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Author Correction : DOME : recommendations for supervised machine learning validation in biologyWalsh, Ian; Fishman, Dmytro; Garcia-Gasulla, Dario; Titma, Tiina; Pollastri, GianlucaNature methods2021 / p. 1409–1410 : ill https://doi.org/10.1038/s41592-021-01304-2 Journal metrics at Scopus Article at Scopus

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- DOME : recommendations for supervised machine learning validation in biology
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Author Correction: SciPy 1.0 : fundamental algorithms for scientific computing in Python (Nature Methods, (2020), 17, 3, (261-272), 10.1038/s41592-019-0686-2)Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt; Reddy, Tyler; Cournapeau, David; Burovski, Evgeni; Peterson, Pearu; Weckesser, Warren; Bright, JonathanNature Methods2020 / p. 352 https://doi.org/10.1038/s41592-020-0772-5 Journal metrics at Scopus Article at Scopus Journal metrics at WOS

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DOME : recommendations for supervised machine learning validation in biologyWalsh, Ian; Fishman, Dmytro; Garcia-Gasulla, Dario; Titma, Tiina; Pollastri, Gianluca; Harrow, Jennifer; Psomopoulos, Fotis E.; Tosatto, Silvio C. E.Nature methods2021 / p. 1122–1127 https://doi.org/10.1038/s41592-021-01205-4 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

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Recovery of missing single-cell RNA-sequencing data with optimized transcriptomic referencesPool, Allan-Hermann; Poldsam, Helen; Chen, Sisi; Thomson, Matt; Oka, YukiNature Methods2023 / p. 1506 - 1515 https://doi.org/10.1038/s41592-023-02003-w Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

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SciPy 1.0 : fundamental algorithms for scientific computing in PythonVirtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt; Reddy, Tyler; Cournapeau, David; Burovski, Evgeni; Peterson, PearuNature methods2020 / p. 261-272 https://doi.org/10.1038/s41592-019-0686-2 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

journal article EST / journal article ENG
Related publications 1
- Author Correction: SciPy 1.0 : fundamental algorithms for scientific computing in Python (Nature Methods, (2020), 17, 3, (261-272), 10.1038/s41592-019-0686-2)

Number of records 5, displaying 1 - 5

1. Changing nature of work
2. nature
3. nature- and biological-inspired
4. nature conservation
5. nature inspired
6. nature protection
7. nature tourism
8. nature values
9. nature writing
10. nature-based composite
11. nature-based solutions
12. nature-based solutions (NBSs)
13. nature-inspired tribology
14. nature-like fishway
15. values of nature
16. ab-initio methods
17. active learning methods
18. algebraic methods
19. algebraic/geometric methods
20. an ensemble of compression methods
21. analysis methods
22. analytical methods
23. approximate methods
24. approximation methods
25. assessment methods
26. Backstepping methods
27. biology experimental methods
28. calculation methods
29. casting methods
30. Cesaro methods
31. chemometric methods
32. computational design methods
33. computational methods
34. computer aided methods
35. computer vision-based methods
36. control methods
37. decontamination methods
38. deep learning methods
39. Design and assessment methods
40. design methods
41. detection methods
42. diagnostics methods
43. didactic methods
44. digital methods
45. electronic structure methods
46. ensemble methods
47. Euler-Knopp methods
48. excavation methods
49. extraction and detection methods
50. finite-element (FE) methods
51. finite-element methods
52. finite-element methods (FEM)
53. flow assessment methods
54. formal methods
55. frontier methods
56. Gauss-Newton-type methods
57. Gauss–Newton-type methods
58. generalized Nörlund methods
59. geometric methods
60. graphic methods
61. integration methods
62. interaction methods
63. interactive teaching methods
64. interpretation methods
65. invasive methods
66. inverse methods
67. inverse methods for engineering applications
68. linear and nonlinear unsupervised methods
69. material characterization methods
70. Mathematical methods
71. matrix methods
72. methods
73. methods of measuring
74. methods of study
75. methods: data analysis
76. Methods: statistical
77. mixed methods
78. mixed-methods
79. model-free methods
80. modulation methods
81. Monte Carlo methods
82. multi-criteria decision-making methods
83. nearest neighbor methods
84. numerical methods
85. optic methods
86. optimization methods
87. outlier’s detection methods
88. physico-chemical methods
89. phytoplankton detection methods
90. plan-driven methods
91. polynomial methods
92. practical teaching methods
93. prediction methods
94. pseudospectral methods
95. qualitative methods
96. qualitative research methods
97. quantitative methods
98. regular matrix methods
99. research methods
100. reversal methods
101. sampling methods
102. selection methods
103. separation methods
104. shootthrough control methods
105. shoot-through control methods
106. stability prediction methods
107. standardized methods
108. state space methods
109. Statistical methods
110. statistics methods
111. substation testing methods
112. Taguchi methods
113. teaching methods
114. testing methods
115. timber construction (construction methods)
116. tracer gas methods
117. transmission line matrix methods
118. Tree-based methods
119. variational methods
120. verification methods
121. visual methods
122. visualization design and evaluation methods
123. X-ray methods
124. X-ray methods B
125. ﬁnite element methods

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