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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. direct methods
46. electronic structure methods
47. ensemble methods
48. Euler-Knopp methods
49. excavation methods
50. extraction and detection methods
51. finite-element (FE) methods
52. finite-element methods
53. finite-element methods (FEM)
54. finite-element methods (FEMs)
55. flow assessment methods
56. formal methods
57. frontier methods
58. Gauss-Newton-type methods
59. Gauss–Newton-type methods
60. generalized Nörlund methods
61. geometric methods
62. graphic methods
63. hybrid methods
64. integration methods
65. interaction methods
66. interactive teaching methods
67. interpretation methods
68. invasive methods
69. inverse methods
70. inverse methods for engineering applications
71. linear and nonlinear unsupervised methods
72. material characterization methods
73. Mathematical methods
74. matrix methods
75. methods
76. methods of measuring
77. methods of study
78. methods: data analysis
79. Methods: statistical
80. mixed methods
81. mixed-methods
82. model-free methods
83. modulation methods
84. Monte Carlo methods
85. multi-criteria decision-making methods
86. nearest neighbor methods
87. numerical methods
88. optic methods
89. optimization methods
90. outlier’s detection methods
91. physico-chemical methods
92. phytoplankton detection methods
93. plan-driven methods
94. polynomial methods
95. practical teaching methods
96. prediction methods
97. pseudospectral methods
98. qualitative methods
99. qualitative research methods
100. quantitative methods
101. regular matrix methods
102. research methods
103. reversal methods
104. sampling methods
105. selection methods
106. separation methods
107. shootthrough control methods
108. shoot-through control methods
109. stability prediction methods
110. standardized methods
111. state space methods
112. Statistical methods
113. statistics methods
114. substation testing methods
115. Taguchi methods
116. teaching methods
117. testing methods
118. timber construction (construction methods)
119. tracer gas methods
120. transmission line matrix methods
121. Tree-based methods
122. variational methods
123. verification methods
124. visual methods
125. visualization design and evaluation methods
126. X-ray methods
127. X-ray methods B
128. ﬁnite element methods

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