-
Notifications
You must be signed in to change notification settings - Fork 3
/
index-ml.html
652 lines (551 loc) · 33.5 KB
/
index-ml.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
<!DOCTYPE html>
<html lang="en">
<head>
<title>Galaxy Europe</title>
<meta property="og:title" content="" />
<meta property="og:description" content="" />
<meta property="og:image" content="/assets/media/galaxy-eu-logo.512.png" />
<meta name="description" content="The European Galaxy Instance">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<link rel="stylesheet" href="/assets/css/bootstrap.min.css">
<link rel="stylesheet" href="/assets/css/main.css">
<link rel="canonical" href="https://galaxyproject.eu/index-ml.html">
<link rel="shortcut icon" href="/assets/media/galaxy-eu-logo.64.png" type="image/x-icon" />
<link rel="alternate" type="application/rss+xml" title="Galaxy Europe" href="/feed.xml">
<link href="/assets/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-wvfXpqpZZVQGK6TAh5PVlGOfQNHSoD2xbE+QkPxCAFlNEevoEH3Sl0sibVcOQVnN" crossorigin="anonymous">
<script src="/assets/js/jquery-3.2.1.slim.min.js" integrity="sha256-k2WSCIexGzOj3Euiig+TlR8gA0EmPjuc79OEeY5L45g=" crossorigin="anonymous"></script>
<script src="/assets/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script>
</head>
<body>
<div id="wrap">
<div id="main">
<div class="container" id="maincontainer">
<div class="home">
<h1 class="no_toc" id="welcome-to-the-galaxy-machine-learning-workbench">Welcome to the Galaxy Machine Learning workbench</h1>
<p><img src="/assets/media/machine_learning_logo.png" alt="ML Galaxy" class="rna-intro-right" /></p>
<p>The Galaxy Machine Learning workbench is a comprehensive set of data preprocessing, machine learning, deep learning and visualisation tools, consolidated workflows for end-to-end machine learning analysis and training materials to showcase the usage of these tools.
The workbench is available on the <a href="https://galaxyproject.org" target="_blank">Galaxy framework</a>, which guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated machine learning analyses independent of command-line knowledge.</p>
<p>The workbench provides you with a Swiss Army knife of <a href="https://scikit-learn.org" target="_blank">scikit-learn</a>,
<a href="https://keras.io" target="_blank">Keras</a> (a deep learning library based on <a href="https://www.tensorflow.org" target="_blank">TensorFlow</a>) and various other tools to transform, learn and predict and plot your data.</p>
<p>The workbench is currently developed by the <a href="https://goeckslab.org">Goecks Lab</a> and the <a href="https://galaxyproject.eu/" target="_blank">European Galaxy project</a>.
The <a href="http://www.denbi.de" target="_blank">German Network for Bioinformatics Infrastructure (de.NBI)</a>,
which runs the German <a href="https://www.elixir-europe.org/" target="_blank">ELIXIR Node</a>, provides the necessary compute clusters with CPUs and GPU resources.</p>
<p>The project is a community effort, please jump in, ask questions, and contribute to the development of new tools, workflows or trainings!</p>
<h1 class="no_toc" id="content">Content</h1>
<ol id="markdown-toc">
<li><a href="#get-started" id="markdown-toc-get-started">Get started</a></li>
<li><a href="#training" id="markdown-toc-training">Training</a></li>
<li><a href="#available-tools" id="markdown-toc-available-tools">Available tools</a> <ol>
<li><a href="#classification" id="markdown-toc-classification">Classification</a></li>
<li><a href="#regression" id="markdown-toc-regression">Regression</a></li>
<li><a href="#clustering" id="markdown-toc-clustering">Clustering</a></li>
<li><a href="#model-building" id="markdown-toc-model-building">Model building</a></li>
<li><a href="#model-evaluation" id="markdown-toc-model-evaluation">Model evaluation</a></li>
<li><a href="#preprocessing-and-feature-selection" id="markdown-toc-preprocessing-and-feature-selection">Preprocessing and feature selection</a></li>
<li><a href="#deep-learning" id="markdown-toc-deep-learning">Deep learning</a></li>
<li><a href="#visualization" id="markdown-toc-visualization">Visualization</a></li>
<li><a href="#utilities" id="markdown-toc-utilities">Utilities</a></li>
<li><a href="#interactive-environments" id="markdown-toc-interactive-environments">Interactive Environments</a></li>
</ol>
</li>
<li><a href="#contributors" id="markdown-toc-contributors">Contributors</a></li>
</ol>
<h1 id="get-started">Get started</h1>
<p>Are you new to Galaxy, or returning after a long time, and looking for help to get started?
