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

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