|
1
|
Huang WJ, Chen WW and Zhang X: Multiple
sclerosis: Pathology, diagnosis and treatments. Exp Ther Med.
13:3163–3166. 2017.PubMed/NCBI View Article : Google Scholar
|
|
2
|
GBD 2016 Multiple Sclerosis Collaborators:
Global, regional, and national burden of multiple sclerosis
1990-2016. A systematic analysis for the global burden of disease
study 2016. Lancet Neurol. 18:269–285. 2019.
|
|
3
|
Cruz-Orengo L, Daniels BP, Dorsey D, Basak
SA, Grajales-Reyes JG, McCandless EE, Piccio L, Schmidt RE, Cross
AH, Crosby SD and Klein RS: Enhanced sphingosine-1-phosphate
receptor 2 expression underlies female CNS autoimmunity
susceptibility. J Clin Invest. 124:2571–2584. 2014.PubMed/NCBI View
Article : Google Scholar
|
|
4
|
Berger B, Daniels NM and Yu YW:
Computational biology in the 21st century: Scaling with compressive
algorithms. Commun ACM. 59:72–80. 2016.PubMed/NCBI View
Article : Google Scholar
|
|
5
|
Wang MD: In the spotlight: Bioinformatics,
computational biology and systems biology. IEEE Rev Biomed Eng.
4:3–5. 2011.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Gu RX and Huang Z: Development and
application of computational methods in biology and medicine. Curr
MedChem. 26:7534–7536. 2020.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Vlachakis D, Fakourelis P, Megalooikonomou
V, Makris C and Kossida S: DrugOn: A fully integrated pharmacophore
modeling and structure optimization toolkit. PeerJ.
3(e725)2015.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Vlachakis D, Papakonstantinou E, Sagar R,
Bacopoulou F, Exarchos T, Kourouthanassis P, Karyotis V, Vlamos P,
Lyketsos C, Avramopoulos D and Mahairaki V: Improving the utility
of polygenic risk scores as a biomarker for Alzheimer's disease.
Cells. 10(1627)2021.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Shakhovska N, Fedushko S, Greguš M,
Melnykova N, Shvorob I and Syerov Y: Big data analysis in
development of personalized medical system. Procedia Comput Sci.
160:229–234. 2019.
|
|
10
|
Moreno-Indias I, Lahti L, Nedyalkova M,
Elbere I, Roshchupkin G, Adilovic M, Aydemir O, Bakir-Gungor B,
Santa Pau EC, D'Elia D, et al: Statistical and machine learning
techniques in human microbiome studies: Contemporary challenges and
solutions. Front Microbiol. 12(635781)2021.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Goetz LH and Schork NJ: Personalized
medicine: Motivation, challenges, and progress. Fertil Steril.
109:952–963. 2018.PubMed/NCBI View Article : Google Scholar
|
|
12
|
Mathur S and Sutton J: Personalized
medicine could transform healthcare. Biomed Rep. 7:3–5.
2017.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Titus HE, Chen Y, Podojil JR, Robinson AP,
Balabanov R, Popko B and Miller SD: Pre-clinical and clinical
implications of ‘inside-out’ vs ‘outside-in’ paradigms in multiple
sclerosis etiopathogenesis. Front Cell Neurosci.
14(599717)2020.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Dulamea AO: Role of oligodendrocyte
dysfunction in demyelination, remyelination and neurodegeneration
in multiple sclerosis. Adv Exp Med Biol. 958:91–127.
2017.PubMed/NCBI View Article : Google Scholar
|
|
15
|
Stys P and Tsutsui S: Recent advances in
understanding multiple sclerosis. F1000Res 8: F1000 Faculty
Rev-2100, 2019.
|
|
16
|
Tillery EE, Clements JN and Howard Z:
What's new in multiple sclerosis? Ment Health Clin. 7:213–220.
2018.PubMed/NCBI View Article : Google Scholar
|
|
17
|
Dennison L, Brown M, Kirby S and Galea I:
Do people with multiple sclerosis want to know their prognosis? A
UK nationwide study. PLoS One. 13(e0193407)2018.PubMed/NCBI View Article : Google Scholar
|
|
18
|
Bsteh G, Ehling R, Lutterotti A, Hegen H,
Di Pauli F, Auer M, Deisenhammer F, Reindl M and Berger T: Long
term clinical prognostic factors in relapsing-remitting multiple
sclerosis: Insights from a 10-year observational study. PLoS One.