Take <a href="https://ml.usegalaxy.eu/tours/core.galaxy_ui" target="_blank">a guided tour</a> through Galaxy’s user interface.</p>
<h1 id="training">Training</h1>
<p>We are passionate about training. So we are working in close collaboration with the
<a href="https://galaxyproject.org/teach/gtn/" target="_blank">Galaxy Training Network (GTN)</a> to develop training materials of data analyses
based on Galaxy <a class="citation" href="#batut2017community">(Batut <i>et al.</i>, 2017)</a>. These materials hosted on the GTN GitHub
repository are available online at <a href="https://training.galaxyproject.org" target="_blank">https://training.galaxyproject.org</a>.</p>
<p>Want to learn more about machine learning? Take one of our guided tours or check out the following hands-on tutorials, developed together with the <a href="https://galaxyproject.org/teach/gtn/">GTN community</a>.</p>
<table class="table table-striped">
<thead>
<tr>
<th>Lesson</th>
<th>Slides</th>
<th>Hands-on</th>
<th>Input dataset</th>
<th>Workflows</th>
<th>Galaxy tour</th>
<th>Galaxy History</th>
</tr>
</thead>
<tbody>
<tr>
<td>Basics of machine learning</td>
<td> </td>
<td><a href="https://training.galaxyproject.org/training-material/topics/statistics/tutorials/machinelearning/tutorial.html" target="_blank"><i class="fa fa-laptop" aria-hidden="true"></i></a></td>
<td><a href="https://zenodo.org/record/1468039#.W8zyxBRoSAo" target="_blank"><i class="fa fa-files-o" aria-hidden="true"></i></a></td>
<td><a href="https://ml.usegalaxy.eu/workflows/run?id=17e99647745eb150" target="_blank"><i class="fa fa-share-alt" aria-hidden="true"></i></a></td>
<td><a href="https://github.com/galaxyproject/training-material/tree/master/topics/statistics/tutorials/machinelearning/tours/" target="_blank"><i class="fa fa-magic" aria-hidden="true"></i></a></td>
<td><a href="https://ml.usegalaxy.eu/u/sbray/h/basics-of-machine-learning" target="_blank"><i class="fa fa-list-ul" aria-hidden="true"></i></a></td>
</tr>
<tr>
<td>Classification</td>
<td> </td>
<td><a href="https://training.galaxyproject.org/training-material/topics/statistics/tutorials/classification_machinelearning/tutorial.html" target="_blank"><i class="fa fa-laptop" aria-hidden="true"></i></a></td>
<td><a href="https://zenodo.org/record/3738729#.XsjpbHUzY5k" target="_blank"><i class="fa fa-files-o" aria-hidden="true"></i></a></td>
<td><a href="https://ml.usegalaxy.eu/workflows/run?id=1d55d5d20c581b16" target="_blank"><i class="fa fa-share-alt" aria-hidden="true"></i></a></td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>Regression</td>
<td> </td>
<td><a href="https://training.galaxyproject.org/training-material/topics/statistics/tutorials/regression_machinelearning/tutorial.html" target="_blank"><i class="fa fa-laptop" aria-hidden="true"></i></a></td>
