A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 4, 2024

Abstract In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial has made it possible to effectively control disease using networks interconnected sensors worn by individuals. The purpose this work develop an AI-IoMT framework for identifying several chronic diseases form the patients’ medical record. For that, Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new framework, been developed rapid diagnosis like heart disease, diabetes, stroke. Then, Auto-Encoder Model (DAEM) used in proposed formulate imputed preprocessed data determining fields characteristics or information that are lacking. To speed up classification training testing, Golden Flower Search (GFS) approach then utilized choose best features from data. addition, cutting-edge Bias Integrated GAN (ColBGaN) model created precisely recognizing classifying types records patients. loss function optimally estimated during Water Drop Optimization (WDO) technique, reducing classifier’s error rate. Using some well-known benchmarking datasets performance measures, DACL’s effectiveness efficiency evaluated compared.

Language: Английский

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Artificial intelligence methods available for cancer research

Frontiers of Medicine, Journal Year: 2024, Volume and Issue: 18(5), P. 778 - 797

Published: Aug. 8, 2024

Abstract Cancer is a heterogeneous and multifaceted disease with significant global footprint. Despite substantial technological advancements for battling cancer, early diagnosis selection of effective treatment remains challenge. With the convenience large-scale datasets including multiple levels data, new bioinformatic tools are needed to transform this wealth information into clinically useful decision-support tools. In field, artificial intelligence (AI) technologies their highly diverse applications rapidly gaining ground. Machine learning methods, such as Bayesian networks, support vector machines, decision trees, random forests, gradient boosting, K-nearest neighbors, neural network models like deep learning, have proven valuable in predictive, prognostic, diagnostic studies. Researchers recently employed large language tackle dimensions problems. However, leveraging opportunity utilize AI clinical settings will require surpassing obstacles—a major issue lack use available reporting guidelines obstructing reproducibility published review, we discuss methods explore benefits limitations. We summarize healthcare highlight potential role impact on future directions cancer research.

Language: Английский

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Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter

Journal of Cancer Research and Clinical Oncology, Journal Year: 2023, Volume and Issue: 149(13), P. 11279 - 11294

Published: June 27, 2023

Language: Английский

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The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

Technology in Cancer Research & Treatment, Journal Year: 2024, Volume and Issue: 23

Published: Jan. 1, 2024

Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, Deep Learning oncology, explaining key concepts algorithms (like SVM, Naïve Bayes, CNN) a clear, accessible manner. It aims to make advancements understandable broad audience, focusing on their application diagnosing, classifying, predicting various types, thereby underlining AI's potential better outcomes. Moreover, we present tabular summary most significant advances from literature, offering time-saving resource for readers grasp each study's main contributions. The remarkable benefits AI-powered underscore advancing research clinical practice. is valuable researchers clinicians interested transformative implications care.

Language: Английский

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Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma

Biochemistry and Biophysics Reports, Journal Year: 2023, Volume and Issue: 37, P. 101587 - 101587

Published: Nov. 25, 2023

Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital advancing diagnostics therapies. This study combines bioinformatics machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such Gene Ontology (GO) function Kyoto Encyclopedia Genes Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, ESR1 were significantly positively correlated overall patient survival, while AKR1B10 NQO1 displayed negative correlation. Moreover, Adaboost prediction model yielded an 86.8 % accuracy, showcasing learning's potential deciphering dataset patterns clinically relevant predictions. findings promise contribute valuable insights into elusive mechanisms driving liver (HCC). They hold guide development more precise diagnostic methods treatment strategies future. In fight against this health challenge, unraveling intricacies paramount importance.

