Christina Jacobs

490 total citations · 1 hit paper
8 papers, 297 citations indexed

About

Christina Jacobs is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Christina Jacobs has authored 8 papers receiving a total of 297 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 2 papers in Molecular Biology. Recurrent topics in Christina Jacobs's work include AI in cancer detection (4 papers), Bee Products Chemical Analysis (2 papers) and Plant and animal studies (2 papers). Christina Jacobs is often cited by papers focused on AI in cancer detection (4 papers), Bee Products Chemical Analysis (2 papers) and Plant and animal studies (2 papers). Christina Jacobs collaborates with scholars based in United States, Australia and Germany. Christina Jacobs's co-authors include Gillian M. Newstead, Kathy D. Miller, Bradley S. Snyder, Janice S. Sung, Robert C. Ward, Christopher Comstock, Mitchell D. Schnall, Ilana F. Gareen, Christiane Kühl and Andrew D. Prather and has published in prestigious journals such as JAMA, Academic Radiology and International Journal of Biomedical Imaging.

In The Last Decade

Christina Jacobs

8 papers receiving 292 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Comparison of Abbreviated Breast MRI vs Digital Breast Tomosynthesis for Breast Cancer Detection Among Women With Dense Breasts Undergoing Screening

2020 265 citations Christopher Comstock, Constantine Gatsonis et al. JAMA profile →

Peers

Christina Jacobs

RNMI

PRM

ONCOL

AI

CR

Vivianne Freitas Canada

Athina Vourtsis United States

Nicole Kay France

Qiufeng Zhao China

Gabrielle Baxter United Kingdom

Einar Vigeland Norway

Iqbal Warsi United Kingdom

Fang-Fang Wu United States

Mary Hughes United States

Carolina Rossi Saccarelli United States

Vivianne Freitas Canada

Christina Jacobs

212 ×1.3

RNMI

148 ×1.3

PRM

116 ×1.3

ONCOL

151 ×1.8

AI

82 ×1.4

CR

Citations per year, relative to Christina Jacobs Christina Jacobs (= 1×) peers Vivianne Freitas

Countries citing papers authored by Christina Jacobs

Since Specialization

Citations

This map shows the geographic impact of Christina Jacobs's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Christina Jacobs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christina Jacobs more than expected).

Fields of papers citing papers by Christina Jacobs

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christina Jacobs. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Christina Jacobs. The network helps show where Christina Jacobs may publish in the future.

Co-authorship network of co-authors of Christina Jacobs

This figure shows the co-authorship network connecting the top 25 collaborators of Christina Jacobs. A scholar is included among the top collaborators of Christina Jacobs based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Christina Jacobs. Christina Jacobs is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Jacobs, Christina, et al.. (2025). Profiling of Australian Stingless Bee Honey Using Multivariate Data Analysis of High-Performance Thin-Layer Chromatography Fingerprints. Chemosensors. 13(2). 30–30. 1 indexed citations

2.

Jacobs, Christina, et al.. (2023). Validation of a high-performance thin-layer chromatography method for the quantitative determination of trehalulose. Journal of Planar Chromatography – Modern TLC. 36(4). 201–210. 6 indexed citations

3.

Comstock, Christopher, Constantine Gatsonis, Gillian M. Newstead, et al.. (2020). Comparison of Abbreviated Breast MRI vs Digital Breast Tomosynthesis for Breast Cancer Detection Among Women With Dense Breasts Undergoing Screening. JAMA. 323(8). 746–746. 265 indexed citations breakdown →

4.

Abdel‐Qader, Ikhlas, et al.. (2011). Embedded Feature Selection using PSO-kNN: Shape-Based Diagnosis of Microcalcification Clusters in Mammography. 3(1). 7–11. 9 indexed citations

5.

Abdel‐Qader, Ikhlas, et al.. (2009). Detection of synthetic and real microcalcifications based on statistical analysis of original and highpass-filtered mammograms. 18(3). 267–288. 1 indexed citations

6.

Abdel‐Qader, Ikhlas, et al.. (2009). Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms. International Journal of Biomedical Imaging. 2009(1). 767805–767805. 7 indexed citations

7.

Abdel‐Qader, Ikhlas, et al.. (2006). Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA. International Journal of Biomedical Imaging. 2006(1). 57850–57850. 7 indexed citations

8.

Jacobs, Christina, et al.. (1994). Pathologic fracture in a 66-year-old man. Academic Radiology. 1(3). 298–301. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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