AI applications in functional genomics

Caudai, Claudia; Galizia, Antonella; Geraci, Filippo; Pera, Loredana Le; Morea, Veronica; Salerno, Emanuele; Via, Allegra; Colombo, Teresa

doi:10.1016/j.csbj.2021.10.009

Cited by 52 publications

(44 citation statements)

References 403 publications

(394 reference statements)

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“…However, this large influx of data presents an issue, as no reliable or standardized means of analysis has been developed. Such data are too large to be analyzed through common visual analysis or statistical correlation methods ( Álvarez-Machancoses et al, 2020 ; Caudai et al, 2021 ). The use of AI and ML techniques alleviates this issue by allowing for the efficient management of data and providing the ability to recognize patterns in complex datasets ( Caudai et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…Such data are too large to be analyzed through common visual analysis or statistical correlation methods ( Álvarez-Machancoses et al, 2020 ; Caudai et al, 2021 ). The use of AI and ML techniques alleviates this issue by allowing for the efficient management of data and providing the ability to recognize patterns in complex datasets ( Caudai et al, 2021 ). In addition, the AI and ML techniques do not require explicit programming to complete specific tasks, as they are able to independently detect and analyze patterns within the data ( Caudai et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…The use of AI and ML techniques alleviates this issue by allowing for the efficient management of data and providing the ability to recognize patterns in complex datasets ( Caudai et al, 2021 ). In addition, the AI and ML techniques do not require explicit programming to complete specific tasks, as they are able to independently detect and analyze patterns within the data ( Caudai et al, 2021 ). The implementation of such techniques and methods also provides the ability to predict an attribute based on correlated data.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

et al. 2022

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Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

et al. 2022

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“…Overall, stoichiometric, thermodynamic, and kinetic information can be extracted from multi-omics data (Caudai et al, 2021) and processed to enable the generation of ODE-based genome-scale biological network models, a methodology that so far has been limited to simple organisms for applications in metabolic engineering and synthetic biology (Karr et al, 2012;Chakrabarti et al, 2013;Almquist et al, 2014;Srinivasan et al, 2015;Miskovic et al, 2019;St. John et al, 2019).…”

Section: Model Simulationmentioning

confidence: 99%

How artificial intelligence enables modeling and simulation of biological networks to accelerate drug discovery

DiNuzzo¹

2022

Front. Drug. Discov.

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The pharmaceutical industry suffered a significant decline of innovation in the last few decades, whose simple reason is complex biology. Artificial intelligence (AI) promises to make the entire drug discovery and development process more efficient. Here I consider the potential benefits of using AI to deepen our mechanistic understanding of disease by leveraging data and knowledge for modeling and simulation of genome-scale biological networks. I outline recent developments that are moving the field forward and I identify several overarching challenges for advancing the state of the art towards the successful integration of AI with modeling and simulation in drug discovery.

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“…In recent 10 years, with the development of modern genomics, [1][2][3] proteomics, [4,5] metabolomics, [6,7] and other "omics" theory [8,9] and with the introduction of system biology perspective and the application of bio informatics, the concept of network Pharmacology came into being. [10,11] It revealed the mystery of the synergistic effect of multi molecular drugs on the human body [12] based on the "disease-gene-targetdrug" interaction network and it systematically and comprehensively observed the intervention and influence of drugs on the disease network through network analysis.…”

Section: Introductionmentioning

confidence: 99%

The molecular mechanism of Ligusticum wallichii for improving idiopathic pulmonary fibrosis

Luo

et al. 2022

Medicine

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Background:At present, there was no evidence that any drugs other than lung transplantation can effectively treat Idiopathic Pulmonary Fibrosis (IPF). Ligusticum wallichii, or Chinese name Chuan xiong has been widely used in different fibrosis fields. Our aim is to use network pharmacology and molecular docking to explore the pharmacological mechanism of the Traditional Chinese medicine (TCM) Ligusticum wallichii to improve IPF.Materials and methods:The main chemical components and targets of Ligusticum wallichii were obtained from TCMSP, Swiss Target Prediction and Phammapper databases, and the targets were uniformly regulated in the Uniprot protein database after the combination. The main targets of IPF were obtained through Gencards, OMIM, TTD and DRUGBANK databases, and protein interaction analysis was carried out by using String to build PPI network. Metascape platform was used to analyze its involved biological processes and pathways, and Cytoscape3.8.2 software was used to construct “component-IPF target-pathway” network. And molecular docking verification was conducted through Auto Dock software.Results:The active ingredients of Ligusticum wallichii were Myricanone, Wallichilide, Perlolyrine, Senkyunone, Mandenol, Sitosterol and FA. The core targets for it to improve IPF were MAPK1, MAPK14, SRC, BCL2L1, MDM2, PTGS2, TGFB2, F2, MMP2, MMP9, and so on. The molecular docking verification showed that the molecular docking affinity of the core active compounds in Ligusticum wallichii (Myricanone, wallichilide, Perlolyrine) was <0 with MAPK1, MAPK14, and SRC. Perlolyrine has the strongest molecular docking ability, and its docking ability with SRC (−6.59 kJ/mol) is particularly prominent. Its biological pathway to improve IPF was mainly acted on the pathways in cancer, proteoglycans in cancer, and endocrine resistance, etc.Conclusions:This study preliminarily identified the various molecular targets and multiple pathways of Ligusticum wallichii to improve IPF.

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AI applications in functional genomics

Abstract: Graphical abstract

Cited by 52 publications

References 403 publications

Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

How artificial intelligence enables modeling and simulation of biological networks to accelerate drug discovery

The molecular mechanism of Ligusticum wallichii for improving idiopathic pulmonary fibrosis

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