update publications of sehi

_members/sehi-lyi.md

@@ -8,9 +8,10 @@ job_title: Research Fellow in Biomedical Informatics
 role: postdoc
 
 services:
-  github: https://github.com/sehilyi
   home: http://sehilyi.com
+  github: https://github.com/sehilyi
   twitter: https://twitter.com/sehi_lyi
+  linked-in: https://www.linkedin.com/in/sehilyi/
 
 start: February 2020
 end:

_publications/gehlenborg-2023-nature-medicine.md

@@ -12,6 +12,6 @@ type: article
 publisher: "https://www.nature.com/nm/"
 cite:
   authors: "Harrison Zhang, Jacqueline Honerlaw, Monika Maripuri, Malarkodi Jebathilagam Samayamuthu, Brendin Beaulieu-Jones, Huma Baig, Sehi L'Yi, Yuk-Lam Ho, Michele Morris, Vidul Ayakulangara Panickan, Xuan Wang, Griffin Weber, Katherine P Liao, Shyam Visweswaran, Bryce Tan, William Yuan, Nils Gehlenborg, Sumitra Muralidhar, Rachel Ramoni, The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Isaac Kohane, Zongqi Xia, Kelly Cho, and Tianxi Cai"
-  published: "Nature Medicine, Accepted."
+  published: "*Nature Medicine* **29**, 1040–1043"
 ---
 

_publications/lyi-2023-chromoscope.md

@@ -0,0 +1,19 @@
+---
+title: "Chromoscope: interactive multiscale visualization for structural variation in human genomes"
+image: chromoscope.png
+
+members:
+  - sehi-lyi
+  - trevor-manz
+  - nils-gehlenborg
+
+year: 2023
+type: article
+
+publisher: "https://www.nature.com/articles/s41592-023-02056-x"
+cite:
+  authors: "S L'Yi, D Maziec, V Stevens, T Manz, A Veit, M Berselli, P J. Park, D Głodzik, N Gehlenborg"
+  published: "*Nature Methods* **20**, 1834–1835"
+
+---
+Whole genome sequencing is now routinely used to profile mutations in DNA in the soma and in the germline, informing molecular diagnoses of disease and therapeutic decisions. Structural variants (SVs) are the main new type of alterations we see more of, and they are often diagnostic, prognostic, or therapy-informing. However, the size and complexity of SV data, combined with the difficulty of obtaining accurate SV calls, pose challenges in the interpretation of SVs, requiring tedious visual inspection of potentially pathogenic variants with multiple visualization tools. To overcome the problems with the interpretation of SVs, we developed Chromoscope, an open-source web-based application for the interactive visualization of structural variants. Chromoscope has several innovative features which unlock the insights from whole genome sequencing: visualization at multiple scale levels simultaneously, effective navigation across scales;  easy setup for loading users' large datasets, and a feature to export, share, and further customize visualizations. We are hosting a freely available public instance of Chromoscope (https://chromoscope.bio) to showcase data from the ‘Pan-cancer Analysis of the Whole Genomes’ consortium, providing easy access to this reference dataset. We additionally facilitate the set up of visualizations for users' data.  We anticipate that Chromoscope will accelerate the exploration and interpretation of SVs by a broad range of scientists and clinicians, leading to new insights into genomic biomarkers.

_publications/lyi-2023-interview.md

@@ -0,0 +1,19 @@
+---
+title: "The Role of Visualization in Genomics Data Analysis Workflows: The Interview"
+image: 2023-interview.png
+
+members:
+  - sehi-lyi
+  - qianwen-wang
+  - nils-gehlenborg
+
+year: 2023
+type: article
+
+publisher: "https://doi.ieeecomputersociety.org/10.1109/VIS54172.2023.00029"
+cite:
+  authors: "S L'Yi, Q Wang, N Gehlenborg"
+  published: "Proceedings of IEEE VIS 2023, 101-105"
+
+---
+The diversity of genome-mapped data and analysis tasks makes it challenging for a single visualization tool to fulfill all visualization needs. To design a visualization tool that supports various genomics workflows of users, it is critical to first gain insights into the diverse workflows and the limitations of existing genomics tools for supporting them. In this paper, we conducted semi-structured interviews (N=9) to understand the role of visualization in genomics data analysis workflows. Our main goals were to identify various genomics workflows, from data analysis to visual exploration and presentation, and to observe challenges that genomics analysts encounter in these workflows when using existing tools. Through the interviews, we found several unique characteristics of genomics workflows, such as the use of multiple visualization tools and many repetitive tasks, which can significantly affect the overall performance. Based on our findings, we discuss implications for designing effective visualization authoring tools that tightly support genomics workflows, such as supporting automation and reproducibility.