Language: Английский

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Basing on the machine learning model to analyse the coronary calcification score and the coronary flow reserve score to evaluate the degree of coronary artery stenosis

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 163, P. 107130 - 107130

Published: June 3, 2023

Language: Английский

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Using the Random Forest for Identifying Key Physicochemical Properties of Amino Acids to Discriminate Anticancer and Non-Anticancer Peptides

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(13), P. 10854 - 10854

Published: June 29, 2023

Anticancer peptides (ACPs) represent a promising new therapeutic approach in cancer treatment. They can target cells without affecting healthy tissues or altering normal physiological functions. Machine learning algorithms have increasingly been utilized for predicting peptide sequences with potential ACP effects. This study analyzed four benchmark datasets based on well-established random forest (RF) algorithm. The were converted into 566 physicochemical features extracted from the amino acid index (AAindex) library, which then subjected to feature selection using methods: light gradient-boosting machine (LGBM), analysis of variance (ANOVA), chi-squared test (Chi

Language: Английский

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Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records

International Journal on Recent and Innovation Trends in Computing and Communication, Journal Year: 2023, Volume and Issue: 11(7), P. 78 - 84

Published: Sept. 1, 2023

The purpose of this study is to show how ensemble learning-driven machine learning algorithms outperform individual at predicting ovarian cancer on a biomarker dataset. Additionally, provides model explanations using explainable Artificial Intelligence methods, method involved gathering and combining 49 risk factors from 349 patients. We hypothesize that systems are superior Machine Learning in cancer. system consists five were trained K-10 cross validation protocols. These training models then used predict the development benign tumors AUC Accuracy metrics for increased by 19% 16%. MCC Kappa scores also over 29% 33%, respectively. As result, we draw conclusion ensembled-based terms carcinoma prediction.

Language: Английский

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Combination of multiple omics techniques for a personalized therapy or treatment selection

Frontiers in Immunology, Journal Year: 2023, Volume and Issue: 14

Published: Sept. 27, 2023

Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number patients long-term responses. Moreover, due to differences within in tumor mutational burden, composition microenvironment as well peripheral immune system microbiome, development escape mechanisms, there is no “one fit all” therapy. Thus, must be personalized based on specific molecular, immunologic and/or metabolic landscape their tumor. In order identify for each patient best possible therapy, different approaches should employed combined. These include (i) use predictive biomarkers identified large cohorts with same type (ii) evaluation individual “omics”-based analyses its ex vivo characterization susceptibility therapies.

Language: Английский

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Comprehensive analysis based on glycolytic and glutaminolytic pathways signature for predicting prognosis and immunotherapy in ovarian cancer

Journal of Cancer, Journal Year: 2023, Volume and Issue: 15(2), P. 383 - 400

Published: Dec. 5, 2023

Background: Our study attempts to develop and identify an aerobic glycolysis glutamine-related genes (AGGRGs) signature for estimating prognostic effectively of ovarian cancer (OV) patients.Materials & methods: OV related data were extracted from the multiple public databases, including TCGA-OV, GSE26193, GSE63885, ICGC-OV.A consistent clustering approach was used characterize subtypes associated with AGGRGs.LASSO Cox regressions utilized construct prognosis signatures AGGRGs.In addition, GSE63885 ICGC-OV served as independent external cohorts assess reliability model.In vitro in vivo experiments conducted role AAK1 malignant progression glutamine metabolism OV, assessed its therapeutic potential treating patients.Results: patients could be separated into four (quiescent, glycolysis, glutaminolytic, mixed subtypes).The survival outcome glutaminolytic subtype notably worse than glycolytic subtype.Besides, we identified eight AGGRGs (AAK1, GJB6, HMGN5, LPIN3, INTS6L, PPOX, SPAG4, ZNF316) establish a patients.Comprehensive analysis revealed that risk score factor OV.Additionally, high-risk less sensitive platinum and, conversely, proved more responsive immunotherapy low-risk score.In cytological experiments, found promote via activating Notch3 pathway cells.Furthermore, knockdown significantly inhibited tumor growth weight, decreased lung metastases, ultimately extended time nude mice.Conclusions: The constructed efficiently estimate effectiveness patients.In may represent promising target OV.

Language: Английский

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