🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge

Emiel A. M. Janssen

Griffith University

ORCID: 0000-0002-7760-5396

Also affiliated with Stavanger University Hospital, University of Stavanger

Biochemistry, Genetics and Molecular BiologyComputer ScienceMedicine

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile
0Author DataRank

Indexed papers

0in pilot corpus
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 9 papers. Top paper: “A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images”.

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images

Bioinformatics(2022)10.1093/bioinformatics/btac558
N/A
0DataRank · unranked
48 citations

Shristi Bhattarai, Hongxiao Li, Zhan Shi, Fusheng Wang, George Teodoro +7 more

Quantitative proteomics landscape and association with BASP1 and breast cancer metastasis.

Journal of Clinical Oncology(2022)10.1200/jco.2022.40.16_suppl.1090
N/A
0DataRank · unranked
3 citations

Marie Austdal, Sung Yun Jung, Emiel A. M. Janssen, Håvard Søiland, Ritu Aneja +1 more

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer

The Journal of Pathology(2023)10.1002/path.6155
N/A
0DataRank · unranked
53 citations

Glenn Broeckx, David B. Page, Chowdhury Arif Jahangir, Sara Verbandt, Zuzana Kos +95 more

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer

The Journal of Pathology(2023)10.1002/path.6165
N/A
0DataRank · unranked
51 citations

Glenn Broeckx, Chowdhury Arif Jahangir, Sara Verbandt, Rajarsi Gupta, Jeppe Thagaard +95 more

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.04.17.536459
N/A
0DataRank · unranked
10 citations

Geetanjali Saini, Hongxiao Li, Hongyi Duanmu, Gaurav Seth, Timothy B. Fisher +5 more

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Research Square(2023)10.21203/rs.3.rs-3243195/v1
N/A
0DataRank · unranked
0 citations

Geetanjali Saini, Rekha TS, Jayashree Krishnamurthy, Shristi Bhattarai, Grace Callagy +5 more

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Breast Cancer Research(2024)10.1186/s13058-023-01752-y
N/A
0DataRank · unranked
36 citations

Geetanjali Saini, T. S. Rekha, Jayashree Krishnamurthy, Shristi Bhattarai, Grace Callagy +5 more

Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer

The Journal of Pathology(2024)10.1002/path.6238
N/A
0DataRank · unranked
16 citations

David B. Page, Glenn Broeckx, Claudia Aura Gonzalez, Caoimbhe Burke, Clodagh Murphy +95 more

Cell cycle traverse rate predicts long-term outcomes in a multi-institutional cohort of patients with triple-negative breast cancer

BJC Reports(2024)10.1038/s44276-024-00097-z
N/A
0DataRank · unranked
1 citations

Manali Rupji, He Chao, Qi Xu, Geetanjali Saini, Padmashree C.G. Rida +9 more