hms-dbmi · manzt · Oct 3, 2024 · Oct 1, 2024 · Oct 1, 2024 · Oct 1, 2024
diff --git a/.gitignore b/.gitignore
@@ -15,4 +15,5 @@ _site/
 .bundle/
 vendor/
 
-
+deno.json
+deno.lock
diff --git a/_includes/publication-row.html b/_includes/publication-row.html
@@ -4,10 +4,9 @@
 <a class="display-lg-only" href="{{publication.url}}"><img alt='Publication page for "{{ publication.title }}"' class="thumb" src="/assets/img/publications/thumbnail/{{ publication.image }}"></a>
 {% endif %}
 </td>
-<td markdown="1">
-<p>{{ publication.cite.authors }}.</p>
-<p>"<a class="link-title" href="{{publication.url}}">{{ publication.title }}</a>"</p>
-<p style="word-break: break-word;"  markdown="1">{{ publication.cite.published }}</p>
-<p>({{ publication.year }})</p>
+<td>
+{% capture doi %}{% if publication.doi and publication.publisher %} doi:[{{ publication.doi }}]({{ publication.publisher }}){% endif %}{% endcapture %}
+{% capture citation %}{{ publication.cite.authors }}. "[{{ publication.title }}]({{ publication.url }})", {{ publication.cite.published }} ({{ publication.year }}).{{ doi }}{% endcapture %}
+<p style="text-wrap: balance">{{ citation | markdownify }}</p>
 </td>
-</tr>
+</tr>
diff --git a/_layouts/publication.html b/_layouts/publication.html
@@ -18,7 +18,8 @@ <h3>Abstract</h3>
     {% endif %}
 
     <h3>Citation</h3>
-    {% capture citation %}{{ page.cite.authors }}. “{{ page.title }}” {{ page.cite.published }} ({{ page.year }}).{% endcapture %}
+    {% capture doi %}{% if page.doi and page.publisher %} doi:[{{ page.doi }}]({{ page.publisher }}){% endif %}{% endcapture %}
+    {% capture citation %}{{ page.cite.authors }}. "{{ page.title }}", {{ page.cite.published }} ({{ page.year}}).{{ doi }}{% endcapture %}
     {{ citation | markdownify }}
     <!-- If we markdownify just one field, it is wrapped by <p> -->
 

diff --git a/_publications/behrisch-arxiv-2018.md b/_publications/behrisch-arxiv-2018.md
@@ -9,9 +9,12 @@ members:
 year: 2018
 type: article
 
-publisher: "https://arxiv.org/abs/1807.01364"
+publisher: "https://ieeexplore.ieee.org/document/8534028/"
+doi: "10.1109/BDVA.2018.8534028"
+zotero-key: "JQA9HF8T"
+preprint: "https://arxiv.org/abs/1807.01364"
 cite:
   authors: "M Behrisch, R Krueger, F Lekschas, T Schreck, N Gehlenborg, H Pfister"
-  published: "*Proceedings of International Symposium on Big Data Visual and Immersive Analytics*, doi:10.1109/BDVA.2018.8534028"
+  published: "*Proceedings of International Symposium on Big Data Visual and Immersive Analytics (BDVA)*"
 ---
 Pattern extraction algorithms are enabling insights into the ever-growing amount of today's datasets by translating reoccurring data properties into compact representations. Yet, a practical problem arises: With increasing data volumes and complexity also the number of patterns increases, leaving the analyst with a vast result space. Current algorithmic and especially visualization approaches often fail to answer central overview questions essential for a comprehensive understanding of pattern distributions and support, their quality, and relevance to the analysis task. To address these challenges, we contribute a visual analytics pipeline targeted on the pattern-driven exploration of result spaces in a semi-automatic fashion. Specifically, we combine image feature analysis and unsupervised learning to partition the pattern space into interpretable, coherent chunks, which should be given priority in a subsequent in-depth analysis. In our analysis scenarios, no ground-truth is given. Thus, we employ and evaluate novel quality metrics derived from the distance distributions of our image feature vectors and the derived cluster model to guide the feature selection process. We visualize our results interactively, allowing the user to drill down from overview to detail into the pattern space and demonstrate our techniques in a case study on biomedical genomic data.
