Unlocking the Value of Transcriptomics in IBD R&D

In recent years, transcriptomics has yielded many promising discoveries towards defining disease pathogenesis for inflammatory bowel disease (IBD). With these insights, transcriptomic data has become one of the most valuable assets for life sciences organizations looking to develop and refine diagnostics and treatments for IBD. In this article, we’ll review how transcriptomics is revolutionizing precision medicine drug development and its recent influence on IBD treatment research.

About Transcriptomics

Transcriptomics enables researchers to gain insight into the functional elements of the genome and their expression patterns. By assessing gene expression in different tissues (comparing diseased versus normal), under different conditions, and at distinct time points, we can uncover critical insights into gene regulation and the intricacies of an organism’s biological functions (1). These insights can be used to better understand the disease’s underlying mechanisms, as well as to develop diagnostics and therapies (2).

With advances in technology and decreasing costs, transcriptomics has become more accessible and prevalent in modern disease research. The most highly sensitive and accurate tool for transcriptomic analysis, RNA Sequencing (RNA-Seq), has provided researchers with visibility into previously undetected changes. With RNA-Seq, researchers can quantify gene expression and allele-specific expression to identify novel genes and dive deep into the realm of non-coding RNA (1).

RNA-Seq has become widely available only in the last decade (as depicted in Fig 1). In that time, large datasets of transcriptomic analysis have been generated to obtain a valid gene expression profile. These datasets and the resulting discoveries have largely been focused on oncology research, given the ease of obtaining tumor biopsies (1).

A timeline since 1990, highlighting the adoption of RNA sequencing.

Figure 1. Evolution of Transcriptomics: A timeline since 1990, highlighting the adoption of RNA sequencing. Considering the relatively recent integration of RNA sequencing into research practices, we are just beginning to scratch the surface of the vast array of discoveries that transcriptomics has the potential to unveil. Reproduced from (1)


However, the last few years have witnessed an expansion. The transformative power of transcriptomic analysis is now being harnessed in the realm of IBD, opening up new avenues for early-stage R&D.

Transcriptomics for IBD R&D

IBD, including ulcerative colitis (UC) and Crohn’s disease (CD), is a chronic inflammatory disorder of the gastrointestinal tract with a global impact on over six million individuals (3, 4). 

IBD is a complex disease with substantial patient heterogeneity across disease location, behavior, and severity (5). Despite decades of research and drug development that has transformed the clinical management of IBD, the exact cause of IBD remains elusive, largely because of its complex nature involving genetic, environmental, and microbial factors (6). 

Transcriptomics, in particular, offers a promising way forward in unraveling these complexities. By analyzing the transcriptome of patients with IBD, researchers can identify specific patterns of gene expression that are associated with the disease. It paves the way to discern why IBD develops in some and not in others, why the disease progresses differently in patients, and why treatment responses are so varied. 

In the following sections, we explore the diverse applications for transcriptomics in R&D, showcasing pivotal studies, as summarized in Table 1.

Table 1: Harnessing Transcriptomics in IBD R&D

Table summarizing transcriptomics applications and key findings & references that are discussed in this article

Transcriptomics: Revolutionizing Biomarker Discovery for IBD

There is no universally adopted standard for IBD clinical diagnosis. Clinical symptoms associated with IBD—ranging from diarrhea and abdominal cramps to rectal bleeding—are non-specific. These symptoms could easily point to an array of inflammatory or infectious diseases with resemblances to IBD (24). 

Unfortunately, the current diagnostic methodology—a combination of symptom reports, laboratory tests, and radiological and endoscopic evaluations—often results in a long, cumbersome, and financially draining journey for patients. This not only compromises their comfort but also introduces potential lags in optimal disease management and treatment efficacy (25).

While some non-invasive methods have shown promise in identifying active disease (26), they lack specificity as the same biomarkers have been found to be present in other gastrointestinal diseases (27). As a result, there’s a pressing need for innovative, accurate diagnostic tests. To help develop these tests, researchers have begun to look for associations in the underlying biological mechanisms of patients with IBD.

