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HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis

Horacio González-Vélez, Mariola Mier, Margarida Julià-Sapé, Theodoros N. Arvanitis, Juan M. García-Gómez, Montserrat Robles, Paul H. Lewis, Srinandan Dasmahapatra, David Dupplaw, Andrew Peet, Carles Arús, Bernardo Celda (+2 others)
2007 Applied intelligence (Boston)  
Abstract We present an agent-based distributed decision support system for the diagnosis and prognosis of brain tumours developed by the HealthAgents project.  ...  HealthAgents is a European Union funded research project, which aims to enhance the classification of brain tumours using such a decision support system based on intelligent agents to securely connect  ...  , and Jan Luts and Javier Vicente for their comments to the machine learning section.  ... 
doi:10.1007/s10489-007-0085-8 fatcat:kefcix5l7zcy7kahfujyjxeq4m

Patient Similarity Networks for Precision Medicine

Shraddha Pai, Gary D. Bader
2018 Journal of Molecular Biology  
We review new methods based on patient similarity networks, including Similarity Network Fusion for patient clustering and netDx for patient classification.  ...  Traditional machine-learning approaches excel at performance, but often have limited interpretability.  ...  National Institutes of Health, National Center for Research Resources grant number P41 GM103504). We thank Ruth Isserlin for providing prepared data for the ependymoma classification example.  ... 
doi:10.1016/j.jmb.2018.05.037 pmid:29860027 pmcid:PMC6097926 fatcat:v6eucwccdjabthfefnzfddtvai

Radiomics: from qualitative to quantitative imaging

William Rogers, Sithin Thulasi Seetha, Turkey A. G. Refaee, Relinde I. Y. Lieverse, Renée W. Y. Granzier, Abdalla Ibrahim, Simon A. Keek, Sebastian Sanduleanu, Sergey P. Primakov, Manon P. L. Beuque, Damiënne Marcus, Alexander M. A. van der Wiel (+5 others)
2020 British Journal of Radiology  
for diagnosis, theragnosis, decision support, and monitoring.  ...  Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs.  ...  Brain tumours are usually graded based on clinical or pathological analysis to define their malignancy.  ... 
doi:10.1259/bjr.20190948 pmid:32101448 fatcat:mnhaur7dyrhanio63v6kdbdthm

From Personalized Medicine to Precision Psychiatry?

Eva Češková, Petr Šilhán
2021 Neuropsychiatric Disease and Treatment  
its analysis by the means of artificial intelligence and machine learning.  ...  Precision medicine bases decisions on quantifiable indicators available thanks to the tremendous progress in science and technology facilitating the acquisition, processing and analysis of huge amounts  ...  Its subspecialty -machine learning -uses a system of algorithms that analyses data based on the experience obtained from structured training data, learns from it and answers predefined questions.  ... 
doi:10.2147/ndt.s337814 pmid:34934319 pmcid:PMC8684413 fatcat:pdvfu7jezzbkhbhspd4pmt7yga

netDx: Interpretable patient classification using integrated patient similarity networks [article]

Shraddha Pai, Shirley Hui, Ruth Isserlin, Muhammad A Shah, Hussam Kaka, Gary D Bader
2016 bioRxiv   pre-print
A clinical predictor based on genomic data needs to be easily interpretable to drive hypothesis-driven research into new treatments.  ...  As a machine learning method, netDx demonstrates consistently excellent performance in a cancer survival benchmark across four cancer types by integrating up to six genomic and clinical data types.  ...  We also thank Han Liang for discussion on implementation details for the machine learning used in Yuan et al. (2014). This work was supported by a Canadian Institutes of Health Research award  ... 
doi:10.1101/084418 fatcat:qhfqydr53nd7zjqudn4ee6ccf4

Brain Tumour Classification by Machine Learning Applications with Selected Biological Features: Towards A Newer Diagnostic Regime

Krishnendu Ghosh, Immunobiology Laboratory, Department of Zoology, Panihati Mahavidyalaya (West Bengal State University), Barasat Road, Sodepur, West Bengal, India
2020 Journal of Analytical Oncology  
In the present study low-grade brain tumour patient samples of three different histopathological types have been trained through machine learning technique using selected features for its classification  ...  These parameters when trained with proper machine learning protocol through extraction of underling features and represented in a 2D perceivable space are found capable of distinguishing the tumour types  ...  This helps in interpretation of large and complex data sets derived from genomic, proteomic, metabolomic and microbiome analysis and also become a handful tool for systems biology approach [8, 9] .  ... 
doi:10.30683/1927-7229.2020.09.02 fatcat:o5mvp2jlgjfqlh7yq7bp2n63y4

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

Kelechi Njoku, Caroline J. Sutton, Anthony D. Whetton, Emma J. Crosbie
2020 Metabolites  
Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms.  ...  Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.  ...  Advances in the use of artificial intelligence and machine learning techniques to combine metabolic signals from multiple studies have potential to enable the generation of a robust metabolomic biomarker  ... 
doi:10.3390/metabo10080314 pmid:32751940 pmcid:PMC7463916 fatcat:u234bxhjrnfzdpocms5zmgxqxm

Towards precision medicine: from quantitative imaging to radiomics

U. Rajendra Acharya, Yuki Hagiwara, Vidya K. Sudarshan, Wai Yee Chan, Kwan Hoong Ng
2018 Journal of Zhejiang University SCIENCE B  
Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes.  ...  However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required  ...  Classifier models are machine learning algorithms for making accurate predictions based on the training dataset and on the features extracted from radiological images.  ... 
doi:10.1631/jzus.b1700260 pmid:29308604 pmcid:PMC5802973 fatcat:7imi5eonkvaibp2llzsuxhmy2y