_publications/lyi-2024-cistrome-data-browser.md

@@ -0,0 +1,18 @@
+---
+title: "Cistrome Data Browser: integrated search, analysis and visualization of chromatin data"
+image: cistrome-data-browser.png
+
+members:
+  - sehi-lyi
+  - nils-gehlenborg
+
+year: 2024
+type: article
+
+publisher: "https://doi.org/10.1093/nar/gkad1069"
+cite:
+  authors: "L Taing, A Dandawate, S L'Yi, N Gehlenborg, M Brown, C A Meyer"
+  published: "*Nucleic Acids Research* **52**(D1), D61-D66"
+
+---
+The Cistrome Data Browser is a resource of ChIP-seq, ATAC-seq and DNase-seq data from humans and mice. It provides maps of the genome-wide locations of transcription factors, cofactors, chromatin remodelers, histone post-translational modifications and regions of chromatin accessible to endonuclease activity. Cistrome DB v3.0 contains approximately 45,000 human and 44,000 mouse samples with about 32,000 newly collected datasets compared to the previous release. The Cistrome DB v3.0 user interface is implemented as a single page application that unifies menu driven and data driven search functions and provides an embedded genome browser, which allows users to find and visualize data more effectively. Users can find informative chromatin profiles through keyword, menu, and data-driven search tools. Browser search functions can predict the regulators of query genes as well as the cell type and factor dependent functionality of potential cis-regulatory elements. Cistrome DB v3.0 expands the display of quality control statistics, incorporates sequence logos into motif enrichment displays and includes more expansive sample metadata. Cistrome DB v3.0 is available at http://db3.cistrome.org/browser.

_publications/wang-2023-autogosling.md

@@ -10,12 +10,12 @@ members:
   - nils-gehlenborg
 
 year: 2023
-type: preprint
+type: article
 
-publisher: "https://doi.org/10.31219/osf.io/5zgye"
+publisher: "https://doi.ieeecomputersociety.org/10.1109/VIS54172.2023.00031"
 cite:
   authors: "Qianwen Wang, Xiao Liu, Man Qing Liang, Sehi L’Yi, Nils Gehlenborg"
-  published: "*OSF Preprints* doi:10.31219/osf.io/5zgye"
+  published: "Proceedings of IEEE VIS 2023, 101-105"
 
 ---
 

_publications/wang-2023-drava.md

@@ -0,0 +1,19 @@
+---
+title: "Drava: Concept-Driven Exploration of Small Multiples using Interpretable Latent Vectors"
+image: drava.png
+
+members:
+  - qianwen-wang
+  - sehi-lyi
+  - nils-gehlenborg
+
+year: 2023
+type: article
+
+publisher: "https://doi.org/10.1145/3544548.3581127"
+cite:
+  authors: "Q Wang, S L'Yi, N Gehlenborg"
+  published: "Proceedings of ACM CHI 2023, 1-15"
+
+---
+Latent vectors extracted by machine learning (ML) are widely used in data exploration (e.g., t-SNE) but suffer from a lack of interpretability. While previous studies employed disentangled representation learning (DRL) to enable more interpretable exploration, they often overlooked the potential mismatches between the concepts of humans and the semantic dimensions learned by DRL. To address this issue, we propose Drava, a visual analytics system that supports users in 1) relating the concepts of humans with the semantic dimensions of DRL and identifying mismatches, 2) providing feedback to minimize the mismatches, and 3) obtaining data insights from concept-driven exploration. Drava provides a set of visualizations and interactions based on visual piles to help users understand and refine concepts and conduct concept-driven exploration. Meanwhile, Drava employs a concept adaptor model to fine-tune the semantic dimensions of DRL based on user refinement. The usefulness of Drava is demonstrated through application scenarios and experimental validation.