Table S11. The result shows the increased number of TPs and decreased number of FNs for all drugs, resulting in the improved recall scores (Figure 7C). In this scheme, both accuracy and recall scores for all drugs are 0.770–1.000 (Figure 7C), indicating the approach can capture over 77% of both reported and non-reported indications. The precision scores for all drugs remain relatively low due to the large number of FPs as compared with that of TPs (Figure 7C and Table S11). The indications found in FP (yellow highlight in Figure 7C) (the full list as Table S11) could include repositioning targets (see further in Discussion). As the number of FPs are relatively large, calculatin" @default.
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• W2998782948 title "Predicting Human Clinical Outcomes Using Mouse Multi-Organ Transcriptome" @default.
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• W2998782948 cites W2033247361 @default.
• W2998782948 cites W2049946556 @default.
• W2998782948 cites W2076192079 @default.
• W2998782948 cites W2104337806 @default.
• W2998782948 cites W2107516065 @default.
• W2998782948 cites W2119002393 @default.
• W2998782948 cites W2132886933 @default.
• W2998782948 cites W2323328911 @default.
• W2998782948 cites W2343742479 @default.
• W2998782948 cites W2382298227 @default.
• W2998782948 cites W2394730474 @default.
• W2998782948 cites W2509960233 @default.
• W2998782948 cites W2789816271 @default.
• W2998782948 cites W2792950357 @default.
• W2998782948 cites W2794737913 @default.
• W2998782948 cites W2804438136 @default.
• W2998782948 cites W2896002881 @default.
• W2998782948 cites W2937307539 @default.
• W2998782948 cites W2963899699 @default.
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