<td><a href="https://zenodo.org/record/2579649#.XHep39F7mL4" target="_blank"><i class="fa fa-files-o" aria-hidden="true"></i></a></td>
<td><a href="https://ml.usegalaxy.eu/workflows/run?id=138d4893a1d6228e" target="_blank"><i class="fa fa-share-alt" aria-hidden="true"></i></a></td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>Age prediction using machine learning</td>
<td> </td>
<td><a href="https://training.galaxyproject.org/training-material/topics/statistics/tutorials/age-prediction-with-ml/tutorial.html" target="_blank"><i class="fa fa-laptop" aria-hidden="true"></i></a></td>
<td><a href="https://zenodo.org/record/2545213#.XEWTJ9-YVa0" target="_blank"><i class="fa fa-files-o" aria-hidden="true"></i></a></td>
<td><a href="https://ml.usegalaxy.eu/workflows/run?id=83fe480cdbb70099" target="_blank"><i class="fa fa-share-alt" aria-hidden="true"></i></a> <a href="https://ml.usegalaxy.eu/workflows/run?id=a669986e1a5cee31" target="_blank"><i class="fa fa-share-alt" aria-hidden="true"></i></a></td>
<td> </td>
<td><a href="https://ml.usegalaxy.eu/u/sbray/h/age-prediction-using-machine-learning---rnaseq" target="_blank"><i class="fa fa-list-ul" aria-hidden="true"></i></a> <a href="https://ml.usegalaxy.eu/u/sbray/h/age-prediction-using-machine-learning---dna-methylation" target="_blank"><i class="fa fa-list-ul" aria-hidden="true"></i></a></td>
</tr>
<tr>
<td>Clustering</td>
<td> </td>
<td><a href="https://training.galaxyproject.org/training-material/topics/statistics/tutorials/clustering_machinelearning/tutorial.html" target="_blank"><i class="fa fa-laptop" aria-hidden="true"></i></a></td>
<td><a href="https://zenodo.org/record/3813447#.Xsjsy3UzY5k" target="_blank"><i class="fa fa-files-o" aria-hidden="true"></i></a></td>
<td><a href="https://ml.usegalaxy.eu/workflows/run?id=848389c45cebe34f" target="_blank"><i class="fa fa-share-alt" aria-hidden="true"></i></a></td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>Introduction to deep learning</td>
<td> </td>
<td><a href="https://training.galaxyproject.org/training-material/topics/statistics/tutorials/intro_deep_learning/tutorial.html" target="_blank"><i class="fa fa-laptop" aria-hidden="true"></i></a></td>
<td><a href="https://zenodo.org/record/3706539#.XsjteHUzY5l" target="_blank"><i class="fa fa-files-o" aria-hidden="true"></i></a></td>
<td> </td>
<td> </td>
<td> </td>
</tr>
</tbody>
</table>
<h1 id="available-tools">Available tools</h1>
<p>In this section we list the most important tools that have been integrated into the Machine Learning workbench.
There are many more tools available so please have a more detailed look at the tool panel.
