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Nature methods (allikas)

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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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Seotud publikatsioonid 1
- 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

artikkel ajakirjas EST / artikkel ajakirjas ENG
Seotud publikatsioonid 1
- SciPy 1.0 : fundamental algorithms for scientific computing in Python
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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

artikkel ajakirjas EST / artikkel ajakirjas ENG
Seotud publikatsioonid 1
- Author Correction : DOME : recommendations for supervised machine learning validation in biology
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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

artikkel ajakirjas EST / artikkel ajakirjas ENG
Seotud publikatsioonid 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)

Kirjeid leitud 5, kuvan 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 measuring
78. methods of study
79. methods: data analysis
80. Methods: statistical
81. mixed methods
82. mixed-methods
83. model-free methods
84. modulation methods
85. Monte Carlo methods
86. multi-criteria decision-making methods
87. nearest neighbor methods
88. numerical methods
89. optic methods
90. optimization methods
91. outlier’s detection methods
92. physico-chemical methods
93. phytoplankton detection methods
94. plan-driven methods
95. polynomial methods
96. practical teaching methods
97. prediction methods
98. pseudospectral methods
99. qualitative methods
100. qualitative research methods
101. quantitative methods
102. regular matrix methods
103. research methods
104. reversal methods
105. sampling methods
106. selection methods
107. separation methods
108. shootthrough control methods
109. shoot-through control methods
110. stability prediction methods
111. standardized methods
112. state space methods
113. Statistical methods
114. statistics methods
115. substation testing methods
116. Taguchi methods
117. teaching methods
118. testing methods
119. timber construction (construction methods)
120. tracer gas methods
121. transmission line matrix methods
122. Tree-based methods
123. variational methods
124. verification methods
125. visual methods
126. visualization design and evaluation methods
127. X-ray methods
128. X-ray methods B
129. ﬁnite element methods

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