Metabolomics - the stethoscope for the 21st century

Hutan Ashrafian, Viknesh Sounderajah, Robert Glen, Timothy Ebbels, Benjamin J. Blaise, Dipak Kalra, Kim Kultima, Ola Spjuth, Leonardo Tenori, Reza Salek, Namrata Kale, Kenneth Haug (+7 others)
2020 Medical Principles and Practice  
We searched PubMed, Medline and Embase for primary and secondary research articles regarding clinical applications of metabolomics.  ...  Metabolic profiling can be performed using mass spectrometry and NMR based techniques using a variety of biological samples.  ...  in the development of new algorithms for metabolite identification [74] and prediction, predominantly through machine learning techniques [75, 76] .  ... 
doi:10.1159/000513545 pmid:33271569 pmcid:PMC8436726 fatcat:epd2weiif5bdbhuz23aenccury

A Review on Machine Learning and Deep Learning Techniques Applied to Liquid Biopsy [chapter]

Arets Paeglis, Boriss Strumfs, Dzeina Mezale, Ilze Fridrihsone
2018 Liquid Biopsy [Working Title]  
For more than a decade, machine learning (ML) and deep learning (DL) techniques have been a mainstay in the toolset for the analysis of large amounts of weakly correlated or high-dimensional data.  ...  As new technologies for detecting and measuring biochemical markers from bodily fluid samples (e.g., microfluidics and labs-on-a-chip) revolutionise the industry of diagnostics and precision medicine,  ...  ) [47] as a tool for accurate tumour diagnosis, both within a single class and across six different tumour classes.  ... 
doi:10.5772/intechopen.79404 fatcat:ydcnwek7argurcs67lrrwp7x5e

Translational cancer research towards Thailand 4.0

Kanlayanee Sawanyawisuth, Chaisiri Wongkham, Chawalit Pairojkul, Sopit Wongkham
2018 ScienceAsia  
High specificity and sensitivity of tumour markers in serum or secretory fluids are helpful for diagnosis of cancer before advanced, high cost, or invasive diagnoses are implemented.  ...  Histoculture drug response assays or in vitro chemo-sensitivity assays, genomic profiling of tumours by next-generation sequencing, drug repositioning for cancer, and chimeric antigen receptor T-cell therapy  ...  Prediction of drug-disease responses could be obtained using bio-informatic technologies, machine learning-based models, biological network analysis and text-mining research.  ... 
doi:10.2306/scienceasia1513-1874.2018.44s.011 fatcat:2j5brxzvnrfmvmigs7q6maiiqy

Computational Methods for the Discovery of Metabolic Markers of Complex Traits

Michael Lee, Ting Hu
2019 Metabolites  
This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers.  ...  budding with high potential for utility in metabolomics research.  ...  Research on designing ANN structures that are more amenable for mechanistic explanations is thus needed for a better utilization of this powerful and advanced machine-learning algorithm.  ... 
doi:10.3390/metabo9040066 pmid:30987289 pmcid:PMC6523328 fatcat:iluqijb2pvgytkdc2m4gjmdtia

Recent trends & applications of big data science in chronobiology

Harshit Kumar, Manjit Panigrahi, Kaiho Kaisa, Divya Rajawat, KA Saravanan, Triveni Dutt, Bharat Bhushan
2020 Journal of Entomology and Zoology Studies  
Approaches using big data mining and machine learning (ML) along with other wellestablished methods of genomics, molecular biology and biotechnology may be implemented with researches in chronobiology.  ...  On the other hand, data science unaided will not reject the need for a considerate understanding of the field of research, nor can such approaches substitute the need for researchers and analysts.  ...  We would also like to acknowledge Director, Indian Veterinary Research Institute for providing all kinds of facilities needed for this study.  ... 
doi:10.22271/j.ento.2020.v8.i4ai.7453 fatcat:neftulkaczgmzntfetlp5eiy4a

Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine

Q. Tian, N. D. Price, L. Hood
2012 Journal of Internal Medicine  
Tian Q, Price ND, Hood L (Institute for Systems  ...  , an NIH Howard Temin Pathway to Independence Award in Cancer Research (R00CA126184), and a Roy J.  ...  Acknowledgements We gratefully acknowledge funding from the Grand Duchy of Luxembourg, NIH ⁄ NCI NanoSystems Biology Cancer Center (U54 CA151819A), NIH ⁄ NIGMS Center for Systems Biology (P50 GM076547)  ... 
doi:10.1111/j.1365-2796.2011.02498.x pmid:22142401 pmcid:PMC3978383 fatcat:ldxftliqcrb47nyijoividqp7m

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes

M. Julià-Sapé, J. R. Griffiths, R. A. Tate, F. A. Howe, D. Acosta, G. Postma, J. Underwood, C. Majós, C. Arús
2015 NMR in Biomedicine  
Marinette van der Graaf, Chantal Remy, Arend Heerschap and Des Watson for their comments which helped to improve this manuscript.  ...  OA) and the emerging ones based on genetic markers (86, 87) ; Less common tumour types, e.g. hemangiopericytoma (88) ; Pediatric brain tumours (42, 89, 90) .  ...  The following lessons were learned, despite being based on a limited number of patients: 1.  ... 
doi:10.1002/nbm.3483 pmid:26915795 fatcat:aruzqjok7zb2ldkqp3mexanyye
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