-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
338 lines (292 loc) · 17.1 KB
/
index.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
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="description" content="Tanevski Lab - Computational biomedical discovery" />
<title>Tanevski Lab - Computational biomedical discovery</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="author" content="Jovan Tanevski">
<link rel="stylesheet" href="css/normalize.css">
<link rel="stylesheet" href="css/skeleton.css">
<link rel="stylesheet" href="css/custom.css">
<link rel="icon" type="image/png" href="images/favicon.png">
</head>
<body>
<header class="container sticky">
<div class="row">
<div class="seven columns">
<a href="index.html" style="text-decoration: none; color: #000000;">
<h5>computational<font color="#99001A">biomedical</font><font color="#003399">discovery</font></h5>
</a>
</div>
<div class="five columns" style="word-spacing: 0.75rem; text-align: right;">
<a href="#main">Research</a>
<a href="#people">People</a>
<a href="#tools">Tools</a>
<a href="#publications">Publications</a>
<a href="#positions">Positions</a>
</div>
</div>
</header>
<div class="container" style="padding-bottom: 2rem;">
<div class="row" style="text-align: center;">
<h3>Tanevski Lab</h3>
<img src="images/logo.png" style="width: 15%;">
<p id="contact" style="font-size: 85%; line-height: 1.3rem;">
Institute for Computational Biomedicine
<br/>
Heidelberg University Hospital
<br/>
Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany
<br/>
<a>contact<at>tanevskilab.org</a>
</p>
</div>
</div>
<div class="container">
<div class="row">
<div class="nine columns">
<div id="main" class="boxin">
<h5>Research</h5>
<p>
<b>Our group focuses on problem driven development of AI/ML approaches to data exploration, hypothesis generation and computational scientific discovery to facilitate translational biomedicine.</b>
</p>
<p >
We currently focus on representation learning and (un-)supervised analysis of highly multiplexed spatial omics data. We develop new and extend existing explainable, scalable and readily deployable methods for multi-view learning, graph neural networks, metaheuristic optimization and optimal transport to:
</p>
<ul>
<li>
Identify clinically relevant regions and interactions by explanatory modeling and optimization of global and local tissue/condition specific persistent multicellular patterns.
</li>
<li>
Learn higher order structural and functional organization to form taxonomical models of tissues for comparative analyses and generation of in-silico samples.
</li>
<li>
Integrate multiomics data with databases of prior knowledge to discover context specific mechanistic insighs spanning multiple omics layers.
</li>
</ul>
<p>
Our interest is to address questions of structure-function relationships in disease, progression and response to treatment.
</p>
<p>
We value collaborations with clinical, experimental biology groups and groups working on the development of novel methods for the acquisition of spatially resolved data. We welcome synergistic collaborations with computational groups towards the construction of more robust theoretical and computational frameworks for the analysis of all aspects of biomedical data and beyond.
</p>
</div>
<div id="tools" class="boxin">
<h5>Tools</h5>
<div class="row">
<div class="three columns">
<img class="full" src="images/misty_badge.png">
<p>
<b>MISTy</b>
<br/>
🔗 <a href="https://saezlab.github.io/mistyR/" target="_blank">R package and page</a>
<br/>
📄 <a href="https://doi.org/10.1186/s13059-022-02663-5" target="_blank">Tanevski et al. 2022 Genome Biology</a>
</p>
</div>
<div class="nine columns">
<p>
The Multiview Intercellular SpaTial modeling framework (MISTy) is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views describing a different spatial or functional context such as intracellular or broader tissue structure, cell-type composition, functional footprints or anatomical regions.
</p>
</div>
</div>
<hr>
<div class="row">
<div class="three columns">
<img class="full" src="images/kasumi_badge.png">
<p>
<b>Kasumi</b>
<br/>
🔗 <a href="https://github.com/jtanevski/kasumi" target="_blank">R package</a>
<br/>
📄 <a href="https://doi.org/10.1101/2024.03.06.583691" target="_blank">Tanevski et al. 2024 bioRxiv</a>
</p>
</div>
<div class="nine columns">
<p>
Kasumi is a method for the identification of spatially localized neighborhoods of intra- and intercellular relationships, persistent across samples and conditions. Kasumi learns compressed explainable representations of spatial omics samples while preserving relevant biological signals that are readily deployable for data exploration and hypothesis generation, facilitating translational tasks.
</p>
</div>
</div>
<hr>
<div class="row">
<div class="three columns">
<img class="full" src="images/DOT_icon.png">
<p>
<b>DOT</b>
<br/>
🔗 <a href="https://saezlab.github.io/DOT/" target="_blank">R package and page</a>
<br/>
📄 <a href="https://doi.org/10.1038/s41467-024-48868-z" target="_blank">Rahimi et al. 2024 Nature Communications</a>
</p>
</div>
<div class="nine columns">
<p>
DOT is a method for transferring cell features from a reference single-cell RNA-seq data to spots/cells in spatial omics. It operates by optimizing a combination of multiple objectives using a Frank-Wolfe algorithm to produce a high quality transfer. Apart from transferring cell types/states to spatial omics, DOT can be used for transferring other relevant categorical or continuous features from one set of omics to another, such as estimating the expression of missinng genes or transferring transcription factor/pathway activities.
