Automated Author ProfileLong, Mark
Long, Mark
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 10.6 (sum of 14 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The AP Science Impact Study, which is funded by the National Science Foundation, seeks to understand the impact of Advanced Placement Biology and Chemistry classes on the high school students who take them. It examines the effects of the updated inquiry-based curriculum on students' confidence in scientific inquiry skills and their post-high school plans, including college type, selectivity, and major. This study has policy implications for science curriculum and the next generation STEM workforce. For more information, see https://evans.uw.edu/ap-science-impact-study
Authors
- Long, Mark ;
- Conger, Dylan ;
- McGhee, Raymond
The AP Science Impact Study, which is funded by the National Science Foundation, seeks to understand the impact of Advanced Placement Biology and Chemistry classes on the high school students who take them. It examines the effects of the updated inquiry-based curriculum on students' confidence in scientific inquiry skills and their post-high school plans, including college type, selectivity, and major. This study has policy implications for science curriculum and the next generation STEM workforce. For more information, see https://evans.uw.edu/ap-science-impact-study
Authors
- Long, Mark ;
- Conger, Dylan ;
- McGhee, Raymond
Table S1. Clinical demographics of treatment-naĂŻve (including both chemotherapy and immunotherapy) epithelial ovarian cancer patients at the time of primary debulking surgery. Patient identification number, age at diagnosis, tumor histologic type, FIGO stage, debulking status, residual tumor mass after debulking surgery, number of recurrences after primary debulking surgery, and RECIST to frontline chemotherapy after primary debulking surgery. (XLSX 10 kb)
Authors
- Liu, Song ;
- Matsuzaki, Junko ;
- Wei, Lei ;
- Tsuji, Takemasa ;
- Battaglia, Sebastiano ;
- Hu, Qiang ;
- Cortes, Eduardo ;
- Wong, Laiping ;
- Yan, Li ;
- Long, Mark ;
- Miliotto, Anthony ;
- Bateman, Nicholas ;
- Lele, Shashikant ;
- Chodon, Thinle ;
- Koya, Richard ;
- Yao, Song ;
- Zhu, Qianqian ;
- Conrads, Thomas ;
- Wang, Jianmin ;
- Maxwell, George ;
- Lugade, Amit ;
- Odunsi, Kunle
Table S2. Somatic point mutations identified from whole-exome sequencing. (a) The 18 patients with either primary tumor or locally invasive tumor; (b) The 2 patients with both primary tumor and locally invasive tumor. AA, amino acid; CGC, cancer gene census; VAF, variant allele frequency. (XLSX 244 kb)
Authors
- Liu, Song ;
- Matsuzaki, Junko ;
- Wei, Lei ;
- Tsuji, Takemasa ;
- Battaglia, Sebastiano ;
- Hu, Qiang ;
- Cortes, Eduardo ;
- Wong, Laiping ;
- Yan, Li ;
- Long, Mark ;
- Miliotto, Anthony ;
- Bateman, Nicholas ;
- Lele, Shashikant ;
- Chodon, Thinle ;
- Koya, Richard ;
- Yao, Song ;
- Zhu, Qianqian ;
- Conrads, Thomas ;
- Wang, Jianmin ;
- Maxwell, George ;
- Lugade, Amit ;
- Odunsi, Kunle
Table S2. Somatic point mutations identified from whole-exome sequencing. (a) The 18 patients with either primary tumor or locally invasive tumor; (b) The 2 patients with both primary tumor and locally invasive tumor. AA, amino acid; CGC, cancer gene census; VAF, variant allele frequency. (XLSX 244 kb)
Authors
- Liu, Song ;
- Matsuzaki, Junko ;
- Wei, Lei ;