For better readability, we have divided them into categories.</p>
<h2 id="classification">Classification</h2>
<p>Identifying which category an object belongs to.</p>
<table class="table table-striped tooltable">
<thead>
<tr>
<th>Tool</th>
<th>Description</th>
<th>Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_svm_classifier/sklearn_svm_classifier" target="_top" title="sklearn_svm_classifier">sklearn_svm_classifier</a></td>
<td>Support vector machines (SVMs) for classification</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_nn_classifier/sklearn_nn_classifier" target="_top" title="sklearn_nn_classifier">sklearn_nn_classifier</a></td>
<td>Nearest Neighbors Classification</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_ensemble/sklearn_ensemble" target="_top" title="sklearn_ensemble">sklearn_ensemble</a></td>
<td>Ensemble methods for classification and regression</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_discriminant_classifier/sklearn_discriminant_classifier" target="_top" title="sklearn_discriminant_classifier">sklearn_discriminant_classifier</a></td>
<td>Linear and Quadratic Discriminant Analysis</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_generalized_linear/sklearn_generalized_linear" target="_top" title="sklearn_generalized_linear">sklearn_generalized_linear</a></td>
<td>Generalized linear models for classification and regression</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_clf_metrics/sklearn_clf_metrics" target="_top" title="sklearn_clf_metrics">sklearn_clf_metrics</a></td>
<td>Calculate metrics for classification performance</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
</tbody>
</table>
<h2 id="regression">Regression</h2>
<p>Predicting a continuous-valued attribute associated with an object.</p>
<table class="table table-striped tooltable">
<thead>
<tr>
<th>Tool</th>
<th>Description</th>
<th>Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_ensemble/sklearn_ensemble" target="_top" title="sklearn_ensemble">sklearn_ensemble</a></td>
<td>Ensemble methods for classification and regression</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_generalized_linear/sklearn_generalized_linear" target="_top" title="sklearn_generalized_linear">sklearn_generalized_linear</a></td>
<td>Generalized linear models for classification and regression</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_regression_metrics/sklearn_regression_metrics" target="_top" title="sklearn_regression_metrics">sklearn_regression_metrics</a></td>
<td>Calculate metrics for regression performance</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
</tbody>
</table>
<h2 id="clustering">Clustering</h2>
<p>Automatic grouping of similar objects into sets.</p>
<table class="table table-striped tooltable">
<thead>
<tr>
<th>Tool</th>
<th>Description</th>
<th>Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_numeric_clustering/sklearn_numeric_clustering" target="_top" title="sklearn_numeric_clustering">sklearn_numeric_clustering</a></td>
<td>Different numerical clustering algorithms</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
</tbody>
</table>
<h2 id="model-building">Model building</h2>
<p>Building general machine learning models.</p>
<table class="table table-striped tooltable">
<thead>
<tr>
<th>Tool</th>
<th>Description</th>
<th>Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_estimator_attributes/sklearn_estimator_attributes" target="_top" title="sklearn_estimator_attributes">sklearn_estimator_attributes</a></td>
<td>Estimator attributes to get all attributes from an estimator or scikit object</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_stacking_ensemble_models/sklearn_stacking_ensemble_models" target="_top" title="sklearn_stacking_ensemble_models">sklearn_stacking_ensemble_models</a></td>
<td>Stacking Ensembles to build stacking, voting ensemble models with numerous base options</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_searchcv/sklearn_searchcv" target="_top" title="sklearn_searchcv">sklearn_searchcv</a></td>
<td>Hyperparameter Search performs hyperparameter optimization using various SearchCVs</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_build_pipeline/sklearn_build_pipeline" target="_top" title="sklearn_build_pipeline">sklearn_build_pipeline</a></td>
<td>Pipeline Builder as an all-in-one platform to build pipeline, single estimator, preprocessor and custom wrappers</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
</tbody>
</table>
<h2 id="model-evaluation">Model evaluation</h2>
