Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Journal of Asthma and Allergy, Год журнала: 2022, Номер Volume 15, С. 855 - 873

Опубликована: Июнь 1, 2022

Asthma is a variable long-term condition. Currently, there no cure for asthma and the focus is, therefore, on management. Mobile health (mHealth) promising chronic disease management but to be able realize its potential, it needs go beyond simply monitoring. mHealth therefore leverage machine learning provide tailored feedback with personalized algorithms. There need understand extent of that has been leveraged in context This review aims fill this gap.

Язык: Английский

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Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Июнь 21, 2023

Abstract Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region single-cell sequencing of tumors enable high-resolution reconstruction the mutational history each tumor highlight extensive diversity across patients. Resolving interactions among mutations recovering recurrent processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, joint inference exclusivity patterns trajectories from cohort intra-tumor phylogenetic trees. Through simulations, show that TreeMHN outperforms existing alternatives can only focus on one aspect task. By analyzing datasets blood, lung, breast cancers, find most likely patterns, consistent with enriching our current understanding tumorigenesis. Moreover, facilitates prediction evolution provides measures next events given tree, prerequisite evolution-guided treatment

Язык: Английский

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Artificial intelligence-driven biomedical genomics

Knowledge-Based Systems, Год журнала: 2023, Номер 279, С. 110937 - 110937

Опубликована: Сен. 7, 2023

Язык: Английский

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Precision cancer classification using liquid biopsy and advanced machine learning techniques

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Март 10, 2024

Abstract Cancer presents a significant global health burden, resulting in millions of annual deaths. Timely detection is critical for improving survival rates, offering crucial window timely medical interventions. Liquid biopsy, analyzing genetic variations, and mutations circulating cell-free, tumor DNA (cfDNA/ctDNA) or molecular biomarkers, has emerged as tool early detection. This study focuses on cancer using plasma cfDNA/ctDNA protein biomarker concentrations. The proposed system initially calculates the correlation coefficient to identify correlated features, while mutual information assesses each feature's relevance target variable, eliminating redundant features improve efficiency. eXtrem Gradient Boosting (XGBoost) feature importance method iteratively selects top ten 60% dataset dimensionality reduction. Light Machine (LGBM) model employed classification, optimizing its performance through random search hyper-parameters. Final predictions are obtained by ensembling LGBM models from tenfold cross-validation, weighted their respective balanced accuracy, averaged get final predictions. Applying this methodology, achieves 99.45% accuracy 99.95% AUC detecting presence achieving 93.94% 97.81% cancer-type classification. Our methodology leads enhanced healthcare outcomes patients.

Язык: Английский

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Measuring Clonal Evolution in Cancer with Genomics

Annual Review of Genomics and Human Genetics, Год журнала: 2019, Номер 20(1), С. 309 - 329

Опубликована: Май 6, 2019

Cancers originate from somatic cells in the human body that have accumulated genetic alterations. These mutations modify phenotype of cells, allowing them to escape homeostatic regulation maintains normal cell number. Viewed through lens evolutionary biology, transformation into malignant is evolution action. Evolution continues throughout cancer growth, progression, treatment resistance, and disease relapse, driven by adaptation changes cancer's environment, intratumor heterogeneity an inevitable consequence this process. Genomics provides a powerful means characterize tumor evolution, enabling quantitative measurement evolving clones across space time. In review, we discuss concepts approaches quantify measure process using genomics.

Язык: Английский

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Somatic mutations in early metazoan genes disrupt regulatory links between unicellular and multicellular genes in cancer

eLife, Год журнала: 2019, Номер 8

Опубликована: Фев. 26, 2019

Extensive transcriptional alterations are observed in cancer, many of which activate core biological processes established unicellular organisms or suppress differentiation pathways formed metazoans. Through rigorous, integrative analysis genomics data from a range solid tumors, we show changes tumors tied to mutations disrupting regulatory interactions between and multicellular genes within human gene networks (GRNs). Recurrent point were enriched regulator linking subnetworks, while copy-number affected downstream target distinctly regions the GRN. Our results depict drivers tumourigenesis as that created key links during evolution early life, whose dysfunction creates widespread dysregulation primitive elements Several identified important this process associated with drug response, demonstrating potential clinical value our approach.

Язык: Английский

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Executable cancer models: successes and challenges

Nature reviews. Cancer, Год журнала: 2020, Номер 20(6), С. 343 - 354

Опубликована: Апрель 27, 2020

Язык: Английский

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Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

International Journal of Molecular Sciences, Год журнала: 2021, Номер 22(9), С. 4394 - 4394

Опубликована: Апрель 22, 2021

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets interest cancer diagnosis and therapies but, to start revolution, software tools need be adapted cover new requirements. In sense, learning becoming a commodity able assist doctors on daily basis, it essential fully understand how models can interpreted. survey, we analyse current machine other in-silico as applied medicine—specifically, research—and discuss their interpretability, performance input data they fed with. neural networks (ANN), logistic regression (LR) support vector machines (SVM) have been observed preferred models. addition, convolutional (CNNs), supported by rapid development graphic processing units (GPUs) high-performance computing (HPC) infrastructures, gaining importance when image feasible. However, interpretability predictions so that them, trust them gain useful insights for clinical practice still rarely considered, which factor needs improved enhance doctors’ predictive capacity achieve individualised near future.

Язык: Английский

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Advances in Research of Adult Gliomas

International Journal of Molecular Sciences, Год журнала: 2021, Номер 22(2), С. 924 - 924

Опубликована: Янв. 18, 2021

Diffuse gliomas are the most frequent brain tumours, representing 75% of all primary malignant tumours in adults. Because their locally aggressive behaviour and fact that they cannot be cured by current therapies, represent one devastating cancers. The present review summarises recent advances our understanding glioma development progression use various vitro vivo models, as well more complex techniques including cultures 3D organoids organotypic slices. We discuss progress has been made heterogeneity, alteration gene expression DNA methylation, silico models. Lastly treatment options future clinical trials, which aim to improve early diagnosis disease monitoring, also discussed.

Язык: Английский

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Precision Oncology Beyond Genomics: The Future Is Here—It Is Just Not Evenly Distributed

Cells, Год журнала: 2021, Номер 10(4), С. 928 - 928

Опубликована: Апрель 17, 2021

Cancer is a multifactorial disease with increasing incidence. There are more than 100 different cancer types, defined by location, cell of origin, and genomic alterations that influence oncogenesis therapeutic response. This heterogeneity between tumors patients also the within same patient's tumor pose an enormous challenge to treatment. In this review, we explore on longitudinal latitudinal axis, reviewing current future approaches study their potential support oncologists in tailoring treatment regimen. We highlight how ideal precision oncology reaching far beyond knowledge genetic variants inform clinical practice discuss technologies strategies already available improve our understanding management will focus integrating multi-omics suitable vitro models proficiency mimicking endogenous heterogeneity.

Язык: Английский

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