diff --git a/_publications/bourgeois-2021-jama-network-open.md b/_publications/bourgeois-2021-jama-network-open.md
@@ -8,7 +8,9 @@ members:
 type: article
 year: 2021
 publisher: "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2780925"
+doi: "10.1001/jamanetworkopen.2021.12596"
+zotero-key: "YT6TJYVC"
 cite:
-  authors: "Bourgeois F, Gutiérrez-Sacristán A, Keller M, Liu M, Hong C, Bonzel C, Tan A, Aronow B, Boeker M, Booth J, Cruz Rojo J, Devkota B, García Barrio N, Gehlenborg N, Geva A, Hanauer D, Hutch M, Issitt R, Klann J, Luo Y, Mandl K, Mao C, Moal B, Moshal K, Murphy S, Neuraz A, Yuan Ngiam K, Omenn G, Patel L, Pedrera Jiménez M, Sebire N, Serrano Balazote P, Serret-Larmande A, South A, Spiridou A, Taylor D, Tippmann P, Visweswaran S, Weber G, Kohane I, Cai T, Avillach P; for the Consortium for Clinical Characterization of COVID-19 by EHR (4CE)"
-  published: "*JAMA Netw Open* **4**:e2112596"
+  authors: "FT Bourgeois, A Gutiérrez-Sacristán, MS Keller, M Liu, C Hong, CL Bonzel, ALM Tan, BJ Aronow, M Boeker, J Booth, J Cruz-Rojo, B Devkota, N García-Barrio, N Gehlenborg, A Geva, DA Hanauer, MR Hutch, RW Issitt, JG Klann, Y Luo, KD Mandl, C Mao, B Moal, KL Moshal, SN Murphy, A Neuraz, KY Ngiam, GS Omenn, LP Patel, M Pedrera-Jiménez, NJ Sebire, P Serrano-Balazote, A Serret-Larmande, AM South, A Spiridou, DM Taylor, P Tippmann, S Visweswaran, GM Weber, IS Kohane, T Cai, P Avillach, *Consortium for Clinical Characterization of COVID-19 by EHR (4CE)*"
+  published: "*JAMA Network Open* **4**(6):e2112596"
 ---
diff --git a/_publications/conway-2017-biorxiv.md b/_publications/conway-2017-biorxiv.md
@@ -10,8 +10,10 @@ year: 2017
 type: article
 
 publisher: "https://academic.oup.com/bioinformatics/article/33/18/2938/3884387"
+doi: "10.1093/bioinformatics/btx364"
+zotero-key: "LY8MQVHZ"
 cite:
   authors: "JR Conway, A Lex, N Gehlenborg"
-  published: "*Bioinformatics* **33**: 2938–2940; doi: https://doi.org/10.1093/bioinformatics/btx364"
+  published: "*Bioinformatics* **33**(18):2938-2940"
 ---
 Venn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets and their properties is needed. We developed UpSetR, an open source R package that employs a scalable matrix-based visualization to show intersections of sets, their size, and other properties. UpSetR is available at https://cran.r-project.org/package=UpSetR and released under the MIT License. A Shiny app is available at https://gehlenborglab.shinyapps.io/upsetr.
diff --git a/_publications/gehlenborg-2003-studienarbeit.md b/_publications/gehlenborg-2003-studienarbeit.md
diff --git a/_publications/gehlenborg-2005-information-visualization.md b/_publications/gehlenborg-2005-information-visualization.md
@@ -5,8 +5,10 @@ members:
   - nils-gehlenborg
 type: article
 year: 2005
-publisher: "http://dx.doi.org/10.1057/palgrave.ivs.9500094"
+publisher: "https://journals.sagepub.com/doi/10.1057/palgrave.ivs.9500094"
+doi: "10.1057/palgrave.ivs.9500094"
+zotero-key: "TDL2SKXT"
 cite:
-  authors: "N Gehlenborg, J Dietzsch and K Nieselt"
+  authors: "N Gehlenborg, J Dietzsch, K Nieselt"
   published: "*Information Visualization* **4**(3):164-175"
 ---
diff --git a/_publications/gehlenborg-2005-softwareforschung-dna.md b/_publications/gehlenborg-2005-softwareforschung-dna.md
@@ -6,6 +6,7 @@ members:
 type: other
 year: 2005
 publisher: "http://www.amazon.de/Aktuelle-Trends-Softwareforschung-Tagungsband-Software-Forschungstag/dp/3898644138"
+zotero-key: "WP3GEH5D"
 cite:
   authors: "N Gehlenborg"
   published: "in D. Spath, K. Haasis and D. Klumpp (Eds.), Aktuelle Trends in der Softwareforschung (Vol. 3), 259-270, IRB Fraunhofer Verlag, Stuttgart, Germany"

