Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential

Al-Taie, Zainab; Liu, Danlu; Mitchem, Jonathan B.; Papageorgiou, Christos; Kaifi, Jussuf T.; Warren, Wesley C.; Shyu, Chi‐Ren

doi:10.1016/j.jbi.2021.103792

Cited by 15 publications

(27 citation statements)

References 90 publications

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“…Variations will affect the resulting number of patterns and the subgroups. Secondly, while this paper is focused on TNBC using a foundation developed for colorectal cancer [ 37 ], a pan-cancer study is needed to further test the robustness of the methods in all major cancers. Thirdly, further studies are required in a “wet lab” in order to validate our data and monitor the efficacy of repurposed drugs suggested, like TKIs and antioxidants.…”

Section: Discussionmentioning

confidence: 99%

“…These levels are path expansion, floating subgroup selection, and inclusion and exclusion. The subgrouping algorithm is an extension of our exploratory data mining methods [ 37 , 46 ]. The algorithm begins with a disease population.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, these biomedical entities include genes, pathways, cellular functions, molecular components, biological processes, diseases, and drugs. The subgroup contrast score ( SPCscore ) [ 37 ] (Equation (5)) is calculated for each subgroup.

…”

Section: Methodsmentioning

confidence: 99%

“…This study implements our data-driven approach to stratify BC patients based on genotypic and phenotypic data. The genotypic data is mapped onto a heterogeneous drug knowledge base to find druggable homogeneous BC subgroups and to reposition drugs for each subgroup [ 37 ]. We have decided to focus on triple negative breast cancer (TNBC), as defined by the lack of Estrogen Receptors (ERs) and progesterone receptors (PRs) and by a HER2-negative status, due to its increasingly recognized heterogeneity not only on the molecular level but also on the pathologic and clinical levels [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

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Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer

Al-Taie

Hannink

Mitchem

et al. 2021

Cancers

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Breast cancer (BC) is the leading cause of death among female patients with cancer. Patients with triple-negative breast cancer (TNBC) have the lowest survival rate. TNBC has substantial heterogeneity within the BC population. This study utilized our novel patient stratification and drug repositioning method to find subgroups of BC patients that share common genetic profiles and that may respond similarly to the recommended drugs. After further examination of the discovered patient subgroups, we identified five homogeneous druggable TNBC subgroups. A drug repositioning algorithm was then applied to find the drugs with a high potential for each subgroup. Most of the top drugs for these subgroups were chemotherapy used for various types of cancer, including BC. After analyzing the biological mechanisms targeted by these drugs, ferroptosis was the common cell death mechanism induced by the top drugs in the subgroups with neoplasm subdivision and race as clinical variables. In contrast, the antioxidative effect on cancer cells was the common targeted mechanism in the subgroup of patients with an age less than 50. Literature reviews were used to validate our findings, which could provide invaluable insights to streamline the drug repositioning process and could be further studied in a wet lab setting and in clinical trials.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer

Al-Taie

Hannink

Mitchem

et al. 2021

Cancers

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“…With the advance of the 2020–2030 strategic plan for nutrition research created by the NIH 64 , large-scale, open-source datasets will be available, encouraging the collaboration of AI and metabolism researchers. While data modality and volume are rapidly increasing, innovative explainable AI methods will become more important for automatic cohort stratification to tailor interventions for targeting patients 65 , 66 . Deeper phenotypic characterization of individuals will be essential for personalized treatments.…”

Section: Research Needs To Support the Integration Of Isotopes In Pre...mentioning

confidence: 99%

Measurement of lipid flux to advance translational research: evolution of classic methods to the future of precision health

2022

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Over the past 70 years, the study of lipid metabolism has led to important discoveries in identifying the underlying mechanisms of chronic diseases. Advances in the use of stable isotopes and mass spectrometry in humans have expanded our knowledge of target molecules that contribute to pathologies and lipid metabolic pathways. These advances have been leveraged within two research paths, leading to the ability (1) to quantitate lipid flux to understand the fundamentals of human physiology and pathology and (2) to perform untargeted analyses of human blood and tissues derived from a single timepoint to identify lipidomic patterns that predict disease. This review describes the physiological and analytical parameters that influence these measurements and how these issues will propel the coming together of the two fields of metabolic tracing and lipidomics. The potential of data science to advance these fields is also discussed. Future developments are needed to increase the precision of lipid measurements in human samples, leading to discoveries in how individuals vary in their production, storage, and use of lipids. New techniques are critical to support clinical strategies to prevent disease and to identify mechanisms by which treatments confer health benefits with the overall goal of reducing the burden of human disease.

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Using Explainable Artificial Intelligence in Drug Discovery: A Theoretical Research

Aksoy,

Yücel,

Aydin

2023

Explainable Machine Learning for Multimedia Based Healthcare Applications

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Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential

Cited by 15 publications

References 90 publications

Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer

Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer

Measurement of lipid flux to advance translational research: evolution of classic methods to the future of precision health

Using Explainable Artificial Intelligence in Drug Discovery: A Theoretical Research

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