11(e0158978)2016.PubMed/NCBI View Article : Google Scholar
|
|
19
|
Bosch GJ, Bolk J, Hart BA and Laman JD:
Multiple sclerosis is linked to MAPKERK overactivity in
microglia. J Mol Med (Berl). 99:1033–1042. 2021.PubMed/NCBI View Article : Google Scholar
|
|
20
|
Garg N and Smith TW: An update on
immunopathogenesis, diagnosis, and treatment of multiple sclerosis.
Brain Behav. 5(e00362)2015.PubMed/NCBI View
Article : Google Scholar
|
|
21
|
Canto E and Oksenberg JR: Multiple
sclerosis genetics. Mult Scler. 24:75–79. 2018.PubMed/NCBI View Article : Google Scholar
|
|
22
|
Guan Y, Jakimovski D, Ramanathan M,
Weinstock-Guttman B and Zivadinov R: The role of Epstein-Barr virus
in multiple sclerosis: From molecular pathophysiology to in vivo
imaging. Neural Regen Res. 14:373–386. 2019.PubMed/NCBI View Article : Google Scholar
|
|
23
|
Fernández-Menéndez S, Fernández-Morán M,
Fernández-Vega I, Pérez-Álvarez A and Villafani-Echazú J:
Epstein-Barr virus and multiple sclerosis. From evidence to
therapeutic strategies. J Neurol Sci. 361:213–219. 2016.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Ascherio A: Environmental factors in
multiple sclerosis. Expert Rev Neurother. 13 (Suppl 12):S3–S9.
2013.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Zhang P, Wang R, Li Z, Wang Y, Gao C, Lv
X, Song Y and Li B: The risk of smoking on multiple sclerosis: A
meta-analysis based on 20,626 cases from case-control and cohort
studies. PeerJ. 4(e1797)2016.PubMed/NCBI View Article : Google Scholar
|
|
26
|
Hunter SF: Overview and diagnosis of
multiple sclerosis. Am J Manag Care. 22 (Suppl 6):s141–s150.
2016.PubMed/NCBI
|
|
27
|
Ford H: Clinical presentation and
diagnosis of multiple sclerosis. Clin Med (Lond). 20:380–383.
2020.PubMed/NCBI View Article : Google Scholar
|
|
28
|
Ziemssen T, Akgün K and Brück W: Molecular
biomarkers in multiple sclerosis. J Neuroinflammation.
16(272)2019.PubMed/NCBI View Article : Google Scholar
|
|
29
|
Paul A, Comabella M and Gandhi R:
Biomarkers in multiple sclerosis. Cold Spring Harb Perspect Med.
9(a029058)2018.PubMed/NCBI View Article : Google Scholar
|
|
30
|
Dennison L, McCloy Smith E, Bradbury K and
Galea I: How do people with multiple sclerosis experience
prognostic uncertainty and prognosis communication? A qualitative
study. PLoS One. 11(e0158982)2016.PubMed/NCBI View Article : Google Scholar
|
|
31
|
Traboulsee AL, Cornelisseª P,
Sandberg-Wollheim M, Uitdehaag BM, Kappos L, Jongen PJ,
Constantinescu CS, di Cantogno EV and Li DK: Prognostic factors for
long-term outcomes in relapsing-remitting multiple sclerosis. Mult
Scler J Exp Transl Clin. 2(2055217316666406)2016.PubMed/NCBI View Article : Google Scholar
|
|
32
|
Hauser SL and Cree BAC: Treatment of
multiple sclerosis: A review. Am J Med. 133:1380–1390.e2.
2020.PubMed/NCBI View Article : Google Scholar
|
|
33
|
Gajofatto A and Benedetti MD: Treatment
strategies for multiple sclerosis: When to start, when to change,
when to stop? World J Clin Cases. 3:545–555. 2015.PubMed/NCBI View Article : Google Scholar
|
|
34
|
Klotz L, Eschborn M, Lindner M, Liebmann
M, Herold M, Janoschka C, Torres Garrido B, Schulte-Mecklenbeck A,
Gross CC, Breuer J, et al: Teriflunomide treatment for multiple
sclerosis modulates T cell mitochondrial respiration with
affinity-dependent effects. Sci Transl Med.
