Emerging applications of machine learning in genomic medicine and healthcare

Chafai, Narjice; Bonizzi, Luigi; Botti, Sara; Badaoui, Bouabid

doi:10.1080/10408363.2023.2259466

Cited by 10 publications

(2 citation statements)

References 155 publications

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“…Genomic data analysis represents another frontier where AI is demonstrating significant impact. Machine learning algorithms, ranging from Decision Tree (DT) to Gradient Boosting Machines (GBMs), play a key role in the discovery of genetic markers associated with antibiotic resistance [ 40 ]. For example, Arango-Argoty et al [ 41 ] developed DeepARG, a novel deep learning approach designed to predict ARGs from metagenomic data, which consists of two models to respond to different annotation strategies, i.e., DeepARG-SS for short read sequences and DeepARG-LS for full gene length sequences.…”

Section: Artificial Intelligence-based Diagnostic Tools For Early Det...mentioning

confidence: 99%

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future

Branda,

Scarpa

2024

Antibiotics

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Antibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to detect resistance markers early on, enabling early interventions. In addition, AI-powered decision support systems can optimize antibiotic use by recommending the most effective treatments based on patient data and local resistance patterns. AI can accelerate drug discovery by predicting the efficacy of new compounds and identifying potential antibacterial agents. Although progress has been made, challenges persist, including data quality, model interpretability, and real-world implementation. A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations.

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Section: Artificial Intelligence-based Diagnostic Tools For Early Det...mentioning

confidence: 99%

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future

Branda,

Scarpa

2024

Antibiotics

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“…In the contemporary landscape of biomedical research, AI has emerged as a transformative force across various domains of regenerative medicine, encompassing drug discovery, disease modeling, predictive modeling, personalized medicine, tissue engineering, cell therapy, clinical trial design, patient monitoring, patient education, and regulatory compliance [16][17][18][19][20]. Our examination specifically narrows to the integration of AI within tissue engineering.…”

Section: Introductionmentioning

confidence: 99%

A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds

Rafieyan,

Ansari,

Vasheghani-Farahani

2024

Biofabrication

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3D (Bio)printing is a highly effective method for fabricating tissue engineering scaffolds, renowned for their exceptional precision and control. Artificial intelligence (AI) has become a crucial technology in this field, capable of learning and replicating complex patterns that surpass human capabilities. However, the integration of AI in tissue engineering is often hampered by the lack of comprehensive and reliable data. This study addresses these challenges by providing one of the most extensive datasets on 3D-printed scaffolds. It provides the most comprehensive open-source dataset and employs various AI techniques, from unsupervised to supervised learning. This dataset includes detailed information on 1,171 scaffolds, featuring a variety of biomaterials and concentrations—including 60 biomaterials such as natural and synthesized biomaterials, crosslinkers, enzymes, etc.—along with 49 cell lines, cell densities, and different printing conditions. We used over 40 machine learning and deep learning algorithms, tuning their hyperparameters to reveal hidden patterns and predict cell response, printability, and scaffold quality. The clustering analysis using KMeans identified five distinct ones. In classification tasks, algorithms such as XGBoost, Gradient Boosting, Extra Trees Classifier, Random Forest Classifier, and LightGBM demonstrated superior performance, achieving higher accuracy and F1 scores. A fully connected neural network with six hidden layers from scratch was developed, precisely tuning its hyperparameters for accurate predictions. To promote future research, the developed dataset and the associated code are publicly available on www.github.com/saeedrafieyan/MLATE.

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From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare

Chakraborty,

Bhattacharya,

Pal

et al. 2024

Current Research in Biotechnology

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Emerging applications of machine learning in genomic medicine and healthcare

Cited by 10 publications

References 155 publications

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future

A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds

From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare

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