Literature DB >> 32669685

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases.

Monika A Myszczynska1, Poojitha N Ojamies2, Alix M B Lacoste3, Daniel Neil2, Amir Saffari2, Richard Mead1, Guillaume M Hautbergue1, Joanna D Holbrook2, Laura Ferraiuolo4.   

Abstract

Globally, there is a huge unmet need for effective treatments for neurodegenerative diseases. The complexity of the molecular mechanisms underlying neuronal degeneration and the heterogeneity of the patient population present massive challenges to the development of early diagnostic tools and effective treatments for these diseases. Machine learning, a subfield of artificial intelligence, is enabling scientists, clinicians and patients to address some of these challenges. In this Review, we discuss how machine learning can aid early diagnosis and interpretation of medical images as well as the discovery and development of new therapies. A unifying theme of the different applications of machine learning is the integration of multiple high-dimensional sources of data, which all provide a different view on disease, and the automated derivation of actionable insights.

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Year:  2020        PMID: 32669685     DOI: 10.1038/s41582-020-0377-8

Source DB:  PubMed          Journal:  Nat Rev Neurol        ISSN: 1759-4758            Impact factor:   42.937


  102 in total

Review 1.  Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

Authors:  S Agatonovic-Kustrin; R Beresford
Journal:  J Pharm Biomed Anal       Date:  2000-06       Impact factor: 3.935

2.  Should we be afraid of medical AI?

Authors:  Ezio Di Nucci
Journal:  J Med Ethics       Date:  2019-06-21       Impact factor: 2.903

3.  No we shouldn't be afraid of medical AI; it involves risks and opportunities.

Authors:  Rosalind J McDougall
Journal:  J Med Ethics       Date:  2019-06-21       Impact factor: 2.903

Review 4.  Artificial intelligence in healthcare.

Authors:  Kun-Hsing Yu; Andrew L Beam; Isaac S Kohane
Journal:  Nat Biomed Eng       Date:  2018-10-10       Impact factor: 25.671

5.  Human neuroimaging as a "Big Data" science.

Authors:  John Darrell Van Horn; Arthur W Toga
Journal:  Brain Imaging Behav       Date:  2014-06       Impact factor: 3.978

6.  Finding the missing link for big biomedical data.

Authors:  Griffin M Weber; Kenneth D Mandl; Isaac S Kohane
Journal:  JAMA       Date:  2014-06-25       Impact factor: 56.272

7.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.

Authors:  M D Ritchie; L W Hahn; N Roodi; L R Bailey; W D Dupont; F F Parl; J H Moore
Journal:  Am J Hum Genet       Date:  2001-06-11       Impact factor: 11.025

8.  Computer knows best? The need for value-flexibility in medical AI.

Authors:  Rosalind J McDougall
Journal:  J Med Ethics       Date:  2018-11-22       Impact factor: 2.903

Review 9.  Reprogramming neurodegeneration in the big data era.

Authors:  Lujia Zhou; Patrik Verstreken
Journal:  Curr Opin Neurobiol       Date:  2018-01-10       Impact factor: 6.627

10.  Big biomedical data and cardiovascular disease research: opportunities and challenges.

Authors:  Spiros C Denaxas; Katherine I Morley
Journal:  Eur Heart J Qual Care Clin Outcomes       Date:  2015-07-01
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  32 in total

Review 1.  Microbial source tracking using metagenomics and other new technologies.

Authors:  Shahbaz Raza; Jungman Kim; Michael J Sadowsky; Tatsuya Unno
Journal:  J Microbiol       Date:  2021-02-10       Impact factor: 3.422

Review 2.  A guide to machine learning for biologists.

Authors:  Joe G Greener; Shaun M Kandathil; Lewis Moffat; David T Jones
Journal:  Nat Rev Mol Cell Biol       Date:  2021-09-13       Impact factor: 94.444

3.  Improving the Diagnostic Potential of Extracellular miRNAs Coupled to Multiomics Data by Exploiting the Power of Artificial Intelligence.

Authors:  Alessandro Paolini; Antonella Baldassarre; Stefania Paola Bruno; Cristina Felli; Chantal Muzi; Sara Ahmadi Badi; Seyed Davar Siadat; Meysam Sarshar; Andrea Masotti
Journal:  Front Microbiol       Date:  2022-06-09       Impact factor: 6.064

4.  Protein-protein interaction and non-interaction predictions using gene sequence natural vector.

Authors:  Nan Zhao; Maji Zhuo; Kun Tian; Xinqi Gong
Journal:  Commun Biol       Date:  2022-07-02

5.  Machine learning based analysis of stroke lesions on mouse tissue sections.

Authors:  Gerasimos Damigos; Evangelia I Zacharaki; Nefeli Zerva; Angelos Pavlopoulos; Konstantina Chatzikyrkou; Argyro Koumenti; Konstantinos Moustakas; Constantinos Pantos; Iordanis Mourouzis; Athanasios Lourbopoulos
Journal:  J Cereb Blood Flow Metab       Date:  2022-02-25       Impact factor: 6.960

6.  Connected speech markers of amyloid burden in primary progressive aphasia.

Authors:  Antoine Slegers; Geneviève Chafouleas; Maxime Montembeault; Christophe Bedetti; Ariane E Welch; Gil D Rabinovici; Philippe Langlais; Maria L Gorno-Tempini; Simona M Brambati
Journal:  Cortex       Date:  2021-10-07       Impact factor: 4.644

7.  Exploring Motor Neuron Diseases Using iPSC Platforms.

Authors:  Alexandra E Johns; Nicholas J Maragakis
Journal:  Stem Cells       Date:  2022-03-03       Impact factor: 5.845

Review 8.  Machine learning for sperm selection.

Authors:  Jae Bem You; Christopher McCallum; Yihe Wang; Jason Riordon; Reza Nosrati; David Sinton
Journal:  Nat Rev Urol       Date:  2021-05-17       Impact factor: 14.432

Review 9.  Machine Learning Use for Prognostic Purposes in Multiple Sclerosis.

Authors:  Ruggiero Seccia; Silvia Romano; Marco Salvetti; Andrea Crisanti; Laura Palagi; Francesca Grassi
Journal:  Life (Basel)       Date:  2021-02-05

Review 10.  The human connectome in Alzheimer disease - relationship to biomarkers and genetics.

Authors:  Meichen Yu; Olaf Sporns; Andrew J Saykin
Journal:  Nat Rev Neurol       Date:  2021-07-20       Impact factor: 44.711

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