11(eaao5563)2019.PubMed/NCBI View Article : Google Scholar
|
|
35
|
Naismith RT, Wundes A, Ziemssen T,
Jasinska E, Freedman MS, Lembo AJ, Selmaj K, Bidollari I, Chen H,
Hanna J, et al: Diroximel fumarate demonstrates an improved
gastrointestinal tolerability profile compared with dimethyl
fumarate in patients with relapsing-remitting multiple sclerosis:
Results from the randomized, double-blind, phase III EVOLVE-MS-2
study. CNS Drugs. 34:185–196. 2020.PubMed/NCBI View Article : Google Scholar
|
|
36
|
Montalban X, Arnold DL, Weber MS, Staikov
I, Piasecka-Stryczynska K, Willmer J, Martin EC, Dangond F, Syed S
and Wolinsky JS: Evobrutinib Phase 2 Study Group.
Placebo-controlled trial of an oral BTK inhibitor in multiple
sclerosis. N Engl J Med. 380:2406–2417. 2019.PubMed/NCBI View Article : Google Scholar
|
|
37
|
Torke S and Weber MS: Inhibition of
Bruton´s tyrosine kinase as a novel therapeutic approach in
multiple sclerosis. Expert Opin Investig Drugs. 29:1143–1150.
2020.PubMed/NCBI View Article : Google Scholar
|
|
38
|
Weber M, Harp C, Bremer M, Goodyear A,
Crawford J, Johnson A and Bar-Or A: Fenebrutinib demonstrates the
highest potency of bruton tyrosine kinase inhibitors (BTKis) in
phase 3 clinical development for multiple sclerosis (MS) (4437).
Neurology. 96 (Suppl 15)(S4437)2021.
|
|
39
|
Klistorner A and Barnett M: Remyelination
trials: Are we expecting the unexpected? Neurol Neuroimmunol
Neuroinflamm. 8(e1066)2021.PubMed/NCBI View Article : Google Scholar
|
|
40
|
Faraco G, Cavone L and Chiarugi A: The
therapeutic potential of HDAC inhibitors in the treatment of
multiple sclerosis. Mol Med. 17:442–447. 2011.PubMed/NCBI View Article : Google Scholar
|
|
41
|
Bae D, Lee JY, Ha N, Park J, Baek J, Suh
D, Lim HS, Ko SM, Kim T, Som Jeong D and Son WC: CKD-506: A novel
HDAC6-selective inhibitor that exerts therapeutic effects in a
rodent model of multiple sclerosis. Sci Rep.
11(14466)2021.PubMed/NCBI View Article : Google Scholar
|
|
42
|
Manzoni C, Kia DA, Vandrovcova J, Hardy J,
Wood NW, Lewis PA and Ferrari R: Genome, transcriptome and
proteome: The rise of omics data and their integration in
biomedical sciences. Brief Bioinform. 19:286–302. 2018.PubMed/NCBI View Article : Google Scholar
|
|
43
|
Li Y and Chen L: Big biological data:
Challenges and opportunities. Genomics Proteomics Bioinformatics.
12:187–189. 2014.PubMed/NCBI View Article : Google Scholar
|
|
44
|
Sousa SA, Leitão JH, Martins RC, Sanches
JM, Suri JS and Giorgetti A: Bioinformatics applications in life
sciences and technologies. Biomed Res Int.
2016(3603827)2016.PubMed/NCBI View Article : Google Scholar
|
|
45
|
Vlachakis D, Tsagrasoulis D,
Megalooikonomou V and Kossida S: Introducing drugster: A
comprehensive and fully integrated drug design, lead and structure
optimization toolkit. Bioinformatics. 29:126–128. 2013.PubMed/NCBI View Article : Google Scholar
|
|
46
|
Koumandou VL, Papageorgiou L, Picasi E,
Mantzouni D, Raftopoulou S, Ramm M, Papathanassopoulou A,
Hagidimitriou M, Cosmidis N and Vlachakis D: Genomic analysis of
the endosymbiotic bacterium candidatus erwinia dacicola provides
insights for the management of the olive pest Bactrocera oleae. J
Biotechnol. 280(S13)2018.
|
|
47
|
Rodin AS, Gogoshin G and Boerwinkle E:
Systems biology data analysis methodology in pharmacogenomics.
Pharmacogenomics. 12:1349–1360. 2011.PubMed/NCBI View Article : Google Scholar
|
|
48
|
Wierling C, Herwig R and Lehrach H:
Resources, standards and tools for systems biology. Brief Funct
Genomic Proteomic. 6:240–251. 2007.PubMed/NCBI View Article : Google Scholar
|
|
49
|
Hillmer RA: Systems biology for
biologists. PLoS Pathog. 11(e1004786)2015.PubMed/NCBI View Article : Google Scholar
|
|
50
|
Wood LB, Winslow AR and Strasser SD:
Systems biology of neurodegenerative diseases. Integr Biol (Camb).