Transcriptomics offers a promising path toward more efficient and accurate IBD diagnosis. A study leveraging microarray data pinpointed MAPK3, NDRG1, and HLA-DRA as pivotal players in IBD’s development and progression (7). Another study, using RNA-Seq on only 13 patient tissue samples, revealed transcriptomic differences between normal mucosa, non-inflamed CD mucosa, and inflamed CD mucosa. They found that the intestinal and serum chemokine (C-X-C) motif ligand 1 (CXCL1) gene was substantially increased with CD, marking its potential as a novel CD biomarker (8).

Transcriptomics is also a powerful tool for biomarker validation. For instance, a significant elevation in S100A8 and S100A9, crucial for the fecal biomarker calprotectin and S100A1210, was observed in IBD patient samples compared to healthy controls. These results aligned with previous literature, further solidifying their role as inflammation indicators in IBD (9).

Harnessing Transcriptomics for IBD Patient Stratification

One of the challenges with IBD diagnosis is clearly differentiating between the IBD subtypes (CD, UC, and IBD-Undefined). CD and UC are distinct conditions but frequently show overlapping symptoms and features that render diagnosis difficult and inaccurate. 

There is evidence that gene expression profiling can help enable this stratification of patients based on underlying pathways that drive their disease. Recent investigations employed cutting-edge machine learning algorithms alongside microarray gene expression profiling of colonic tissue biopsies to identify predictive transcriptional signatures associated with either CD or UC (10). This breakthrough finding underscores the transformational potential of transcriptomics in stratifying patients across clear diagnostic lines.

How Transcriptomics Can Help Predict Disease Course

IBD is known for its unpredictability of disease course and outcomes, which contributes significantly to disease burden and has a large impact on their psychological well-being and overall quality of life. When a patient is diagnosed with IBD, it’s difficult for clinicians to accurately explain what long-term prognosis the patient should expect (i.e., whether the disease will follow a slow, lethargic course or a more aggressive course) and when relapses or progression may occur (28).

Luckily, there has also been significant progress using transcriptomics to predict disease courses. One key development has been harnessing RNA-Seq data to develop a transcriptional risk score that identifies patients with CD who are likely to progress to complicated disease (11). This is a promising prediction model that needs further testing and validation with adult cohorts. 

Moreover, a blood-derived 17-gene classifier (commercially available test PredictSURE) leverages RNA extracted from blood samples to predict more aggressive manifestations of IBD with promising initial outcomes (12). 

Beyond these achievements, combining large integrated medical records, multiomics datasets, and a powerful machine learning framework have yielded exceptional results. One seminal study identified a unique expression profile in anti-TNF-naive and anti-TNF-exposed patients with CD that could predict postoperative disease recurrence. The authors uncovered 30 influential transcripts in anti-TNF-naive patients using machine learning models built on clinical data extracted from the EMR and transcriptomic profile of non-inflamed tissue (13).

Navigating New Frontiers: Transcriptomics in Target Identification for IBD

An expanding range of effective treatments has significantly improved disease outcomes and quality of life for IBD patients. Yet, there remain patients who have not benefited from existing treatment because they have not responded, their response has waned, or they’ve grappled with untenable adverse reactions (29). Transcriptomics is helping researchers identify novel therapeutic targets and optimize applications of existing treatments for these populations.

For example, CXCL8 has been widely recognized to be increased in UC patients compared to healthy individuals. This increase seems to be connected to how severe the inflammation is. Researchers have also found that CXCL8 is not necessarily higher in those with CD (14, 30). 

Another recent study investigated the microarray expression profiles of ncRNAs and mRNAs in inflamed tissues of 15 CD patients compared to the normal tissues. They reported that miR-21, miR-126, miR-146a, and miR-3194 were significant players in platelet activation signaling—a potential therapeutic pathway for CD (15).