diff --git a/_publications/gehlenborg-2006-bioinformatics-mayday.md b/_publications/gehlenborg-2006-bioinformatics-mayday.md
@@ -5,8 +5,10 @@ members:
   - nils-gehlenborg
 type: article
 year: 2006
-publisher: "http://dx.doi.org/10.1093/bioinformatics/btl070"
+publisher: "https://academic.oup.com/bioinformatics/article/22/8/1010/227568"
+doi: "10.1093/bioinformatics/btl070"
+zotero-key: "B6ZLATNB"
 cite:
-  authors: "J Dietzsch\\*, N Gehlenborg\\* and K Nieselt"
+  authors: "J Dietzsch, N Gehlenborg, K Nieselt"
   published: "*Bioinformatics* **22**(8):1010-1012"
 ---
diff --git a/_publications/gehlenborg-2006-diplomarbeit.md b/_publications/gehlenborg-2006-diplomarbeit.md
@@ -6,6 +6,7 @@ members:
 type: thesis
 year: 2006
 publisher: "http://gehlenborg.com/wp-content/uploads/2011/12/nils-gehlenborg_masters.pdf"
+zotero-key: "3Q23X7NK"
 cite:
   authors: "N Gehlenborg"
   published: "*Diplomarbeit (eq. Masters Thesis)*, University of Tübingen, Germany"

diff --git a/_publications/gehlenborg-2007-bmc-highlights.md b/_publications/gehlenborg-2007-bmc-highlights.md
@@ -5,9 +5,11 @@ members:
   - nils-gehlenborg
 type: other
 year: 2007
-publisher: "http://dx.doi.org/10.1186/1471-2105-8-S8-I1"
+publisher: "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-S8-I1"
+doi: "10.1186/1471-2105-8-S8-I1"
+zotero-key: "EZ2IRDCJ"
 cite:
-  authors: "N Gehlenborg, M Corpas and S Chandra Janga (Eds.)"
-  published: "*BMC Bioinformatics* **8**(Suppl 8):I1"
+  authors: "N Gehlenborg, M Corpas, SC Janga"
+  published: "*BMC Bioinformatics* **8**(S8):I1"
 ---
 In this meeting report we give an overview of the 3rd International Society for Computational Biology Student Council Symposium. Furthermore, we explain the role of the Student Council and the symposium series in the context of large, international conferences.
diff --git a/_publications/gehlenborg-2008-bmc-highlights.md b/_publications/gehlenborg-2008-bmc-highlights.md
@@ -5,9 +5,11 @@ members:
   - nils-gehlenborg
 type: other
 year: 2008
-publisher: "http://dx.doi.org/10.1186/1471-2105-9-S10-I1"
+publisher: "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-S10-I1"
+doi: "10.1186/1471-2105-9-S10-I1"
+zotero-key: "S8XF2TKU"
 cite:
-  authors: "L Peixoto, N Gehlenborg and S Chandra Janga (Eds.)"
-  published: "*BMC Bioinformatics* **9**(Suppl 10):I1"
+  authors: "L Peixoto, N Gehlenborg, SC Janga"
+  published: "*BMC Bioinformatics* **9**(S10):I1"
 ---
 In this meeting report we give an overview of the talks and presentations from the Fourth International Society for Computational Biology (ISCB) Student Council Symposium held as part of the annual Intelligent Systems for Molecular Biology (ISMB) conference in Toronto, Canada. Furthermore, we detail the role of the Student Council (SC) as an international student body in organizing this symposium series in the context of large, international conferences.
diff --git a/_publications/gehlenborg-2008-plos-ten.md b/_publications/gehlenborg-2008-plos-ten.md
@@ -5,9 +5,11 @@ members:
   - nils-gehlenborg
 type: other
 year: 2008
-publisher: "http://dx.doi.org/10.1371/journal.pcbi.1000080"
+publisher: "https://dx.plos.org/10.1371/journal.pcbi.1000080"
+doi: "10.1371/journal.pcbi.1000080"
+zotero-key: "JB58LA24"
 cite:
-  authors: "M Corpas, N Gehlenborg, S Chandra Janga and PE Bourne"
+  authors: "M Corpas, N Gehlenborg, SC Janga, PE Bourne"
   published: "*PLoS Computational Biology* **4**(6):e1000080"
 ---
 Scientific meetings come in various flavors—from one-day focused workshops of 1–20 people to large-scale multiple-day meetings of 1,000 or more delegates, including keynotes, sessions, posters, social events, and so on. These ten rules are intended to provide insights into organizing meetings across the scale.