- Tsuji, Takemasa ;
- Battaglia, Sebastiano ;
- Hu, Qiang ;
- Cortes, Eduardo ;
- Wong, Laiping ;
- Yan, Li ;
- Long, Mark ;
- Miliotto, Anthony ;
- Bateman, Nicholas ;
- Lele, Shashikant ;
- Chodon, Thinle ;
- Koya, Richard ;
- Yao, Song ;
- Zhu, Qianqian ;
- Conrads, Thomas ;
- Wang, Jianmin ;
- Maxwell, George ;
- Lugade, Amit ;
- Odunsi, Kunle
Table S3. Somatic mutation burdens and predicted neoantigen load in the 20 patients. The predicted neoantigens are classified as expressed or non-expressed based on the mutant alleleâ s expression level in RNAseq data (see Method section). (XLSX 10 kb)
Authors
- Liu, Song ;
- Matsuzaki, Junko ;
- Wei, Lei ;
- Tsuji, Takemasa ;
- Battaglia, Sebastiano ;
- Hu, Qiang ;
- Cortes, Eduardo ;
- Wong, Laiping ;
- Yan, Li ;
- Long, Mark ;
- Miliotto, Anthony ;
- Bateman, Nicholas ;
- Lele, Shashikant ;
- Chodon, Thinle ;
- Koya, Richard ;
- Yao, Song ;
- Zhu, Qianqian ;
- Conrads, Thomas ;
- Wang, Jianmin ;
- Maxwell, George ;
- Lugade, Amit ;
- Odunsi, Kunle
Table S4. Description of the 75 neopeptides screened for immunogenicity. The expression status is based on the mutant alleleâ s expression level in RNAseq data, and affinity score is predicted by NetMHC algorithm with default setting (see Method section). (XLSX 15 kb)
Authors
- Liu, Song ;
- Matsuzaki, Junko ;
- Wei, Lei ;
- Tsuji, Takemasa ;
- Battaglia, Sebastiano ;
- Hu, Qiang ;
- Cortes, Eduardo ;
- Wong, Laiping ;
- Yan, Li ;
- Long, Mark ;
- Miliotto, Anthony ;
- Bateman, Nicholas ;
- Lele, Shashikant ;
- Chodon, Thinle ;
- Koya, Richard ;
- Yao, Song ;
- Zhu, Qianqian ;
- Conrads, Thomas ;
- Wang, Jianmin ;
- Maxwell, George ;
- Lugade, Amit ;
- Odunsi, Kunle
Table S4. Description of the 75 neopeptides screened for immunogenicity. The expression status is based on the mutant alleleâ s expression level in RNAseq data, and affinity score is predicted by NetMHC algorithm with default setting (see Method section). (XLSX 15 kb)
Authors
- Liu, Song ;
- Matsuzaki, Junko ;
- Wei, Lei ;
- Tsuji, Takemasa ;
- Battaglia, Sebastiano ;
- Hu, Qiang ;
- Cortes, Eduardo ;
- Wong, Laiping ;
- Yan, Li ;
- Long, Mark ;
- Miliotto, Anthony ;
- Bateman, Nicholas ;
- Lele, Shashikant ;
- Chodon, Thinle ;
- Koya, Richard ;
- Yao, Song ;
- Zhu, Qianqian ;
- Conrads, Thomas ;
- Wang, Jianmin ;
- Maxwell, George ;
- Lugade, Amit ;
- Odunsi, Kunle
Table S5. The list of 31 genes within the derived APPM signature (see Method section). (XLSX 11 kb)
Authors
- Liu, Song ;
- Matsuzaki, Junko ;
- Wei, Lei ;
- Tsuji, Takemasa ;
- Battaglia, Sebastiano ;
- Hu, Qiang ;
- Cortes, Eduardo ;
- Wong, Laiping ;
- Yan, Li ;
- Long, Mark ;
- Miliotto, Anthony ;
- Bateman, Nicholas ;
- Lele, Shashikant ;
- Chodon, Thinle ;
- Koya, Richard ;
- Yao, Song ;
- Zhu, Qianqian ;
- Conrads, Thomas ;
- Wang, Jianmin ;
- Maxwell, George ;
- Lugade, Amit ;
- Odunsi, Kunle
Table S5. The list of 31 genes within the derived APPM signature (see Method section). (XLSX 11 kb)
Authors
- Liu, Song ;
- Matsuzaki, Junko ;
- Wei, Lei ;
- Tsuji, Takemasa ;
- Battaglia, Sebastiano ;
- Hu, Qiang ;
- Cortes, Eduardo ;
- Wong, Laiping ;
- Yan, Li ;
- Long, Mark ;
- Miliotto, Anthony ;
- Bateman, Nicholas ;
- Lele, Shashikant ;
- Chodon, Thinle ;
- Koya, Richard ;
- Yao, Song ;
- Zhu, Qianqian ;
- Conrads, Thomas ;
- Wang, Jianmin ;
- Maxwell, George ;
- Lugade, Amit ;
- Odunsi, Kunle