Researchers have also shown that in comparison with healthy tissues, IBD intestinal tissues displayed increased expression levels of TNF (Figure 2), IFNG, IL12B, ITGA4, and ITGB7—all encoding for proteins known to be drivers of chronic inflammation, thereby making them enticing therapeutic targets (9).

Figure 2: Differential Expression Levels of TNF in IBD Patients (box plots)

Figure 2: Differential Expression Levels of TNF in IBD Patients: Differential tumor necrosis factor (TNF) normalized expression among UC and CD, as well as healthy ileum, colon, and rectum. Reproduced from (9).

Detailed transcriptomic analyses in two large cohorts of patients with CD and UC found that the most overexpressed genes—CD177, OLFM4, and GPR15—may represent important molecules in the pathogenesis of IBD and are potentially druggable targets (16).

In perhaps the most innovative study on the topic, researchers applied machine learning to IBD-tissue-derived transcriptomic data to create an AI-guided drug discovery approach. They identified a target (PRKAB1) that protects the epithelial barrier, a pivotal factor in disease relapse, and validated a first-in-class agent (PF-06409677) to activate the target (17).

How Transcriptomics Enhances the Path to Predicting Treatment Response

The unpredictability of IBD treatments has posed significant challenges. Despite initial success with certain therapeutics, patients can develop tolerances, leading them on a distressing journey of trial and error. This isn’t merely about drug inefficacy; non-responders risk enduring worsening tissue damage (29) and reduced potential for future treatments. It’s widely accepted that the initial biologic chosen carries the highest efficacy, and even the sequence of administered drugs can influence outcomes (31).

Due to this dynamic, clinicians are increasingly relying on biology rather than clinical symptoms to tailor aggressive treatments for those predicted to have severe disease progression while avoiding potent therapies for those predicted to have a benign disease course at diagnosis (32) (Figure 3). Biomarkers could be used to accurately predict IBD patients who are likely to respond well to currently available treatments (33).

One-size-fits-all paradigm vs precision medicine paradigm - a top-down vs a step-up approach is pictured

Figure 3. IBD Precision Medicine Paradigm: Instead of waiting for the patient’s response to treatment to understand their disease better and escalate to the next treatment, clinicians could use biomarkers to predict their treatment response more accurately. That allows them to tailor treatment strategies, starting high-risk patients on aggressive therapies that will lead to better disease outcomes and reducing exposure to unnecessary and costly biologics for low-risk patients. Current clinical practice is closer to the paradigm on the left (34).


Transcriptomics has been an essential tool for research on predicting treatment response in IBD patients: 

    1. Anti-TNF-α Therapy in CD: Marked transcriptional differences have been found between responders and non-responders of anti-TNF-α therapy (18).
    2. OSM and OSMR: High levels of oncostatin M (OSM) and its receptor (OSMR) in the inflamed gut of IBD patients were associated with non-response to anti-TNF therapy (19).
    3. Plasma Cells as Biomarkers: Percentage of plasma cells from inflamed biopsy samples from IBD patients predicted non-response to anti-TNF therapy. Non-responders also exhibited CCL7-CCR2 pathway upregulation and TREM1 downregulation (20). It’s worth noting that these findings have conflicted with other studies (64), likely due to varying definitions of responsiveness.
    4. Golimumab Response in UC: Transcriptomics was used to identify a gene expression signature in UC patients who achieved mucosal healing by weeks 6 and 30 (21).
    5. Response to Etrolizumab: Elevated baseline mucosal levels of granzyme A and integrin αE in UC patients have been associated with a favorable response to this monoclonal antibody treatment (22).
    6. MicroRNA Signatures and Response: A study showed microRNA signatures in colonic biopsies that could distinguish between responders and non-responders to corticosteroids, infliximab, and cyclosporine in acute severe UC cases (23).
    7. Expression Indicating Dose Escalation: Transcriptome-based models have been associated with the need for treatment escalation, including the expression of CLEC5A/CDH2 in UC patients (16).