</p>
</div>
</div>
</div>
</div>
<div id="people" class="three columns boxin">
<h5>People</h5>
<p><b>Group Leader</b></p>
<div style="text-align: center;">
<img class="round" src="images/jovan.jpg" style="object-position: 50% 10%;">
<p>
<a href="people.html#jovan">
Jovan Tanevski
</a>
</p>
</div>
<p><b>PhD Students</b></p>
<div style="text-align: center;">
<img class="round" src="images/sebastian.jpg">
<p>
<a href="people.html#sebastian">
Sebastian Gonzalez Tirado
</a>
</p>
</div>
<p><b>Associated Postdocs</b></p>
<div style="text-align: center;">
<img class="round" src="images/chang.jpg">
<p>
<a href="people.html#chang">
Chang Lu
</a>
</p>
</div>
<p><b>Associated PhD Students</b></p>
<div style="text-align: center;">
<img class="round" src="images/philipp.jpg">
<p>
<a href="people.html#philipp">
Philipp Schaefer
</a>
</p>
</div>
<div style="text-align: center;">
<img class="round" src="images/robin.jpg">
<p>
<a href="people.html#robin">
Robin Fallegger
</a>
</p>
</div>
<div style="text-align: center;">
<img class="round" src="images/chiara.jpg">
<p>
<a href="people.html#chiara">
Chiara Schiller
</a>
</p>
</div>
<p><b>Join us</b></p>
<div style="text-align: center;">
<img class="round" src="images/anon.jpg">
<p>
<a href="#positions">
Recruiting now
</a>
</p>
</div>
<div style="text-align: left;">
<a class="button button-primary" href="people.html">Learn more</a>
</div>
</div>
</div>
</div>
<div id="publications" class="container">
<div class="row boxin">
<h5>Publications</h5>
Tanevski, J., Vuillard, L., Hartmann, F., Saez-Rodriguez, J. <a href="https://doi.org/10.1101/2024.03.06.583691" target="_blank">Learning tissue representation by identification of persistent local patterns in spatial omics data</a>. bioRxiv:2024.03.06.583691 (2024).
<br/>
Rahimi, A., Vale-Silva, L.A., Faelth Savitski, M., Tanevski, J., Saez-Rodriguez, J. <a href="https://doi.org/10.1038/s41467-024-48868-z" target="_blank">DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics</a>. Nature Communications 15 (2024).
<br/>
Dimitrov, D., Schäfer, P.S.L., Farr, E., et al. <a href="https://doi.org/10.1038/s41556-024-01469-w" target="_blank">LIANA+ provides an all-in-one framework for cell–cell communication inference</a>. Nature Cell Biology (2024).
<br/>
Laury, A. R., Zheng, S., Aho, N. et al. <a href="https://doi.org/10.1016/j.modpat.2024.100508" target ="_blank">Opening the black box: spatial transcriptomics and the relevance of AI-detected prognostic regions in high grade serous carcinoma</a>. Modern Pathology 100508 (2024).
<br/>
Paton, V., Gabor, A., Ramirez Flores, R.O. et al. <a href="https://doi.org/10.1093/nar/gkae552" target="_blank">Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results</a>. Nucleic Acids Research (2024).
<br/>
Heumos, L., Schaar, A.C., Lance, C. et al. <a href="https://doi.org/10.1038/s41576-023-00586-w" target="_blank">Best practices for single-cell analysis across modalities</a>. Nature Reviews Genetics 24, 550–572 (2023).
<br/>
Tanevski, J., Ramirez Flores, R.O., Gabor, A. et al. <a href="https://doi.org/10.1186/s13059-022-02663-5" target="_blank">Explainable multiview framework for dissecting spatial relationships from highly multiplexed data</a>. Genome Biology 23, 97 (2022).
<br/>
Kuppe, C., Ramirez Flores, R.O., Li, Z. et al. <a href="https://doi.org/10.1038/s41586-022-05060-x" target="_blank">Spatial multi-omic map of human myocardial infarction</a>. Nature 608, 766–777 (2022).
<br/>
Gabor, A., Tognetti, M., Driessen, A., Tanevski, J. et al. <a href="https://doi.org/10.15252/msb.202110402" target="_blank">Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd</a>. Molecular Systems Biology, 17(10), e10402 (2021).
<br/>
Schwabenland, M., Salié, H., Tanevski, J. et al. <a href="https://doi.org/10.1016/j.immuni.2021.06.002" target="_blank">Deep spatial profiling of human COVID-19 brains reveals neuroinflammation with distinct microanatomical microglia-T-cell interactions</a>. Immunity 54(70), 1594-1610.e11 (2021)
<br/>
Holland, C.H., Tanevski, J., Perales-Patón, J. et al. <a href="https://doi.org/10.1186/s13059-020-1949-z" target="_blank">Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data</a>. Genome Biology 21, 36 (2020).
