A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

[1]  D. Louis,et al.  The 2021 World Health Organization Classification of Tumors of the Central Nervous System: What Neuroradiologists Need to Know , 2022, American Journal of Neuroradiology.

[2]  E. Li,et al.  Guide to Metabolomics Analysis: A Bioinformatics Workflow , 2022, Metabolites.

[3]  M. Ciborowski,et al.  Proteomics and metabolomics approach in adult and pediatric glioma diagnostics. , 2022, Biochimica et biophysica acta. Reviews on cancer.

[4]  Zaved Siddiqui,et al.  A non-invasive method for concurrent detection of early-stage women-specific cancers , 2022, Scientific Reports.

[5]  Fan Yang,et al.  Comprehensive metabolomics expands precision medicine for triple-negative breast cancer , 2022, Cell Research.

[6]  A. Feuchtinger,et al.  The synergism of spatial metabolomics and morphometry improves machine learning‐based renal tumour subtype classification , 2022, Clinical and translational medicine.

[7]  M. Stampfer,et al.  A prospective study of pre-diagnostic circulating tryptophan and kynurenine, and the kynurenine/tryptophan ratio and risk of glioma. , 2021, Cancer Epidemiology.

[8]  B. Bojko,et al.  Metabolomic Phenotyping of Gliomas: What Can We Get with Simplified Protocol for Intact Tissue Analysis? , 2022, Cancers.

[9]  Yuanting Zheng,et al.  Correction: Both IDO1 and TDO contribute to the malignancy of gliomas via the Kyn–AhR–AQP4 signaling pathway , 2021, Signal Transduction and Targeted Therapy.

[10]  M. Kretowski,et al.  Integration of solutions and services for multi-omics data analysis towards personalized medicine , 2021, Biocybernetics and Biomedical Engineering.

[11]  T. Xiong,et al.  Trends in Intracranial Glioma Incidence and Mortality in the United States, 1975-2018 , 2021, Frontiers in Oncology.

[12]  H. Azzazy,et al.  Cell surface sphingomyelin: key role in cancer initiation, progression, and immune evasion , 2021, Lipids in Health and Disease.

[13]  A. Gyenesei,et al.  Gut Microbiome in Chronic Coronary Syndrome Patients , 2021, Journal of clinical medicine.

[14]  R. Bachoo,et al.  Glutamine anaplerosis is required for amino acid biosynthesis in human meningiomas. , 2021, Neuro-oncology.

[15]  Leo L. Cheng,et al.  Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data , 2021, Metabolites.

[16]  W. Mao,et al.  Combination of Plasma-Based Metabolomics and Machine Learning Algorithm Provides a Novel Diagnostic Strategy for Malignant Mesothelioma , 2021, Diagnostics.

[17]  G. Reifenberger,et al.  The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. , 2021, Neuro-oncology.

[18]  Marek Kretowski,et al.  Accelerated evolutionary induction of heterogeneous decision trees for gene expression-based classification , 2021, GECCO.

[19]  David L. Roberts,et al.  Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics. , 2021, Journal of proteome research.

[20]  Daniel M. Rotroff,et al.  Salivary metabolites are promising non‐invasive biomarkers of hepatocellular carcinoma and chronic liver disease , 2021, Liver cancer international.

[21]  R. Gottlieb,et al.  Elevated Asparagine Biosynthesis Drives Brain Tumor Stem Cell Metabolic Plasticity and Resistance to Oxidative Stress , 2021, Molecular Cancer Research.

[22]  G. de Castro,et al.  Serum Creatinine as a Potential Biomarker of Skeletal Muscle Atrophy in Non-small Cell Lung Cancer Patients , 2021, Frontiers in Physiology.

[23]  Zhongqiu Liu,et al.  GC-MS-based metabolomics reveals new biomarkers to assist the differentiation of prostate cancer and benign prostatic hyperplasia. , 2021, Clinica chimica acta; international journal of clinical chemistry.