Advantages of Multiomics Data Integration

When single datasets are analyzed in isolation, their potential to provide a full understanding of disease mechanisms can be limited. The complexity of diseases like IBD is not just due to the individual layers of biological processes but, more importantly, because of the interplay between these processes. Integrating data from multiple omic sources provides a more holistic understanding of the disease.

We can take examples from oncology, which, unlike IBD, has a high number of genomic and transcriptomic datasets. The WINTHER trial, for example, combined genomic and transcriptomic data along with other clinical data from triple-negative breast cancer patients to prioritize drug targets, which were subsequently verified using in vitro experiments (35).

Although genomic and transcriptomic combined datasets in IBD are limited, recent standout studies have emerged. In one example, researchers created an algorithm based on previously identified causal gene relationships and applied it to differential gene expression and clinical data to predict response to infliximab in UC patients. The researchers identified TNF, IFNG, and LPS as potential regulators of infliximab response with an accuracy of seventy percent (36). In another recent study, researchers predicted endoscopic response to ustekinumab by incorporating genomics and transcriptomics data into a machine learning model in patients with CD (37).

By concurrently screening a patient’s molecular data, such as genome and transcriptome, alongside their clinical data, such as medical records, machine learning algorithms can capture complex patterns previously impossible to discern.

Unlock the Power of Transcriptomics with Ovation’s IBD Omics Data

It is widely recognized that the combination of WGS, RNA-Seq, and clinical data offers life sciences researchers the chance to discover new biomarkers, stratify patients, identify and validate potential targets, and predict disease course and treatment response—all leading to the creation, refinement, and expansion of next-generation IBD diagnostics and treatments. Yet, there is a sparsity of multiomics data in IBD research that has created a major bottleneck to harnessing the power of systems biology (38). 

Ovation is filling this gap. Our IBD Omics Data helps life sciences organizations accelerate and de-risk the identification and validation of IBD therapeutic biomarkers and targets. Ovation’s extensive biobank includes thousands of normal and diseased tissue samples from IBD patients across various subtypes, including Crohn’s disease and ulcerative colitis. 

An initial sequenced cohort of 212 IBD patients with high-quality WGS and Total RNA-Seq on normal and diseased tissues linked to longitudinal clinical data is available for immediate delivery into life sciences researchers’ preferred storage and analytical environments. In addition to our initial cohort, Ovation generates novel datasets by sequencing material from our biobank of thousands of paired normal and diseased IBD tissue samples from the same patients and non-IBD healthy controls indexed with longitudinal clinical annotation, including surgical history, treatment exposures, and more.

The Ovation team analyzed RNA-Seq data from our initial cohort of 212 IBD tissue samples and identified ~1,600 genes that were differentially expressed in diseased tissue compared to normal tissue (p-value <0.001) across all IBD patients (Figure 4). This includes several signatures that have previously been reported, such as CXCL8, CXCL1, GPR15, S100A8, and S100A9 (8).

Volcano plot showing differential gene expression in Ovation IBD Omics Data

Figure 4. Ovation IBD Omics Data Initial Cohort Differential Gene Expression Levels. Plot of differentially expressed genes between inflamed and non-inflamed tissue from 212 IBD patients in Ovation’s dataset. Both tissue conditions were collected from colon, small intestine-ileum, and/or rectum for each patient. The adjusted p-value after -log10 transformation (y-axis) was plotted against the log2 fold-change (x-axis). Differentially expressed genes with adjusted p-values < 0.0001 and log2 fold-change > |0.6| were colored according to the direction of their fold-change; genes that were upregulated in inflamed tissue relative to non-inflamed tissue are colored blue, while those that were downregulated are colored in red. Top candidate genes names were labeled if they had adjusted p-values < 1.0E-21. Differential expression analysis was conducted using PyDESeq2 (alpha = 0.05), and the volcano plot was produced in R with ggplot2.