7:758–775. 2015.PubMed/NCBI View Article : Google Scholar
|
|
51
|
Merelli I, Pérez-Sánchez H, Gesing S and
D'Agostino D: Managing, analysing, and integrating big data in
medical bioinformatics: Open problems and future perspectives.
Biomed Res Int. 2014(134023)2014.PubMed/NCBI View Article : Google Scholar
|
|
52
|
Rehman HU, Khan A and Habib U: Fog
computing for bioinformatics applications. Fog Computing,
pp529-546, 2020.
|
|
53
|
Amisha Malik P, Pathania M and Rathaur VK:
Overview of artificial intelligence in medicine. J Family Med Prim
Care. 8:2328–2331. 2019.PubMed/NCBI View Article : Google Scholar
|
|
54
|
Chakraborty I, Choudhury A and Banerjee
TS: Artificial intelligence in biological data. J Inform Tech Softw
Eng. 7:1–6. 2017.
|
|
55
|
Luo J, Wu M, Gopukumar D and Zhao Y: Big
data application in biomedical research and health care: A
literature review. Biomed Inform Insights. 8:1–10. 2016.PubMed/NCBI View Article : Google Scholar
|
|
56
|
Bohr A and Memarzadeh K: The rise of
artificial intelligence in healthcare applications. Artificial
Intelligence in Healthcare, pp25-60, 2020.
|
|
57
|
Barrett M, Boyne J, Brandts J, Brunner-La
Rocca HP, De Maesschalck L, De Wit K, Dixon L, Eurlings C,
Fitzsimons D, Golubnitschaja O, et al: Artificial intelligence
supported patient self-care in chronic heart failure: A paradigm
shift from reactive to predictive, preventive and personalised
care. EPMA J. 10:445–464. 2019.PubMed/NCBI View Article : Google Scholar
|
|
58
|
Tahri Sqalli M and Al-Thani D: On how
chronic conditions affect the patient-AI interaction: A literature
review. Healthcare (Basel). 8(313)2020.PubMed/NCBI View Article : Google Scholar
|
|
59
|
Khakpour A and Colomo-Palacios R:
Convergence of gamification and machine learning: A systematic
literature review. Technol Knowl Learn. 26:597–636. 2021.
|
|
60
|
Hulse JV, Khoshgoftaar TM and Napolitano
A: Experimental perspectives on learning from imbalanced data. In:
Proceedings of the 24th International Conference on Machine
Learning (ICML 2007). ACM, New York, pp935-942, 2007.
|
|
61
|
Richter AN and Khoshgoftaar TM: Sample
size determination for biomedical big data with limited labels.
Netw Model Anal Health Inform Bioinforma. 9(12)2020.
|
|
62
|
McShane LM and Polley MYC: Development of
omics-based clinical tests for prognosis and therapy selection: The
challenge of achieving statistical robustness and clinical utility.
Clin Trials. 10:653–665. 2013.PubMed/NCBI View Article : Google Scholar
|
|
63
|
Costea PI, Zeller G, Sunagawa S, Pelletier
E, Alberti A, Levenez F, Tramontano M, Driessen M, Hercog R, Jung
FE, et al: Towards standards for human fecal sample processing in
metagenomic studies. Nat Biotechnol. 35:1069–1076. 2017.PubMed/NCBI View Article : Google Scholar
|
|
64
|
Tam V, Patel N, Turcotte M, Bossé Y, Paré
G and Meyre D: Benefits and limitations of genome-wide association
studies. Nat Rev Genet. 20:467–484. 2019.PubMed/NCBI View Article : Google Scholar
|
|
65
|
Cotsapas C and Mitrovic M: Genome-wide
association studies of multiple sclerosis. Clin Transl Immunology.
7(e1018)2018.PubMed/NCBI View Article : Google Scholar
|
|
66
|
Baranzini SE and Oksenberg JR: The
genetics of multiple sclerosis: From 0 to 200 in 50 years. Trends
Genet. 33:960–970. 2017.PubMed/NCBI View Article : Google Scholar
|
|
67
|
Zhang C, Shang G, Gui X, Zhang X, Bai XC
and Chen ZJ: Structural basis of STING binding with and
phosphorylation by TBK1. Nature. 567:394–398. 2019.PubMed/NCBI View Article : Google Scholar
|
|
68
|
Cervantes-Gracia K and Husi H: Integrative
analysis of multiple sclerosis using a systems biology approach.