<br/>
Tanevski, J., Nguyen, T., Truong, B. et al. <a href="https://doi.org/10.26508/lsa.202000867" target="_blank">Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data</a>. Life Science Alliance 3 (11), e202000867 (2020).
<br/>
Tanevski, J., Todorovski, L., Džeroski, S. <a href="https://doi.org/10.1016/j.engappai.2019.103423" target="_blank">Combinatorial search for selecting the structure of models of dynamical systems with equation discovery</a>. Engineering Applications of Artificial Intelligence 89, 103423 (2020).
<br/>
Tanevski, J., Todorovski, L., Džeroski, S. <a href="https://doi.org/10.1038/srep34107" target="_blank">Process-based design of dynamical biological systems</a>. Scientific Reports 6, 34107 (2016).
<br/>
Tanevski, J., Todorovski, L., Džeroski, S. <a href="https://doi.org/10.1186/s12918-016-0273-4" target="_blank">Learning stochastic process-based models of dynamical systems from knowledge and data</a>. BMC Systems Biology 10, 30 (2016).
</div>
</div>
<div id="positions" class="container">
<div class="row boxin">
<h5>Positions</h5>
<h6>Interns/Master theses</h6>
<p>
We offer opportunities for internships and supervision of master theses. We recommend that the duration of the internships is no shorter than three months. To inquire and apply write to <a>contact<at>tanevskilab.org</a>. Please include a CV, information on relevant courses attended during bachelor and/or master studies as well as well as a cover letter describing your more specific interests in the work done in our lab.
</p>
<!-- <h6>PhD</h6>
<p>
We are looking for a highly motivated and talented PhD student to join the newly created Translational Spatial Profiling Center at the Heidelberg University Hospital, managed by the Institute for Computational Biomedicine and the Institute for Pathology.
</p>
<p>
In a dynamic and multidisciplinary environment, the candidate will work on the development of novel methods for analysis of state-of-the-art spatially resolved data. The candidate will explore the landscape of explainable machine-learning and optimization approaches and create novel approaches that can be applied in a clinical setting. The work will be motivated by problems arising from a range of translational applications, initially focusing on cancer research, and will be directly supported by data generated within the center.
</p>
The candidate should:
<ul>
<li>Have either a degree in computer science, engineering sciences, physics or mathematics, or a degree in biological sciences or medicine with previous experience in computational work.</li>
<li>Be motivated to work on challenging interdisciplinary project with biomedical relevance.</li>
<li>Have a strong interest and/or knowledge about histo(patho)logy and cancer biology.</li>
<li>Have experience programming in R or Python.</li>
<li>Have good English communication skills.</li>
</ul>
<hr> -->
<h6>Postdoc</h6>
<p>
We are looking for a postdoctoral researcher for a 2 year project on the topic of Identifying the clinical relevance of higher order tissue organization in the tumor microenvironment from spatial multiplexed omics data. The position is offered as part of the Health + Life Science Alliance Inter-institutional Postdoc Program</a>. The position will be shared between the Tanevski Lab at the Heidelberg University Hospital and the <a href="https://www.dkfz.de/en/systemimmunologie-und-einzelzell-biologie/index.php">Hartmann lab</a> at DKFZ.
</p>
<p>
In this project, we are leveraging the MIBI (multiplexed ion beam imaging) platform established in the Hartmann lab, combined with the experience of the Tanevski Lab in representation learning, optimization and graph-based approaches. Our aim is to identify the different levels of structural and functional organization in tumors from colorectal carcinoma and breast cancer patients to better understand how cellular behavior impacts therapeutic responses and clinical outcomes. Beyond the immediate cellular neighborhood, we will expand the analysis both toward the broader tumor microenvironment and toward capturing higher-level organizational patterns.
</p>
The candidate should:
<ul>
<li>Have either a degree in computer science, engineering sciences, physics or mathematics, or a degree in biological sciences or medicine with previous experience in computational work.</li>
<li>Have experience programming in R or Python.</li>
<li>Interest in spatial omics and cancer biology.</li>
</ul>
<hr>
For all PhD and Postdoc postions we offer:
<ul>
<li>Work contract and funding according to TV-L with all corresponding social benefits.</li>
<li>Stimulating and supporting interdisciplinary research environment with access to international research networks.</li>
<li>Access to state-of-the-art spatial omics and clinical data.</li>
<li>Access to high performance computing infrastructure.</li>
<li>Access to further training opportunities offered by the Heidelberg University and Heidelberg University Hospital.</li>
</ul>
<p>
To apply please submit a <b>letter of motivation tailored to the position</b> (1 page), <b>CV</b> and a <b>list of references with contact details</b> to <a>contact<at>tanevskilab.org</a>.
</p>
</div>
</div>
<footer class = "container">
<div class="row" style="text-align: center;">
<hr>
© 2024 Tanevski Lab
</div>
</footer>
</body>
</html>