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

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