Sci Rep. 8(5633)2018.PubMed/NCBI View Article : Google Scholar
|
|
69
|
Chimusa ER, Dalvie S, Dandara C, Wonkam A
and Mazandu GK: Post genome-wide association analysis: Dissecting
computational pathway/network-based approaches. Brief Bioinform.
20:690–700. 2019.PubMed/NCBI View Article : Google Scholar
|
|
70
|
Muñoz-Culla M, Irizar H and Otaegui D: The
genetics of multiple sclerosis: Review of current and emerging
candidates. Appl Clin Genet. 6:63–73. 2013.PubMed/NCBI View Article : Google Scholar
|
|
71
|
Rodriguez-Esteban R and Jiang X:
Differential gene expression in disease: A comparison between
high-throughput studies and the literature. BMC Med Genomics.
10(59)2017.PubMed/NCBI View Article : Google Scholar
|
|
72
|
Paraboschi EM, Cardamone G, Rimoldi V,
Gemmati D, Spreafico M, Duga S, Soldà G and Asselta R:
Meta-analysis of multiple sclerosis microarray data reveals
dysregulation in RNA splicing regulatory genes. Int J Mol Sci.
16:23463–23481. 2015.PubMed/NCBI View Article : Google Scholar
|
|
73
|
Liu M, Hou X, Zhang P, Hao Y, Yang Y, Wu
X, Zhu D and Guan Y: Microarray gene expression profiling analysis
combined with bioinformatics in multiple sclerosis. Mol Biol Rep.
40:3731–3737. 2013.PubMed/NCBI View Article : Google Scholar
|
|
74
|
International Multiple Sclerosis Genetics
Consortium. Multiple sclerosis genomic map implicates peripheral
immune cells and microglia in susceptibility. Science.
365(eaav7188)2019.PubMed/NCBI View Article : Google Scholar
|
|
75
|
Shang Z, Sun W, Zhang M, Xu L, Jia X,
Zhang R and Fu S: Identification of key genes associated with
multiple sclerosis based on gene expression data from peripheral
blood mononuclear cells. PeerJ. 8(e8357)2020.PubMed/NCBI View Article : Google Scholar
|
|
76
|
Villoslada P and Baranzini S: Data
integration and systems biology approaches for biomarker discovery:
Challenges and opportunities for multiple sclerosis. J
Neuroimmunol. 248:58–65. 2012.PubMed/NCBI View Article : Google Scholar
|
|
77
|
Tomioka R and Matsui M: Biomarkers for
multiple sclerosis. Intern Med. 53:361–365. 2014.PubMed/NCBI View Article : Google Scholar
|
|
78
|
Gul M, Jafari AA, Shah M, Mirmoeeni S,
Haider SU, Moinuddin S and Chaudhry A: Molecular biomarkers in
multiple sclerosis and its related disorders: A critical review.
Int J Mol Sci. 21(6020)2020.PubMed/NCBI View Article : Google Scholar
|
|
79
|
Harris VK, Tuddenham JF and Sadiq SA:
Biomarkers of multiple sclerosis: Current findings. Degener Neurol
Neuromuscul Dis. 7:19–29. 2017.PubMed/NCBI View Article : Google Scholar
|
|
80
|
Afzal HMR, Luo S, Ramadan S and
Lechner-Scott J: The emerging role of artificial intelligence in
multiple sclerosis imaging. Mult Scler. 28:849–858. 2022.PubMed/NCBI View Article : Google Scholar
|
|
81
|
Arani LA, Hosseini A, Asadi F, Masoud SA
and Nazemi E: Intelligent computer systems for multiple sclerosis
diagnosis: A systematic review of reasoning techniques and methods.
Acta Inform Med. 26:258–264. 2018.PubMed/NCBI View Article : Google Scholar
|
|
82
|
Ion-Mărgineanu A, Kocevar G, Stamile C,
Sima DM, Durand-Dubief F, Van Huffel S and Sappey-Marinier D:
Machine learning approach for classifying multiple sclerosis
courses by combining clinical data with lesion loads and magnetic
resonance metabolic features. Front Neurosci.
