Personalized Medicine and Machine Learning: A Roadmap for the Future

Sebastiani, Marco; Vacchi, Caterina; Manfredi, Andreina; Cassone, Giulia

doi:10.3390/jcm11144110

Cited by 19 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, the prognostic models can be standardized for specific group of patients using their gene expression patterns and can be used for discovering candidate/prognostic genes specific to a subgroup of patients. There have been several reports promoting the use of AI-ML techniques in personalized medicine ( Schork, 2019 ; Peng et al, 2021 ; Sebastiani et al, 2022 ). Thus, our study could aid in personalized medicine of hematological malignancies.…”

Section: Discussionmentioning

confidence: 99%

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning

Zhang,

Liu,

et al. 2024

Front. Pharmacol.

View full text Add to dashboard Cite

Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor.Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML. Furthermore, using different ML algorithms, prognostic models were constructed and validated using the DHRGs. At the end molecular docking studies were performed to identify potential drug candidates targeting the selected DHRGs.Results: We identified a total of 80 DHRGs by comparing the differentially expressed genes derived between AML patients and normal controls and high-risk AML genes identified by Cox regression. Genetic and epigenetic alteration analyses of the DHRGs revealed a significant association of their copy number variations and methylation status with overall survival (OS) of AML patients. Out of the 137 models constructed using different ML algorithms, the combination of Ridge and plsRcox maintained the highest mean C-index and was used to build the final model. When AML patients were classified into low- and high-risk groups based on DHRGs, the low-risk group had significantly longer OS in the AML training and validation cohorts. Furthermore, immune infiltration, miRNA coexpression, stemness feature and hallmark pathway analyses revealed significant differences in the prognosis of the low- and high-risk AML groups. Drug sensitivity and molecular docking studies revealed top 5 drugs, including carboplatin and austocystin-D that may significantly affect the DHRGs in AML.Conclusion: The findings from the current study identified a set of high-risk genes that may be used as prognostic and therapeutic markers for AML patients. In addition, significant use of the ML algorithms in constructing and validating the prognostic models in AML was demonstrated. Although our study used extensive bioinformatics and machine learning methods to identify the hub genes in AML, their experimental validations using knock-out/-in methods would strengthen our findings.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning

Zhang,

Liu,

et al. 2024

Front. Pharmacol.

View full text Add to dashboard Cite

show abstract

“…Comorbidities and extra-articular manifestations of RA have been deeply investigated in the last ten years and largely influence the treatment of these patients. In particular, cardiovascular comorbidities and lung involvement can worsen the prognosis and the quality of life of RA patients, limiting the available therapeutic options for the rheumatologist [ 72 , 73 ].…”

Section: The Evolution Of Treatment In Rheumatoid Arthritis: the Long...mentioning

confidence: 99%

From Bench to Bedside in Rheumatoid Arthritis from the “2022 GISEA International Symposium”

Vitale

Alivernini

Caporali

et al. 2023

JCM

Self Cite

View full text Add to dashboard Cite

While precision medicine is still a challenge in rheumatic disease, in recent years many advances have been made regarding pathogenesis, the treatment of inflammatory arthropathies, and their interaction. New insight into the role of inflammasome and synovial tissue macrophage subsets as predictors of drug response give hope for future tailored therapeutic strategies and a personalized medicine approach in inflammatory arthropathies. Here, we discuss the main pathogenetic mechanisms and therapeutic approaches towards precision medicine in rheumatoid arthritis from the 2022 International GISEA/OEG Symposium.

show abstract

“…Personalized treatment: Machine learning models could be used to develop personalized treatment plans for patients with allergic rhinitis. These treatment plans could be based on the patient's free IgE concentration, other factors, and the patient's preferences [20][21][22] . Overall, the development of machine learning-based prediction models for free IgE concentration in patients with allergic rhinitis treated with AIT and omalizumab has the potential to significantly improve the management of allergic rhinitis.…”

Section: Background and Significancementioning

confidence: 99%

Machine Learning-Based Prediction of Free IgE Concentration in Allergic Rhinitis Patients Treated with Allergen Immunotherapy and Omalizumab

Olanrewaju,

Ngansop Djampou

2023

Preprint

View full text Add to dashboard Cite

Free immunoglobulin E (IgE) concentration is a key biomarker for allergic diseases. Prediction of free IgE concentration can help clinicians diagnose and monitor allergic diseases more effectively. In this study, we used machine learning to predict free IgE concentration in the blood serum of patients with allergic rhinitis who received allergen immunotherapy co-administered with omalizumab. The predictors for free IgE concentration were the number of visits for treatment and baseline checking, and treatment groups (1) omalizumab/ragweed, (2) omalizumab/placebo, (3) placebo/ragweed, and (4) placebo/placebo. Several machine learning algorithms (MLA) were trained with the immunotherapy dataset imported from Immune Tolerance Network (ITN) TrialShare into the Orange data mining platform. The decision tree algorithm model amidst the list of MLAs trained and tested was the best performing model for predicting free IgE concentration, with an R-squared of about 0.6. This study demonstrates that machine learning can be used to predict free IgE concentration with high accuracy. This prediction model could be used to help clinicians diagnose and monitor allergic diseases more effectively.

show abstract

Personalized Medicine and Machine Learning: A Roadmap for the Future

Abstract: In the last ten years, many advances have been made in the treatment and diagnosis of immune-mediated diseases [...]

Cited by 19 publications

References 23 publications

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning

From Bench to Bedside in Rheumatoid Arthritis from the “2022 GISEA International Symposium”

Machine Learning-Based Prediction of Free IgE Concentration in Allergic Rhinitis Patients Treated with Allergen Immunotherapy and Omalizumab

Contact Info

Product

Resources

About