<p>Evaluation, validating and choosing parameters and models.</p>
<table class="table table-striped tooltable">
<thead>
<tr>
<th>Tool</th>
<th>Description</th>
<th>Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_model_validation/sklearn_model_validation" target="_top" title="sklearn_model_validation">sklearn_model_validation</a></td>
<td>Model Validation includes cross_validate, cross_val_predict, learning_curve, and more</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_pairwise_metrics/sklearn_pairwise_metrics" target="_top" title="sklearn_pairwise_metrics">sklearn_pairwise_metrics</a></td>
<td>Evaluate pairwise distances or compute affinity or kernel for sets of samples</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_train_test_eval/sklearn_train_test_eval" target="_top" title="sklearn_train_test_eval">sklearn_train_test_eval</a></td>
<td>Train, Test and Evaluation to fit a model using part of dataset and evaluate using the rest</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/model_prediction/model_prediction" target="_top" title="model_prediction">model_prediction</a></td>
<td>Model Prediction predicts on new data using a preffited model</td>
<td><a href="https://keras.io" target="_blank">Chollet et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_fitted_model_eval/sklearn_fitted_model_eval" target="_top" title="sklearn_fitted_model_eval">sklearn_fitted_model_eval</a></td>
<td>Evaluate a Fitted Model using a new batch of labeled data</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_model_fit/sklearn_model_fit" target="_top" title="sklearn_model_fit">sklearn_model_fit</a></td>
<td>Fit a Pipeline, Ensemble or other models using a labeled dataset</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
</tbody>
</table>
<h2 id="preprocessing-and-feature-selection">Preprocessing and feature selection</h2>
<p>Feature selection and preprocessing.</p>
<table class="table table-striped tooltable">
<thead>
<tr>
<th>Tool</th>
<th>Description</th>
<th>Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_data_preprocess/sklearn_data_preprocess" target="_top" title="sklearn_data_preprocess">sklearn_data_preprocess</a></td>
<td>Preprocess raw feature vectors into standardized datasets</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_feature_selection/sklearn_feature_selection" target="_top" title="sklearn_feature_selection">sklearn_feature_selection</a></td>
<td>Feature Selection module, including univariate filter selection methods and recursive feature elimination algorithm</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
</tbody>
</table>
<h2 id="deep-learning">Deep learning</h2>
<p>Build and use deep neural networks.</p>
<table class="table table-striped tooltable">
<thead>
<tr>
<th>Tool</th>
<th>Description</th>
<th>Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/keras_batch_models/keras_batch_models" target="_top" title="keras_batch_models">keras_batch_models</a></td>
<td>Build Deep learning Batch Training Models with online data generator for Genomic/Protein sequences and images</td>
<td><a href="https://keras.io" target="_blank">Chollet et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_builder/keras_model_builder" target="_top" title="keras_model_builder">keras_model_builder</a></td>
<td>Create deep learning model with an optimizer, loss function and fit parameters</td>
<td><a href="https://keras.io" target="_blank">Chollet et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config" target="_top" title="keras_model_config">keras_model_config</a></td>
<td>Create a deep learning model architecture using Keras</td>
<td><a href="https://keras.io" target="_blank">Chollet et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval" target="_top" title="keras_train_and_eval">keras_train_and_eval</a></td>
<td>Deep learning training and evaluation either implicitly or explicitly</td>
<td><a href="https://keras.io" target="_blank">Chollet et al. 2011</a></td>
</tr>
</tbody>
</table>
<h2 id="visualization">Visualization</h2>
<p>Plotting and visualization.</p>
<table class="table table-striped tooltable">
<thead>
<tr>
<th>Tool</th>
<th>Description</th>
<th>Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/plotly_regression_performance_plots/plotly_regression_performance_plots" target="_top" title="plotly_regression_performance_plots">plotly_regression_performance_plots</a></td>
<td>Plot actual vs predicted curves and residual plots of tabular data</td>