diff --git a/_publications/gehlenborg-2008-proteomics-peptide.md b/_publications/gehlenborg-2008-proteomics-peptide.md
@@ -5,9 +5,11 @@ members:
   - nils-gehlenborg
 type: article
 year: 2008
-publisher: "http://dx.doi.org/10.1074/mcp.M700498-MCP200"
+publisher: "https://linkinghub.elsevier.com/retrieve/pii/S1535947620308823"
+doi: "10.1074/mcp.M700498-MCP200"
+zotero-key: "PLX69EPR"
 cite:
-  authors: "A Schmidt, N Gehlenborg, B Bodenmiller, LN Mueller, D Campbell, M Mueller, R Aebersold and B Domon"
-  published: "*Molecular & Cellular Proteomics* **7**:2138-2150"
+  authors: "A Schmidt, N Gehlenborg, B Bodenmiller, LN Mueller, D Campbell, M Mueller, R Aebersold, B Domon"
+  published: "*Molecular & Cellular Proteomics* **7**(11):2138-2150"
 ---
 LC-MS/MS has emerged as the method of choice for the identification and quantification of protein sample mixtures. For very complex samples such as complete proteomes, the most commonly used LC-MS/MS method, data-dependent acquisition (DDA) precursor selection, is of limited utility. The limited scan speed of current mass spectrometers along with the highly redundant selection of the most intense precursor ions generates a bias in the pool of identified proteins toward those of higher abundance. A directed LC-MS/MS approach that alleviates the limitations of DDA precursor ion selection by decoupling peak detection and sequencing of selected precursor ions is presented. In the first stage of the strategy, all detectable peptide ion signals are extracted from high resolution LC-MS feature maps or aligned sets of feature maps. The selected features or a subset thereof are subsequently sequenced in sequential, non-redundant directed LC-MS/MS experiments, and the MS/MS data are mapped back to the original LC-MS feature map in a fully automated manner. The strategy, implemented on an LTQ-FT MS platform, allowed the specific sequencing of 2,000 features per analysis and enabled the identification of more than 1,600 phosphorylation sites using a single reversed phase separation dimension without the need for time-consuming prefractionation steps. Compared with conventional DDA LC-MS/MS experiments, a substantially higher number of peptides could be identified from a sample, and this increase was more pronounced for low intensity precursor ions. 
diff --git a/_publications/gehlenborg-2009-bioinformatics-microarray.md b/_publications/gehlenborg-2009-bioinformatics-microarray.md
@@ -5,9 +5,11 @@ members:
   - nils-gehlenborg
 type: article
 year: 2009
-publisher: "http://dx.doi.org/10.1093/bioinformatics/btp215 "
+publisher: "https://academic.oup.com/bioinformatics/article/25/12/i145/190680"
+doi: "10.1093/bioinformatics/btp215"
+zotero-key: "5NNFAAUE"
 cite:
-  authors: "J Caldas, N Gehlenborg, A Faisal, A Brazma and S Kaski"
+  authors: "J Caldas, N Gehlenborg, A Faisal, A Brazma, S Kaski"
   published: "*Bioinformatics* **25**(12):i145-i153"
 ---
 Motivation: As ArrayExpress and other repositories of genome-wide experiments are reaching a mature size, it is becoming more meaningful to search for related experiments, given a particular study. We introduce methods that allow for the search to be based upon measurement data, instead of the more customary annotation data. The goal is to retrieve experiments in which the same biological processes are activated. This can be due either to experiments targeting the same biological question, or to as yet unknown relationships.

diff --git a/_publications/gehlenborg-2009-bioinformatics-prequips.md b/_publications/gehlenborg-2009-bioinformatics-prequips.md
@@ -5,9 +5,11 @@ members:
   - nils-gehlenborg
 type: article
 year: 2009
-publisher: "http://dx.doi.org/10.1093/bioinformatics/btp005"
+publisher: "https://academic.oup.com/bioinformatics/article/25/5/682/182599"
+doi: "10.1093/bioinformatics/btp005"
+zotero-key: "LVZD2QBC"
 cite:
-  authors: "N Gehlenborg, W Yan, I Lee, H Yoo, K Nieselt, D Hwang, R Aebersold, L Hood"
+  authors: "N Gehlenborg, W Yan, IY Lee, H Yoo, K Nieselt, D Hwang, R Aebersold, L Hood"
   published: "*Bioinformatics* **25**(5):682-683"
 ---
 We describe an integrative software platform, Prequips, for comparative proteomics-based systems biology analysis that: (i) integrates all information generated from mass spectrometry (MS)-based proteomics as well as from basic proteomics data analysis tools, (ii) visualizes such information for various proteomic analyses via graphical interfaces and (iii) links peptide and protein abundances to external tools often used in systems biology studies.
