Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells

1. Maurel C, Dangoumau A, Marouillat S, et al. Causative genes in

amyotrophic lateral sclerosis and protein degradation pathways: a

link to neurodegeneration. Mol Neurobiol 2018;55:6480–6499.

2. Brooks BR, Miller RG, Swash M, Munsat TL. El Escorial revisited:

revised criteria for the diagnosis of amyotrophic lateral sclerosis.

Amyotroph Lateral Scler Other Motor Neuron Disord 2000;1:

293–299.

3. Hardiman O, van den Berg LH, Kiernan MC. Clinical diagnosis and

management of amyotrophic lateral sclerosis. Nat Rev Neurol 2011;

7:639–649.

4. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:

115–118.

5. Causey JL, Zhang J, Ma S, et al. Highly accurate model for prediction

of lung nodule malignancy with CT scans. Sci Rep 2018;8:9286.

6. Kather JN, Pearson AT, Halama N, et al. Deep learning can predict

microsatellite instability directly from histology in gastrointestinal

cancer. Nat Med 2019;25:1054–1056.

7. Christiansen EM, Yang SJ, Ando DM, et al. In Silico labeling:

predicting fluorescent labels in unlabeled images. Cell 2018;173:

792–803.e19.

8. Okita K, Yamakawa T, Matsumura Y, et al. An efficient nonviral

method to generate integration-free human-induced pluripotent

stem cells from cord blood and peripheral blood cells. Stem Cells

2013;31:458–466.

9. Tada Y, Kume K, Matsuda Y, et al. Genetic screening for potassium

channel mutations in Japanese autosomal dominant spinocerebellar

ataxia. J Hum Genet 2020;65:363–369.

10. Renton AE, Majounie E, Waite A, et al. A hexanucleotide repeat

expansion in C9ORF72 is the cause of chromosome 9p21-linked

ALS-FTD. Neuron 2011;72:257–268.

11. Philbrick KA, Yoshida K, Inoue D, et al. What does deep learning

see? Insights from a classifier trained to predict contrast enhancement phase from CT images. AJR Am J Roentgenol 2018;211:

1184–1193.

12. Rampasek L, Goldenberg A. TensorFlow: biology’s gateway to deep

learning? Cell Syst 2016;2:12–14.

13. Murakami N, Imamura K, Izumi Y, et al. Proteasome impairment in

neural cells derived from HMSN-P patient iPSCs. Mol Brain 2017;

10:7.

14. Matsuzono K, Imamura K, Murakami N, et al. Antisense oligonucleotides reduce RNA foci in spinocerebellar ataxia 36 patient iPSCs.

Mol Ther Nucleic Acids 2017;8:211–219.

15. Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation.

IEEE Trans Pattern Anal Mach Intell 2017;39:2481–2495.

16. Hohman F, Park H, Robinson C, Chau DH. Summit: scaling deep

learning interpretability by visualizing activation and attribution summarizations. IEEE Trans Vis Comput Graph 2020;26:1096–1106.

17. Cao C, Liu F, Tan H, et al. Deep learning and its applications in biomedicine. Genomics Proteomics Bioinformatics 2018;16:17–32.

18. Yang SJ, Lipnick SL, Makhortova NR, et al. Applying deep neural

network analysis to high-content image-based assays. SLAS Discov

2019;24:829–841.

19. Imamura K, Izumi Y, Watanabe A, et al. The Src/c-Abl pathway is a

potential therapeutic target in amyotrophic lateral sclerosis. Sci

Transl Med 2017;9:eaaf3962.

20. Schrooten M, Smetcoren C, Robberecht W, Van Damme P. Benefit

of the Awaji diagnostic algorithm for amyotrophic lateral sclerosis: a

prospective study. Ann Neurol 2011;70:79–83.

21. Kiernan MC, Vucic S, Talbot K, et al. Improving clinical trial outcomes in amyotrophic lateral sclerosis. Nat Rev Neurol 2021;17:

104–118.

22. Costa J, Swash M, de Carvalho M. Awaji criteria for the diagnosis of

amyotrophic lateral sclerosis: a systematic review. Arch Neurol 2012;

69:1410–1416.

ANNALS of

Neurology

A Self-archived copy in

Kyoto University Research Information Repository

https://repository.kulib.kyoto-u.ac.jp

23. Gagliardi D, Meneri M, Saccomanno D, et al. Diagnostic and prognostic role of blood and cerebrospinal fluid and blood neurofilaments in amyotrophic lateral sclerosis: a review of the literature.

Int J Mol Sci 2019;20:4152.

24. Vijayakumar UG, Milla V, Cynthia Stafford MY, et al. A systematic

review of suggested molecular strata, biomarkers and their tissue

sources in ALS. Front Neurol 2019;10:400.

25. Richards D, Morren JA, Pioro EP. Time to diagnosis and factors

affecting diagnostic delay in amyotrophic lateral sclerosis. J Neurol

Sci 2020;417:117054.

26. Al-Chalabi A, Fang F, Hanby MF, et al. An estimate of amyotrophic

lateral sclerosis heritability using twin data. J Neurol Neurosurg Psychiatry 2010;81:1324–1326.

27. Bandres-Ciga S, Noyce AJ, Hemani G, et al. Shared polygenic risk

and causal inferences in amyotrophic lateral sclerosis. Ann Neurol

2019;85:470–481.

28. Polo JM, Anderssen E, Walsh RM, et al. A molecular roadmap of

reprogramming somatic cells into iPS cells. Cell 2012;151:

1617–1632.

29. Hawrot J, Imhof S, Wainger BJ. Modeling cell-autonomous motor

neuron phenotypes in ALS using iPSCs. Neurobiol Dis 2020;134:

104680.

30. Fujimori K, Ishikawa M, Otomo A, et al. Modeling sporadic ALS in

iPSC-derived motor neurons identifies a potential therapeutic agent.

Nat Med 2018;24:1579–1589.

31. Burkhardt MF, Martinez FJ, Wright S, et al. A cellular model for sporadic ALS using patient-derived induced pluripotent stem cells. Mol

Cell Neurosci 2013;56:355–364.

32. Gupta A, Harrison PJ, Wieslander H, et al. Deep learning in image

cytometry: a review. Cytometry A 2019;95:366–380.

33. Horvath S. DNA methylation age of human tissues and cell types.

Genome Biol 2013;14:R115.

Volume 00, No. 0

...