<td> </td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/plotly_ml_performance_plots/plotly_ml_performance_plots" target="_top" title="plotly_ml_performance_plots">plotly_ml_performance_plots</a></td>
<td>Plot confusion matrix, precision, recall and ROC and AUC curves of tabular data</td>
<td> </td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/ml_visualization_ex/ml_visualization_ex" target="_top" title="ml_visualization_ex">ml_visualization_ex</a></td>
<td>Machine Learning Visualization Extension includes several types of plotting for machine learning</td>
<td><a href="https://keras.io" target="_blank">Chollet et al. 2011</a></td>
</tr>
</tbody>
</table>
<h2 id="utilities">Utilities</h2>
<p>General data and table manipulation tools.</p>
<table class="table table-striped tooltable">
<thead>
<tr>
<th>Tool</th>
<th>Description</th>
<th>Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/iuc/table_compute/table_compute" target="_top" title="table_compute">table_compute</a></td>
<td>The power of the pandas data library for manipulating and computing expressions upon tabular data and matrices.</td>
<td> </td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/iuc/datamash_ops/datamash_ops" target="_top" title="datamash_ops">datamash_ops</a></td>
<td>Datamash operations on tabular data</td>
<td> </td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/iuc/datamash_transpose/datamash_transpose" target="_top" title="datamash_transpose">datamash_transpose</a></td>
<td>Transpose rows/columns in a tabular file</td>
<td> </td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_sample_generator/sklearn_sample_generator" target="_top" title="sklearn_sample_generator">sklearn_sample_generator</a></td>
<td>Generate random samples with controlled size and complexity</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
<tr>
<td><a href="https://ml.usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_train_test_split/sklearn_train_test_split" target="_top" title="sklearn_train_test_split">sklearn_train_test_split</a></td>
<td>Split Dataset into training and test subsets</td>
<td><a href="http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" target="_blank">Pedregosa et al. 2011</a></td>
</tr>
</tbody>
</table>
<h2 id="interactive-environments">Interactive Environments</h2>
<p>You have done the heavy lifting and now want to use your coding skills inside Jupyter or RStudio? Work on data with the following:</p>
<table class="table table-striped tooltable">
<thead>
<tr>
<th>Tool</th>
<th>Description</th>
<th>Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://live.usegalaxy.eu/?tool_id=interactive_tool_jupyter_notebook" target="_blank">Jupyter</a></td>
<td>Jupyter lab</td>
<td> </td>
</tr>
<tr>
<td><a href="https://live.usegalaxy.eu/?tool_id=interactive_tool_rstudio_notebook" target="_blank">RStudio</a></td>
<td>RStudio</td>
<td> </td>
</tr>
</tbody>
</table>
<h1 id="contributors">Contributors</h1>
<ul>
<li><a href="https://github.com/qiagu">Qiang Gu</a></li>
<li><a href="https://github.com/jgoecks">Jeremy Goecks</a></li>
<li><a href="https://github.com/anuprulez">Anup Kumar</a></li>
<li><a href="https://github.com/bgruening">Bjoern Gruening</a></li>
<li><a href="https://github.com/khanteymoori">Alireza Khanteymoori</a></li>
<li><a href="https://github.com/simonbray">Simon Bray</a></li>
<li><a href="https://github.com/VJalili">Vahid Jalili</a></li>
</ul>
<h2>Our Data Policy</h2>
<table class="table table-striped">
<thead>
<tr>
<th>Registered Users</th><th>Unregistered Users</th><th>FTP Data</th><th>GDPR Compliance</th>
</tr>
</thead>
<tbody>
<tr>
<td>User data on UseGalaxy.eu (i.e. datasets, histories) will be available as long
as they are not deleted by the user. Once marked as deleted the datasets will
be permanently removed within 14 days. If the user "purges" the dataset in the
Galaxy, it will be removed immediately, permanently.
An <a href="https://docs.google.com/forms/d/e/1FAIpQLSf9w2MOS6KOlu9XdhRSDqWnCDkzoVBqHJ3zH_My4p8D8ZgkIQ/viewform" target="_blank">extended quota can be requested</a>
for a limited time period in special cases.
</td>
<td>Processed data will only be accessible during one browser session, using a
cookie to identify your data. This cookie is not used for any other purposes
(e.g. tracking or analytics).
If UseGalaxy.eu service is not accessed for 90 days, those datasets will be
permanently deleted.
</td>
<td>Any user data uploaded to our <a href="https://galaxyproject.eu/ftp/">FTP server</a> should be imported into Galaxy as soon
as possible. Data left in FTP folders for more than 3 months, will be deleted.
