Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Adir, Omer; Poley, Maria; Chen, Gal; Froim, Sahar; Krinsky, Nitzan; Shklover, Jeny; Shainsky‐Roitman, Janna; Lammers, Twan; Schroeder, Avi

doi:10.1002/adma.201901989

Cited by 235 publications

(186 citation statements)

References 178 publications

(144 reference statements)

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“…Some important uses of ML applications in clinical practice include: provision of up-to-date information for reducing diagnostic and therapeutic errors, real time inferences, health risk alerts, and health outcome predictions 11,12 . Though there is substantial literature of AI and ML in healthcare research, most of the research focuses in the fields of Cancer, Neurology and Cardiology 11,[13][14][15][16][17][18][19][20][21] . In addition, the literature lacks successful applications of ML that deal with complex medical diagnostic fields like Hematology 22 .…”

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confidence: 99%

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Syed-Abdul

Firdani

Chung

et al. 2020

Sci Rep

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Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD. The data collection was done at Konkuk University Medical Center, Seoul. A total of (882 cases: 457 hematologic malignancy and 425 hematologic nonmalignancy) were used for analysis. In our study, seven machine learning models, i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN, were used. In order to measure the performance of our ML models, stratified 10-fold cross validation was performed, and metrics, such as accuracy, precision, recall, and AUC were used. We observed outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5% ± 2.6 respectively. ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.

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confidence: 99%

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Syed-Abdul

Firdani

Chung

et al. 2020

Sci Rep

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“…AI and other deep learning tools and techniques can be utilized to optimize the utilization of patients’ derived multi-omics data to extract target bio-entities and fit the targets with drug–target interaction data to extract relevant drugs and doses in the omics data landscape. Technologies like nanotechnology are boosting the attempt to targeted drug delivery ( 107 ). Software like G-DOC Plus provides infrastructure to explore and analyze clinical, multi-omics data at different levels, from individual to a population as a whole ( 108 ).…”

Section: Precision Medicine Approachesmentioning

confidence: 99%

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Biswas

Chakrabarti

2020

Front. Oncol.

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Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of “big”-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

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“…AI can also be successfully interfaced with nanomedicine, specifically in the optimization of combination drug therapy based on the stratification of patients as per the BC type and to maintain the drug levels at the target site [ 193 ]. In addition, AI may also aid in the selection of materials for the preparation of nanocarriers based on their interaction with drug targets, biological fluids, and cell membranes, which ultimately affect therapeutic efficacy [ 194 ]. However, challenges such as data integrity, a huge amount of data and translation of this data into knowledge, ethical consideration, and regulatory approvals lay back the clinical translation of AI [ 195 ].…”

Section: Artificial Intelligence In Personalized Bc Therapymentioning

confidence: 99%

Combination Therapy and Nanoparticulate Systems: Smart Approaches for the Effective Treatment of Breast Cancer

et al. 2020

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Breast cancer has become one of the biggest concerns for oncologists in the past few decades because of its unpredictable etiopathology and nonavailability of personalized translational medicine. The number of women getting affected by breast cancer has increased dramatically, owing to lifestyle and environmental changes. Besides, the development of multidrug resistance has become a challenge in the therapeutic management of breast cancer. Studies reveal that the use of monotherapy is not effective in the management of breast cancer due to high toxicity and the development of resistance. Combination therapies, such as radiation therapy with adjuvant therapy, endocrine therapy with chemotherapy, and targeted therapy with immunotherapy, are found to be effective. Thus, multimodal and combination treatments, along with nanomedicine, have emerged as a promising strategy with minimum side effects and drug resistance. In this review, we emphasize the multimodal approaches and recent advancements in breast cancer treatment modalities, giving importance to the current data on clinical trials. The novel treatment approach by targeted therapy, according to type, such as luminal, HER2 positive, and triple-negative breast cancer, are discussed. Further, passive and active targeting technologies, including nanoparticles, bioconjugate systems, stimuli-responsive, and nucleic acid delivery systems, including siRNA and aptamer, are explained. The recent research exploring the role of nanomedicine in combination therapy and the possible use of artificial intelligence in breast cancer therapy is also discussed herein. The complexity and dynamism of disease changes require the constant upgrading of knowledge, and innovation is essential for future drug development for treating breast cancer.

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Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Cited by 235 publications

References 178 publications

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Combination Therapy and Nanoparticulate Systems: Smart Approaches for the Effective Treatment of Breast Cancer

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