[24]  Xiao-mei Zhang,et al.  Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data , 2021, Current Medical Science.

[25]  Xiaojian Zhang,et al.  Integrative Analysis of Metabolomic and Transcriptomic Data Reveals Metabolic Alterations in Glioma Patients. , 2021, Journal of proteome research.

[26]  T. Cloughesy,et al.  Targeting glioblastoma signaling and metabolism with a re-purposed brain-penetrant drug , 2021, bioRxiv.

[27]  E. V. Gaisler,et al.  Correlation of Metabolic Profiles of Plasma and Cerebrospinal Fluid of High-Grade Glioma Patients , 2021, Metabolites.

[28]  W. Papierz,et al.  Plasma amino acids indicate glioblastoma with ATRX loss , 2021, Amino Acids.

[29]  Guowang Xu,et al.  Metabolic Alterations Related to Glioma Grading Based on Metabolomics and Lipidomics Analyses , 2020, Metabolites.

[30]  Hu-dan Pan,et al.  Early lung cancer diagnostic biomarker discovery by machine learning methods , 2020, Translational oncology.

[31]  M. Loda,et al.  Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum , 2020, Molecular Cancer Research.

[32]  Eden L. Romm,et al.  Finding distinctions between oral cancer and periodontitis using saliva metabolites and machine learning. , 2020, Oral diseases.

[33]  Yunwen Tao,et al.  Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma , 2020, Nature Communications.

[34]  G. Milano,et al.  Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer , 2020, Computational and structural biotechnology journal.

[35]  A. J. Vanisree,et al.  METABOLIC VARIATIONS AMONG LOW GRADE AND HIGH GRADE GLIOMAS - PROFILING BY 1H NMR SPECTROSCOPY. , 2020, Journal of proteome research.

[36]  Yuanting Zheng,et al.  Both IDO1 and TDO contribute to the malignancy of gliomas via the Kyn–AhR–AQP4 signaling pathway , 2020, Signal Transduction and Targeted Therapy.

[37]  Nicola Zamboni,et al.  Targeting glioma-initiating cells via the tyrosine metabolic pathway. , 2020, Journal of neurosurgery.

[38]  A. Mangoni,et al.  Inhibition of Dimethylarginine Dimethylaminohydrolase (DDAH) Enzymes as an Emerging Therapeutic Strategy to Target Angiogenesis and Vasculogenic Mimicry in Cancer , 2020, Frontiers in Oncology.

[39]  I. Namer,et al.  Metabolomic profile of aggressive meningiomas by using high-resolution magic angle spinning nuclear magnetic resonance. , 2020, Journal of proteome research.

[40]  S. Pitson,et al.  Targeting the Sphingolipid System as a Therapeutic Direction for Glioblastoma , 2020, Cancers.

[41]  G. Pavesi,et al.  A metabolomic data fusion approach to support gliomas grading , 2019, NMR in biomedicine.

[42]  Kevin M. Mendez,et al.  A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification , 2019, Metabolomics.

[43]  Mei-Ling Cheng,et al.  Metabolomic biomarkers in cervicovaginal fluid for detecting endometrial cancer through nuclear magnetic resonance spectroscopy , 2019, Metabolomics.

[44]  H. Baek,et al.  Metabolic profiling of human gliomas assessed with NMR , 2019, Journal of Clinical Neuroscience.

[45]  P. Račay,et al.  Metabolomic profiling of blood plasma in patients with primary brain tumours: Basal plasma metabolites correlated with tumour grade and plasma biomarker analysis predicts feasibility of the successful statistical discrimination from healthy subjects – a preliminary study , 2019, IUBMB life.

[46]  M. R. Melo-Júnior,et al.  Osteopenia-osteoporosis discrimination in postmenopausal women by 1H NMR-based metabonomics , 2019, PloS one.