Ovation has demonstrated that high-quality NGS data can be generated from the large and rich set of samples sourced within the Ovation Research Network. This unique resource can be leveraged with patient clinical data, including claims and routine labs, to test research questions and validate predictive models. In addition, it adds to the collective sample diversity in the IBD research community, complementing existing datasets in the search for disease-associated genetic variants, biomarkers, and potential drug targets.

Charting the Future: The Promise of Precision Medicine in IBD

The challenges that IBD patients face are multifold: the ambiguity of diagnosis, the unpredictability of disease course, and the looming uncertainty of treatment efficacy. A diagnostic and treatment journey that is tailored to a patient’s unique biological makeup would make a significant difference to the six million lives currently impacted by IBD worldwide. 

At Ovation, we understand the critical role of comprehensive and diverse transcriptomics data, linked to genomic and clinical data, at population scale, in driving groundbreaking discoveries in IBD research. We are committed to empowering life sciences companies with unparalleled access to high-quality omics data, ensuring that they are well-equipped to pave the way for innovative IBD treatments that patients rightfully deserve. To discuss how Ovation’s RNA-Seq data can accelerate IBD drug development research, connect with our team.


  1. Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T. Transcriptomics technologies. PLoS Comput Biol. 2017;13(5):e1005457. Published 2017 May 18. doi:10.1371/journal.pcbi.1005457
  2. Casamassimi A, Federico A, Rienzo M, Esposito S, Ciccodicola A. Transcriptome Profiling in Human Diseases: New Advances and Perspectives. Int J Mol Sci. 2017;18(8):1652. Published 2017 Jul 29. doi:10.3390/ijms18081652
  3. Borowitz SM. The epidemiology of inflammatory bowel disease: Clues to pathogenesis? Front Pediatr. 2023;10:1103713. doi:10.3389/fped.2022.1103713
  4. Alatab S, Sepanlou SG, Ikuta K, et al. The global, regional, and national burden of inflammatory bowel disease in 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol Hepatol. 2020;5(1):17-30. doi:10.1016/S2468-1253(19)30333-4
  5. Fiocchi C, Dragoni G, Iliopoulos D, et al. Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-What, Why, and How. J Crohns Colitis. 2021;15(9):1410-1430. doi:10.1093/ecco-jcc/jjab051
  6. Denson LA, Curran M, McGovern DPB, et al. Challenges in IBD Research: Precision Medicine. Inflamm Bowel Dis. 2019;25(Supplement_2):S31-S39. doi:10.1093/ibd/izz078
  7. Li XL, Zhou CY, Sun Y, et al. Bioinformatic analysis of potential candidates for therapy of inflammatory bowel disease. Eur Rev Med Pharmacol Sci. 2015;19(22):4275-4284.
  8. Hong SN, Joung JG, Bae JS, et al. RNA-seq Reveals Transcriptomic Differences in Inflamed and Noninflamed Intestinal Mucosa of Crohn’s Disease Patients Compared with Normal Mucosa of Healthy Controls. Inflamm Bowel Dis. 2017;23(7):1098-1108. doi:10.1097/MIB.0000000000001066
  9. Massimino, L., Lamparelli, L.A., Houshyar, Y. et al. The Inflammatory Bowel Disease Transcriptome and Metatranscriptome Meta-Analysis (IBD TaMMA) framework. Nat Comput Sci 1, 511–515 (2021). https://doi.org/10.1038/s43588-021-00114-y