diff --git a/_publications/gehlenborg-2009-bmc-probabilistic.md b/_publications/gehlenborg-2009-bmc-probabilistic.md
@@ -5,9 +5,11 @@ members:
   - nils-gehlenborg
 type: other
 year: 2009
-publisher: "http://www.biomedcentral.com/1471-2105/10/S13/P1"
+publisher: "https://academic.oup.com/bioinformatics/article/25/12/i145/190680"
+doi: "10.1093/bioinformatics/btp215"
+zotero-key: "5NNFAAUE"
 cite:
-  authors: "J Caldas, N Gehlenborg, A Faisal, A Brazma and S Kaski"
-  published: "*BMC Bioinformatics* **10**(Suppl 13):P1"
+  authors: "J Caldas, N Gehlenborg, A Faisal, A Brazma, S Kaski"
+  published: "*Bioinformatics* **25**(12):i145-i153"
 ---
 Repositories of genome-wide expression studies such as ArrayExpress [1] have been growing rapidly over the last few years and continue to do so. The more experimental data are deposited into these repositories, the more likely it becomes that some of them can provide a meaningful biological context to aid in the planning and analysis of new studies. Retrieval of experiments based on their textual description and experimental design has several shortcomings. First of all, textual description of an experiment or its results is not as information-rich as the actual data itself. Secondly, information about the experimental design alone is only of limited use in retrieving biologically relevant data because it does not reflect the results, which contain the bulk of the information and may reveal unexpected relationships. We introduce novel retrieval methods that incorporate the actual gene expression measurements into the search process, along with visualization tools for interpreting and exploring the results [2].
diff --git a/_publications/gehlenborg-2009-bmc-space.md b/_publications/gehlenborg-2009-bmc-space.md
@@ -5,9 +5,11 @@ members:
   - nils-gehlenborg
 type: other
 year: 2009
-publisher: "http://www.biomedcentral.com/1471-2105/10/S13/O7"
+publisher: "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-S13-O7"
+doi: "10.1186/1471-2105-10-S13-O7"
+zotero-key: "9HEPAIPW"
 cite:
   authors: "N Gehlenborg and A Brazma"
-  published: "*BMC Bioinformatics* **10**(Suppl 13):O7"
+  published: "*BMC Bioinformatics* **10**(S13):O7"
 ---
 Heatmaps and profile plots are effective techniques to visualize expression profiles of several hundred genes across a few dozen samples. However, these techniques do not scale to data sets with expression profiles that have been measured across several hundred samples or even thousands of samples. Our motivation to find a solution to this scaling problem is based on the observation that with increasingly mature and affordable microarray platforms, the number of studies in ArrayExpress [1] including hundreds of samples has been increasing steadily over the years.
diff --git a/_publications/gehlenborg-2009-database.md b/_publications/gehlenborg-2009-database.md
@@ -5,9 +5,11 @@ members:
   - nils-gehlenborg
 type: article
 year: 2009
-publisher: "http://dx.doi.org/10.1093/database/bap011"
+publisher: "https://academic.oup.com/database/article/doi/10.1093/database/bap011/356471"
+doi: "10.1093/database/bap011"
+zotero-key: "LXGWQWMY"
 cite:
-  authors: "N Gehlenborg, D Hwang\\*, IY Lee\\*, H Yoo, B Petritis, D Baxter, R Pitstick, B Marzolf, SJ DeArmond, GA Carlson and LE Hood"
-  published: "*Database* 2009:bap011"
+  authors: "N Gehlenborg, D Hwang, IY Lee, H Yoo, D Baxter, B Petritis, R Pitstick, B Marzolf, SJ DeArmond, GA Carlson, L Hood"
+  published: "*Database* **2009**"
 ---
 Prion diseases reflect conformational conversion of benign isoforms of prion protein (PrPC) to malignant PrPSc isoforms. Networks perturbed by PrPSc accumulation and their ties to pathological events are poorly understood. Time-course transcriptomic and phenotypic data in animal models are critical for understanding prion-perturbed networks in systems biology studies. Here, we present the Prion Disease Database (PDDB), the most comprehensive data resource on mouse prion diseases to date. The PDDB contains: (i) time-course mRNA measurements spanning the interval from prion inoculation through appearance of clinical signs in eight mouse strain-prion strain combinations and (ii) histoblots showing temporal PrPSc accumulation patterns in brains from each mouse–prion combination. To facilitate prion research, the PDDB also provides a suite of analytical tools for reconstructing dynamic networks via integration of temporal mRNA and interaction data and for analyzing these networks to generate hypotheses.