Nancy Diao

2.0k total citations · 1 hit paper
16 papers, 375 citations indexed

About

Nancy Diao is a scholar working on Pulmonary and Respiratory Medicine, Health, Toxicology and Mutagenesis and Molecular Biology. According to data from OpenAlex, Nancy Diao has authored 16 papers receiving a total of 375 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Pulmonary and Respiratory Medicine, 5 papers in Health, Toxicology and Mutagenesis and 4 papers in Molecular Biology. Recurrent topics in Nancy Diao's work include Lung Cancer Diagnosis and Treatment (5 papers), Gene expression and cancer classification (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Nancy Diao is often cited by papers focused on Lung Cancer Diagnosis and Treatment (5 papers), Gene expression and cancer classification (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Nancy Diao collaborates with scholars based in United States, China and United Kingdom. Nancy Diao's co-authors include David C. Christiani, Raymond H. Mak, Michael Lanuti, Andrea T. Shafer, Hugo J.W.L. Aerts, Tafadzwa L. Chaunzwa, Yiwen Xu, Ahmed Hosny, Maitreyi Mazumdar and Quazi Quamruzzaman and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and Scientific Reports.

In The Last Decade

Nancy Diao

15 papers receiving 365 citations

Hit Papers

Deep learning classification of lung cancer histology usi... 2021 2026 2022 2024 2021 50 100 150
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.

  1. Deep learning classification of lung cancer histology using CT images
    2021 163 citations Tafadzwa L. Chaunzwa, Ahmed Hosny et al. Scientific Reports profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nancy Diao United States 10 154 138 94 70 49 16 375
Colleen Bouzan United States 6 73 0.5× 187 1.4× 9 0.1× 74 1.1× 7 0.1× 7 568
Fengxia Li China 10 32 0.2× 67 0.5× 10 0.1× 64 0.9× 23 0.5× 27 357
Shimaa Ahmed Egypt 8 33 0.2× 38 0.3× 11 0.1× 135 1.9× 7 0.1× 25 301
Yuko Sakakibara Japan 7 29 0.2× 81 0.6× 7 0.1× 27 0.4× 8 0.2× 34 258
Da-Feng Lin China 6 24 0.2× 55 0.4× 4 0.0× 71 1.0× 12 0.2× 11 275
Yukie Hayashi Japan 11 22 0.1× 101 0.7× 15 0.2× 14 0.2× 4 0.1× 46 308
Mathilde Boulanger France 10 14 0.1× 61 0.4× 4 0.0× 78 1.1× 110 2.2× 31 276
Xianmei Qi China 7 23 0.1× 283 2.1× 5 0.1× 37 0.5× 12 0.2× 8 412

Countries citing papers authored by Nancy Diao

Since Specialization
Citations

This map shows the geographic impact of Nancy Diao'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 Nancy Diao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nancy Diao more than expected).

Fields of papers citing papers by Nancy Diao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nancy Diao. 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 Nancy Diao. The network helps show where Nancy Diao may publish in the future.

Co-authorship network of co-authors of Nancy Diao

This figure shows the co-authorship network connecting the top 25 collaborators of Nancy Diao. A scholar is included among the top collaborators of Nancy Diao 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 Nancy Diao. Nancy Diao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Bliznashka, Lilia, Aditi Roy, David C. Christiani, et al.. (2023). Pregnancy pesticide exposure and child development in low- and middle-income countries: A prospective analysis of a birth cohort in rural Bangladesh and meta-analysis. PLoS ONE. 18(6). e0287089–e0287089. 11 indexed citations
2.
3.
Bliznashka, Lilia, Nancy Diao, Antonia M. Calafat, et al.. (2022). Prenatal Pesticide Exposure Is Associated With Lower Cognitive, Language, and Motor Development Scores in Children 20–40 Months of Age Rural Bangladesh. Current Developments in Nutrition. 6. 550–550. 1 indexed citations
4.
Aredo, Jacqueline V., Natasha Purington, Li Su, et al.. (2021). Metabolomic profiling for second primary lung cancer: A pilot case-control study. Lung Cancer. 155. 61–67. 11 indexed citations
5.
Chaunzwa, Tafadzwa L., Ahmed Hosny, Yiwen Xu, et al.. (2021). Deep learning classification of lung cancer histology using CT images. Scientific Reports. 11(1). 5471–5471. 163 indexed citations breakdown →
6.
Jaacks, Lindsay M., Nancy Diao, Antonia M. Calafat, et al.. (2019). Association of prenatal pesticide exposures with adverse pregnancy outcomes and stunting in rural Bangladesh. Environment International. 133(Pt B). 105243–105243. 53 indexed citations
7.
Mazzella, Matthew J., Susan Sumner, Li Su, et al.. (2019). Quantitative methods for metabolomic analyses evaluated in the Children’s Health Exposure Analysis Resource (CHEAR). Journal of Exposure Science & Environmental Epidemiology. 30(1). 16–27. 12 indexed citations
8.
Chaunzwa, Tafadzwa L., David C. Christiani, Michael Lanuti, et al.. (2018). A deep-learning radiomics model for predicting survival in early-stage non-small cell lung cancer.. Journal of Clinical Oncology. 36(15_suppl). 8528–8528. 2 indexed citations
9.
Chaunzwa, Tafadzwa L., David C. Christiani, Michael Lanuti, et al.. (2018). Using deep-learning radiomics to predict lung cancer histology.. Journal of Clinical Oncology. 36(15_suppl). 8545–8545. 11 indexed citations
10.
Huque, Md Hamidul, Raymond J. Carroll, Nancy Diao, David C. Christiani, & Louise Ryan. (2016). Exposure Enriched Case‐Control (EECC) Design for the Assessment of Gene–Environment Interaction. Genetic Epidemiology. 40(7). 570–578.
11.
Kile, Molly L., Ema Rodrigues, Maitreyi Mazumdar, et al.. (2014). A prospective cohort study of the association between drinking water arsenic exposure and self-reported maternal health symptoms during pregnancy in Bangladesh. Environmental Health. 13(1). 29–29. 44 indexed citations
12.
Lu, Chuncheng, Jie Jiang, Ruyang Zhang, et al.. (2014). Gene copy number alterations in the azoospermia-associated AZFc region and their effect on spermatogenic impairment. Molecular Human Reproduction. 20(9). 836–843. 26 indexed citations
13.
Kinsey, C. Matthew, Raúl San Jośe Estépar, Yang Zhao, et al.. (2014). Invasive adenocarcinoma of the lung is associated with the upper lung regions. Lung Cancer. 84(2). 145–150. 26 indexed citations
14.
Zhao, Yang, Feng Chen, Rihong Zhai, et al.. (2012). Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis. PLoS ONE. 7(9). e44978–e44978. 10 indexed citations
15.
Zhao, Yang, Hao Yu, Ying Zhu, et al.. (2012). Genetic Association Analysis Using Sibship Data: A Multilevel Model Approach. PLoS ONE. 7(2). e31134–e31134. 2 indexed citations
16.
Zhao, Yang, Feng Chen, Rihong Zhai, et al.. (2012). Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. 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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