  10. Montero-Meléndez T, Llor X, García-Planella E, Perretti M, Suárez A. Identification of novel predictor classifiers for inflammatory bowel disease by gene expression profiling. PLoS One. 2013;8(10):e76235. Published 2013 Oct 14. doi:10.1371/journal.pone.0076235
  11. Marigorta UM, Denson LA, Hyams JS, et al. Transcriptional risk scores link GWAS to eQTLs and predict complications in Crohn’s disease. Nat Genet. 2017;49(10):1517-1521. doi:10.1038/ng.3936
  12. Biasci D, Lee JC, Noor NM, et al. A blood-based prognostic biomarker in IBD. Gut. 2019;68:1386-1395. doi: 10.1136/gutjnl-2019-318343
  13. Cushing KC, Mclean R, McDonald KG, et al. Predicting Risk of Postoperative Disease Recurrence in Crohn’s Disease: Patients With Indolent Crohn’s Disease Have Distinct Whole Transcriptome Profiles at the Time of First Surgery [published correction appears in Inflamm Bowel Dis. 2019 Nov 14;25(12):e167]. Inflamm Bowel Dis. 2019;25(1):180-193. doi:10.1093/ibd/izy228
  14. Bruno ME, Rogier EW, Arsenescu RI, et al. Correlation of Biomarker Expression in Colonic Mucosa with Disease Phenotype in Crohn’s Disease and Ulcerative Colitis. Dig Dis Sci. 2015;60(10):2976-2984. doi:10.1007/s10620-015-3700-2
  15. Palmieri O, Creanza TM, Bossa F, et al. Functional Implications of MicroRNAs in Crohn’s Disease Revealed by Integrating MicroRNA and Messenger RNA Expression Profiling. Int J Mol Sci. 2017 Jul 20;18(7):1580. doi: 10.3390/ijms18071580.
  16. Nowak JK, Adams AT, Kalla R, et al. Characterisation of the Circulating Transcriptomic Landscape in Inflammatory Bowel Disease Provides Evidence for Dysregulation of Multiple Transcription Factors Including NFE2, SPI1, CEBPB, and IRF2. J Crohns Colitis. 2022;16(8):1255-1268. doi:10.1093/ecco-jcc/jjac033
  17. Sahoo D, Swanson L, Sayed IM, et al. Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease. Nat Commun. 2021;12(1):4246. Published 2021 Jul 12. doi:10.1038/s41467-021-24470-5
  18. Váradi C, Holló Z, Póliska S, et al. Combination of IgG N-glycomics and corresponding transcriptomics data to identify anti-TNF-α treatment responders in inflammatory diseases. Electrophoresis 2015; 36(11–12):1330–5. doi: 10.1002/elps.201400575
  19. West NR, Hegazy AN, Owens BMJ, et al. Oncostatin M drives intestinal inflammation and predicts response to tumor necrosis factor-neutralizing therapy in patients with inflammatory bowel disease [published correction appears in Nat Med. 2017 Jun 6;23 (6):788]. Nat Med. 2017;23(5):579-589. doi:10.1038/nm.4307
  20. Gaujoux R, Starosvetsky E, Maimon N, et al. Cell-centred meta-analysis reveals baseline predictors of anti-TNFα non-response in biopsy and blood of patients with IBD. Gut. 2019;68(4):604-614. doi:10.1136/gutjnl-2017-315494
  21. Telesco SE, Brodmerkel C, Zhang H, et al. Gene Expression Signature for Prediction of Golimumab Response in a Phase 2a Open-Label Trial of Patients With Ulcerative Colitis. Gastroenterology. 2018;155(4):1008-1011.e8. doi:10.1053/j.gastro.2018.06.077
  22. Tew GW, Hackney JA, Gibbons D, et al. Association Between Response to Etrolizumab and Expression of Integrin αE and Granzyme A in Colon Biopsies of Patients With Ulcerative Colitis. Gastroenterology. 2016;150(2):477-87.e9. doi:10.1053/j.gastro.2015.10.041
  23. Morilla I, Uzzan M, Laharie D, et al. Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis. Clin Gastroenterol Hepatol. 2019;17(5):905-913. doi:10.1016/j.cgh.2018.08.068