11(398)2017.PubMed/NCBI View Article : Google Scholar
|
|
83
|
Roca P, Attye A, Colas L, Tucholka A,
Rubini P, Cackowski S, Ding J, Budzik JF, Renard F, Doyle S, et al:
Artificial intelligence to predict clinical disability in patients
with multiple sclerosis using FLAIR MRI. Diagn Interv Imaging.
101:795–802. 2020.PubMed/NCBI View Article : Google Scholar
|
|
84
|
Saccà V, Sarica A, Novellino F, Barone S,
Tallarico T, Filippelli E, Granata A, Chiriaco C, Bruno Bossio R,
Valentino P and Quattrone A: Evaluation of machine learning
algorithms performance for the prediction of early multiple
sclerosis from resting-state FMRI connectivity data. Brain Imaging
Behav. 13:1103–1114. 2019.PubMed/NCBI View Article : Google Scholar
|
|
85
|
Brosch T, Tang LY, Yoo Y, Li DK,
Traboulsee A and Tam R: Deep 3D convolutional encoder networks with
shortcuts for multiscale feature integration applied to multiple
sclerosis lesion segmentation. IEEE Trans Med Imaging.
35:1229–1239. 2016.PubMed/NCBI View Article : Google Scholar
|
|
86
|
Nedjati-Gilani GL, Schneider T, Hall MG,
Cawley N, Hill I, Ciccarelli O, Drobnjak I, Wheeler-Kingshott CAMG
and Alexander DC: Machine learning based compartment models with
permeability for white matter microstructure imaging. NeuroImage.
150:119–135. 2017.PubMed/NCBI View Article : Google Scholar
|
|
87
|
Branco D, di Martino B, Esposito A,
Tedeschi G, Bonavita S and Lavorgna L: Machine learning techniques
for prediction of multiple sclerosis progression. Soft Comput.
26:12041–12055. 2022.PubMed/NCBI View Article : Google Scholar
|
|
88
|
Kocevar G, Stamile C, Hannoun S, Cotton F,
Vukusic S, Durand-Dubief F and Sappey-Marinier D: Graph
theory-based brain connectivity for automatic classification of
multiple sclerosis clinical courses. Front Neurosci.
10(478)2016.PubMed/NCBI View Article : Google Scholar
|
|
89
|
Eshaghi A, Young AL, Wijeratne PA, Prados
F, Arnold DL, Narayanan S, Guttmann CRG, Barkhof F, Alexander DC,
Thompson AJ, et al: Identifying multiple sclerosis subtypes using
unsupervised machine learning and MRI data. Nat Commun.
12(2078)2021.PubMed/NCBI View Article : Google Scholar
|
|
90
|
Tommasin S, Cocozza S, Taloni A, Giannì C,
Petsas N, Pontillo G, Petracca M, Ruggieri S, De Giglio L, Pozzilli
C, et al: Machine learning classifier to identify clinical and
radiological features relevant to disability progression in
multiple sclerosis. J Neurol. 268:4834–4845. 2021.PubMed/NCBI View Article : Google Scholar
|
|
91
|
Derfuss T: Personalized medicine in
multiple sclerosis: Hope or reality? BMC Med.
10(116)2012.PubMed/NCBI View Article : Google Scholar
|
|
92
|
Hansen MR and Okuda DT: Precision medicine
for multiple sclerosis promotes preventative medicine. Ann NY Acad
Sci. 1420:62–71. 2018.PubMed/NCBI View Article : Google Scholar
|
|
93
|
Schork NJ: Artificial intelligence and
personalized medicine. Cancer Treat Res. 178:265–283.
2019.PubMed/NCBI View Article : Google Scholar
|
|
94
|
Chase HS, Mitrani LR, Lu GG and Fulgieri
DJ: Early recognition of multiple sclerosis using natural language
processing of the electronic health record. BMC Med Inform Decis
Mak. 17(24)2017.PubMed/NCBI View Article : Google Scholar
|
|
95
|
Zhao Y, Healy BC, Rotstein D, Guttmann CR,
Bakshi R, Weiner HL, Brodley CE and Chitnis T: Exploration of
machine learning techniques in predicting multiple sclerosis
disease course. PLoS One. 12(e0174866)2017.PubMed/NCBI View Article : Google Scholar
|
|
96
|
Ziemssen T, Kern R, Voigt I and Haase R:
Data collection in multiple sclerosis: The MSDS approach. Front
Neurol. 11(445)2020.PubMed/NCBI View Article : Google Scholar
|