[47]  B. Lim,et al.  Methionine is a metabolic dependency of tumor-initiating cells , 2019, Nature Medicine.

[48]  Xin Lu,et al.  Integrated Metabolomics and Lipidomics Analyses Reveal Metabolic Reprogramming in Human Glioma with IDH1 Mutation. , 2018, Journal of proteome research.

[49]  R. Shamir,et al.  Multi-omic and multi-view clustering algorithms: review and cancer benchmark , 2018, Nucleic acids research.

[50]  H. Jia,et al.  Glycerophosphatidylcholine PC(36:1) absence and 3′-phosphoadenylate (pAp) accumulation are hallmarks of the human glioma metabolome , 2018, Scientific Reports.

[51]  C. James,et al.  An overview of meningiomas. , 2018, Future oncology.

[52]  R. Shamir,et al.  Multi-omic and multi-view clustering algorithms: review and cancer benchmark , 2018, bioRxiv.

[53]  Fadhl M Alakwaa,et al.  Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data , 2017, bioRxiv.

[54]  A. Brenner,et al.  Metabolomic signature of brain cancer , 2017, Molecular carcinogenesis.

[55]  A. Colquhoun Cell biology-metabolic crosstalk in glioma. , 2017, The international journal of biochemistry & cell biology.

[56]  G. Reifenberger,et al.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.

[57]  Xifeng Wu,et al.  Metabolomics profiling in plasma samples from glioma patients correlates with tumor phenotypes , 2016, Oncotarget.

[58]  N. S. Yarla,et al.  Phospholipase A2: A Potential Therapeutic Target in Inflammation and Cancer (In silico, In vitro, In vivo and Clinical Approach) , 2015 .

[59]  Alex Alves Freitas,et al.  Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets , 2014, IEEE Transactions on Evolutionary Computation.

[60]  Vijay Kotu,et al.  Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner , 2014 .

[61]  A. Chinnaiyan,et al.  Delineating metabolic signatures of head and neck squamous cell carcinoma: phospholipase A2, a potential therapeutic target. , 2012, The international journal of biochemistry & cell biology.

[62]  Peter Canoll,et al.  The cellular origin for malignant glioma and prospects for clinical advancements , 2012, Expert review of molecular diagnostics.

[63]  D. Monleón,et al.  Metabolic aggressiveness in benign meningiomas with chromosomal instabilities. , 2010, Cancer research.

[64]  Joseph Wiemels,et al.  Epidemiology and etiology of meningioma , 2010, Journal of Neuro-Oncology.

[65]  F. Howe,et al.  Taurine: a potential marker of apoptosis in gliomas , 2009, British Journal of Cancer.

[66]  David Bensimon,et al.  Some nonlinear challenges in biology , 2008 .

[67]  F. Fazzino,et al.  Taurine as a Micronutrient in Development and Regeneration of the Central Nervous System , 2001, Nutritional neuroscience.

[68]  M. Wyss,et al.  Creatine and creatinine metabolism. , 2000, Physiological reviews.

[69]  Y. Kinoshita,et al.  Absolute concentrations of metabolites in human brain tumors using in vitro proton magnetic resonance spectroscopy , 1997, NMR in biomedicine.

[70]  Jack Ngarambe,et al.  Recent advances in black box and white-box models for urban heat island prediction: Implications of fusing the two methods , 2022, Renewable and Sustainable Energy Reviews.

[71]  A. Krętowski,et al.  A review of gliomas-related proteins. Characteristics of potential biomarkers. , 2021, American journal of cancer research.

[72]  K. Chou,et al.  Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks. , 2019, Genomics.

[73]  [World Health Organization classification of tumours of the central nervous system: a summary]. , 2016, Zhonghua bing li xue za zhi = Chinese journal of pathology.

[74]  M. Walid Prognostic factors for long-term survival after glioblastoma. , 2008, The Permanente journal.

[75]  J. Sneep,et al.  With a summary , 1945 .