</td>
<td>The Galaxy service complies with the EU General Data Protection Regulation
(GDPR). You can read more about this on our
<a href="https://usegalaxy.eu/terms/">Terms and Conditions</a>.</td>
</tr>
</tbody>
</table>
<!-- <h4>Registered Users</h4>
User data on UseGalaxy.eu (i.e. datasets, histories) will be available as long
as they are not deleted by the user. Once marked as deleted the datasets will
be permanently removed within 14 days. If the user "purges" the dataset in the
Galaxy, it will be removed immediately, permanently.
An <a href="https://docs.google.com/forms/d/e/1FAIpQLSf9w2MOS6KOlu9XdhRSDqWnCDkzoVBqHJ3zH_My4p8D8ZgkIQ/viewform" target="_blank">extended quota can be requested</a>
for a limited time period in special cases.
<h4>Unregistered Users</h4>
Processed data will only be accessible during one browser session, using a
cookie to identify your data. This cookie is not used for any other purposes
(e.g. tracking or analytics).
If UseGalaxy.eu service is not accessed for 90 days, those datasets will be
permanently deleted.
<h4>FTP Data</h4>
Any user data uploaded to our <a href="https://galaxyproject.eu/ftp/">FTP server</a> should be imported into Galaxy as soon
as possible. Data left in FTP folders for more than 3 months, will be deleted.
<h4>GDPR Compliance</h4>
The Galaxy service complies with the EU General Data Protection Regulation
(GDPR). You can read more about this on our
<a href="https://usegalaxy.eu/terms/">Terms and Conditions</a>.
-->
<div>
<iframe style="border: 0px" width="100%" height="150px" src="https://stats.galaxyproject.eu/d-solo/000000034/jobs-dashboard?orgId=1&refresh=1m&panelId=1" ></iframe>
</div>
<div>
<!--<iframe style="border: 0px" width="33%" height="100px" src="https://stats.galaxyproject.eu/d-solo/000000034/jobs-dashboard?orgId=1&refresh=1m&panelId=3" ></iframe>-->
</div>
<div class="row">
<section class="section-content">
<div class="col-md-12">
</div>
</section>
</div>
</div>
</div>
</div>
</div>
<footer class="navbar-default">
<div class="container">
<div class="row">
</div>
<div class="row">
<div class="col-lg-12" style="text-align:center">
<p>UseGalaxy.eu is maintained largely by the <a href="/freiburg/">Freiburg Galaxy Team</a> but also collectively by groups and individuals from across Europe. All of the member sites in this repository contribute to the European Galaxy Project.
For <strong>acknowledgement</strong>, please refer to the <a href="/about">About</a> section.
All content on this site is available under <a href="https://creativecommons.org/share-your-work/public-domain/cc0/">CC0-1.0</a> unless otherwise specified.</p>
</div>
</div>
<div class="row">
<div class="col-lg-12" style="text-align:center">
<ul class="contact-info">
<li><i class="fa fa-github"></i><a href="https://github.com/usegalaxy-eu/website/tree/master/index-ml.md">Edit this page on GitHub</a></li>
<li><i class="fa fa-envelope"></i><a href="mailto:contact@usegalaxy.eu">contact@usegalaxy.eu</a></li>
<li><i class="fa fa-github"></i><a href="https://github.com/usegalaxy-eu">usegalaxy-eu</a></li>
<li><img class="fa-mastodon" src="/assets/media/mastodon.svg" style="width:18px;height:18px;padding-right:4px;filter:grayscale(100%);-webkit-filter:grayscale(100%);"/><a href="https://bawü.social/@galaxyfreiburg">galaxyfreiburg</a></li>
<li><i class="fa fa-rss"></i>Subscribe <a href="/feed.xml">via RSS (UseGalaxy.eu Feed)</a></li>
</ul>
</div>
</div>
</div>
</footer>
<script async defer data-domain="galaxyproject.eu" src="https://plausible.galaxyproject.eu/js/plausible.js"></script>
</body>
</html>