RAMAN-BASED MACHINE LEARNING PLATFORM REVEALS UNIQUE METABOLIC DIFFERENCES BETWEEN IDHMUT AND IDHWT GLIOMA

被引：0
|
作者：
Lita, Adrian [1 ]
Sjoberg, Joel [2 ]
Pacioianu, David [3 ]
Celiku, Orieta [1 ]
Dowdy, Tyrone [1 ]
Paun, Andrei [3 ,4 ]
Gilbert, Mark R. [1 ]
Noushmehr, Houtan [5 ]
Petre, Ion [2 ]
Larion, Mioara [1 ]
机构：
[1] NCI, NIH, Neurooncol Branch, Bethesda, MD USA
[2] Univ Turku, Dept Math & Stat, Turku, Finland
[3] Univ Bucharest, Fac Math & Comp Sci, Bucharest, Romania
[4] Natl Inst Res & Dev Biol Sci, Dept Bioinformat, Bucharest, Romania
[5] Henry Ford Hlth Syst, Dept Neurosurg, Detroit, MI USA
来源：
NEURO-ONCOLOGY | 2024年 / 26卷
关键词：
D O I：
10.1093/neuonc/noae165.0754
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
PATH-55
引用
收藏
页数：1
相关论文
共 35 条
  • [1] Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma
    Lita, Adrian
    Sjoeberg, Joel
    Pacioianu, David
    Siminea, Nicoleta
    Celiku, Orieta
    Dowdy, Tyrone
    Paun, Andrei
    Gilbert, Mark R.
    Noushmehr, Houtan
    Petre, Ion
    Larion, Mioara
    NEURO-ONCOLOGY, 2024, 26 (11) : 1994 - 2009
  • [2] Chemical changes in childhood obesity blood as a marker of the disease. A Raman-based machine learning study
    Guleken, Zozan
    Ceylan, Zeynep
    Cecen, Serpil
    Jakubczyk, Dorota
    Jakubczyk, Pawel
    Depciuch, Joanna
    JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS, 2023, 233
  • [3] Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment
    Garbulowski, Mateusz
    Smolinska, Karolina
    Cabuk, Ugur
    Yones, Sara A.
    Celli, Ludovica
    Yaz, Esma Nur
    Barrenas, Fredrik
    Diamanti, Klev
    Wadelius, Claes
    Komorowski, Jan
    CANCERS, 2022, 14 (04)
  • [4] Sequence-Based Machine Learning Reveals 3D Genome Differences between Bonobos and Chimpanzees
    Brand, Colin M.
    Kuang, Shuzhen
    Gilbertson, Erin N.
    McArthur, Evonne
    Pollard, Katherine S.
    Webster, Timothy H.
    Capra, John A.
    GENOME BIOLOGY AND EVOLUTION, 2024, 16 (11):
  • [5] System transferability of Raman-based oesophageal tissue classification using modern machine learning to support multi-centre clinical diagnostics
    Nathan Blake
    Riana Gaifulina
    Martin Isabelle
    Jennifer Dorney
    Manuel Rodriguez-Justo
    Katherine Lau
    Stéphanie Ohrel
    Gavin Lloyd
    Neil Shepherd
    Aaran Lewis
    Catherine A. Kendall
    Nick Stone
    Ian Bell
    Geraint Thomas
    BJC Reports, 2 (1):
  • [6] Meta-Analysis of the Efficacy of Raman Spectroscopy and Machine-Learning-Based Identification of Glioma Tissue
    Goff, Nicolas K.
    Ashby, Landon
    Ashour, Ramsey
    WORLD NEUROSURGERY, 2024, 189 : 26 - 32
  • [7] Metabolic Phenotyping of Adipose-Derived Stem Cells Reveals a Unique Signature and Intrinsic Differences between Fat Pads
    Lefevre, Camille
    Panthu, Baptiste
    Naville, Danielle
    Guibert, Sylvie
    Pinteur, Claudie
    Elena-Herrmann, Benedicte
    Vidal, Hubert
    Rautureau, Gilles J. P.
    Mey, Anne
    STEM CELLS INTERNATIONAL, 2019, 2019
  • [8] Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction
    Kohls, Gregor
    Elster, Erik M.
    Tino, Peter
    Fairchild, Graeme
    Stadler, Christina
    Popma, Arne
    Freitag, Christine M.
    De Brito, Stephane A.
    Konrad, Kerstin
    Pauli, Ruth
    BMC PSYCHIATRY, 2025, 25 (01)
  • [9] Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures
    Zheng, Ran
    Su, Rui
    Fan, Yusi
    Xing, Fan
    Huang, Keke
    Yan, Fei
    Chen, Huanwen
    Liu, Botong
    Fang, Laiping
    Du, Yechao
    Zhou, Fengfeng
    Wang, Daguang
    Feng, Shouhua
    ANALYTICAL CHEMISTRY, 2024, 96 (21) : 8772 - 8781
  • [10] Machine learning-based metabolic imaging: A bridge between in vitro models and clinical applications
    Bianchetti, G.
    NUOVO CIMENTO C-COLLOQUIA AND COMMUNICATIONS IN PHYSICS, 2023, 46 (03):
1234