  24. Gecse KB, Vermeire S. Differential diagnosis of inflammatory bowel disease: imitations and complications. Lancet Gastroenterol Hepatol. 2018;3(9):644-653. doi:10.1016/S2468-1253(18)30159-6
  25. Chen P, Zhou G, Lin J, et al. Serum Biomarkers for Inflammatory Bowel Disease. Front Med (Lausanne). 2020;7:123. Published 2020 Apr 22. doi:10.3389/fmed.2020.00123
  26. Sands BE. Biomarkers of Inflammation in Inflammatory Bowel Disease. Gastroenterology. 2015;149(5):1275-1285.e2. doi:10.1053/j.gastro.2015.07.003
  27. Noor NM, Verstockt B, Parkes M, Lee JC. Personalised medicine in Crohn’s disease. Lancet Gastroenterol Hepatol. 2020;5(1):80-92. doi:10.1016/S2468-1253(19)30340-1
  28. Hart AL, Rubin DT. Entering the Era of Disease Modification in Inflammatory Bowel Disease. Gastroenterology. 2022;162(5):1367-1369. doi:10.1053/j.gastro.2022.02.013
  29. Colombel JF, Narula N, Peyrin-Biroulet L. Management Strategies to Improve Outcomes of Patients With Inflammatory Bowel Diseases. Gastroenterology. 2017;152(2):351-361.e5. doi:10.1053/j.gastro.2016.09.046
  30. Zahn A, Giese T, Karner M, et al. Transcript levels of different cytokines and chemokines correlate with clinical and endoscopic activity in ulcerative colitis. BMC Gastroenterol. 2009;9:13. Published 2009 Feb 9. doi:10.1186/1471-230X-9-13
  31. Jossen J, Kiernan BD, Pittman N, Dubinsky MC. Anti-tumor Necrosis Factor-alpha Exposure Impacts Vedolizumab Mucosal Healing Rates in Pediatric Inflammatory Bowel Disease. J Pediatr Gastroenterol Nutr. 2020;70(3):304-309. doi:10.1097/MPG.0000000000002556
  32. Borg-Bartolo SP, Boyapati RK, Satsangi J, Kalla R. Precision medicine in inflammatory bowel disease: concept, progress and challenges. F1000Res. 2020;9:F1000 Faculty Rev-54. Published 2020 Jan 28. doi:10.12688/f1000research.20928.1
  33. Wang C, Baer HM, Gaya DR, Nibbs RJB, Milling S. Can molecular stratification improve the treatment of inflammatory bowel disease?. Pharmacol Res. 2019;148:104442. doi:10.1016/j.phrs.2019.104442
  34. Vieujean S, Louis E. Precision medicine and drug optimization in adult inflammatory bowel disease patients. Therap Adv Gastroenterol. 2023;16:17562848231173331. Published 2023 May 10. doi:10.1177/17562848231173331
  35. Vitali F, Cohen LD, Demartini A, et al. A Network-Based Data Integration Approach to Support Drug Repurposing and Multi-Target Therapies in Triple Negative Breast Cancer [published correction appears in PLoS One. 2017 Jan 11;12 (1):e0170363]. PLoS One. 2016;11(9):e0162407. Published 2016 Sep 15. doi:10.1371/journal.pone.0162407
  36. Zarringhalam K, Enayetallah A, Reddy P, Ziemek D. Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks. Bioinformatics. 2014;30(12):i69-i77. doi:10.1093/bioinformatics/btu272
  37. Verstockt B, Sudahakar P, Creyns B, et al. DOP70 An integrated multi-omics biomarker predicting endoscopic response in ustekinumab treated patients with Crohn’s disease. Journal of Crohn’s and Colitis. 2019; 13:S072–S073. doi.org/10.1093/ecco-jcc/jjy222.104
  38. Sudhakar P, Alsoud D, Wellens J, et al. Tailoring Multi-omics to Inflammatory Bowel Diseases: All for One and One for All. J Crohns Colitis. 2022;16(8):1306-1320. doi:10.1093/ecco-jcc/jjac027

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