Shaw‐Hwa Lo

1.4k total citations
52 papers, 969 citations indexed

About

Shaw‐Hwa Lo is a scholar working on Genetics, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Shaw‐Hwa Lo has authored 52 papers receiving a total of 969 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Genetics, 16 papers in Molecular Biology and 15 papers in Artificial Intelligence. Recurrent topics in Shaw‐Hwa Lo's work include Genetic Associations and Epidemiology (22 papers), Statistical Methods and Inference (14 papers) and Bioinformatics and Genomic Networks (10 papers). Shaw‐Hwa Lo is often cited by papers focused on Genetic Associations and Epidemiology (22 papers), Statistical Methods and Inference (14 papers) and Bioinformatics and Genomic Networks (10 papers). Shaw‐Hwa Lo collaborates with scholars based in United States, Hong Kong and United Kingdom. Shaw‐Hwa Lo's co-authors include Tian Zheng, Kesar Singh, Herman Chernoff, Adeline Lo, Kani Chen, Min‐Te Chao, Inchi Hu, Donald J. McMahon, Mark Zucker and Vicki Addesso and has published in prestigious journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and SHILAP Revista de lepidopterología.

In The Last Decade

Shaw‐Hwa Lo

50 papers receiving 907 citations

Peers

Shaw‐Hwa Lo

GENET

ONCOL

Daowen Zhang United States

Søren Feodor Nielsen Denmark

Ying Wu China

Dan Lin United States

Kimberly F. Sellers United States

Donglin Zeng United States

Sounak Chakraborty United States

Anthony Y. C. Kuk Singapore

James N. Arvesen United States

Daowen Zhang United States

Shaw‐Hwa Lo

737 ×2.2

239 ×1.3

207 ×1.2

351 ×2.0

GENET

44 ×0.5

ONCOL

Citations per year, relative to Shaw‐Hwa Lo Shaw‐Hwa Lo (= 1×) peers Daowen Zhang

Countries citing papers authored by Shaw‐Hwa Lo

Since Specialization

Citations

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

Fields of papers citing papers by Shaw‐Hwa Lo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shaw‐Hwa Lo. 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 Shaw‐Hwa Lo. The network helps show where Shaw‐Hwa Lo may publish in the future.

Co-authorship network of co-authors of Shaw‐Hwa Lo

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

All Works

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20 of 20 papers shown

Di, Xuan, Zhaobin Mo, Shaw‐Hwa Lo, et al.. (2023). Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score. Artificial Intelligence in Medicine. 138. 102510–102510. 5 indexed citations

Lo, Shaw‐Hwa, et al.. (2021). An Interaction-Based Convolutional Neural Network (ICNN) Toward a Better Understanding of COVID-19 X-ray Images. Algorithms. 14(11). 337–337. 7 indexed citations

Auerbach, Jonathan, et al.. (2018). Coping with family structure in genome-wide association studies: a comparative evaluation. BMC Proceedings. 12(S9). 42–42. 1 indexed citations

Lo, Adeline, Jonathan Auerbach, Rachel Y. Y. Fan, et al.. (2016). Network-guided interaction mining for the blood pressure phenotype of unrelated individuals in genetic analysis workshop 19. BMC Proceedings. 10(S7). 333–336.

Auerbach, Jonathan, et al.. (2016). Identifying regions of disease-related variants in admixed populations with the summation partition approach. BMC Proceedings. 10(S7). 131–134. 1 indexed citations

Wang, Maggie Haitian, Chien‐Hsun Huang, Tian Zheng, Shaw‐Hwa Lo, & Inchi Hu. (2014). Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics. BMC Proceedings. 8(S1). S62–S62. 1 indexed citations

Liu, Ying, Chien‐Hsun Huang, Inchi Hu, Shaw‐Hwa Lo, & Tian Zheng. (2014). A dual-clustering framework for association screening with whole genome sequencing data and longitudinal traits. BMC Proceedings. 8(S1). S112–S112. 1 indexed citations

Huang, Chien‐Hsun, et al.. (2014). Considering interactive effects in the identification of influential regions with extremely rare variants via fixed bin approach. BMC Proceedings. 8(S1). S7–S7. 4 indexed citations

Liu, Ying, et al.. (2011). Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach. BMC Proceedings. 5(S9). S106–S106. 4 indexed citations

10.

Huang, Chien‐Hsun, et al.. (2011). Identifying influential regions in extremely rare variants using a fixed-bin approach. BMC Proceedings. 5(S9). S3–S3. 3 indexed citations

11.

Wang, Maggie Haitian, Chien‐Hsun Huang, Shaw‐Hwa Lo, Tian Zheng, & Inchi Hu. (2011). New insights into old methods for identifying causal rare variants. BMC Proceedings. 5(S9). S50–S50. 1 indexed citations

12.

Huang, Chien‐Hsun, et al.. (2011). Identifying rare disease variants in the Genetic Analysis Workshop 17 simulated data: a comparison of several statistical approaches. BMC Proceedings. 5(S9). S17–S17. 3 indexed citations

13.

Lei, Cong, et al.. (2009). Genome-wide gene-based analysis of rheumatoid arthritis-associated interaction with PTPN22 and HLA-DRB1. BMC Proceedings. 3(S7). S132–S132. 11 indexed citations

14.

Huang, Chien‐Hsun, et al.. (2009). Rheumatoid arthritis-associated gene-gene interaction network for rheumatoid arthritis candidate genes. BMC Proceedings. 3(S7). S75–S75. 16 indexed citations

15.

Lei, Cong, et al.. (2007). Constructing gene association networks for rheumatoid arthritis using the backward genotype-trait association (BGTA) algorithm. BMC Proceedings. 1(S1). S13–S13. 12 indexed citations

16.

Zheng, Tian, Hui Wang, & Shaw‐Hwa Lo. (2006). Backward Genotype-Trait Association (BGTA)-Based Dissection of Complex Traits in Case-Control Designs. Human Heredity. 62(4). 196–212. 31 indexed citations

17.

Lo, Shaw‐Hwa, et al.. (2005). Multilocus Linkage Analysis of Affected Sib Pairs. Human Heredity. 60(4). 227–240. 2 indexed citations

18.

Shane, Elizabeth, Vicki Addesso, Pearila Brickner Namerow, et al.. (2004). Alendronate versus Calcitriol for the Prevention of Bone Loss after Cardiac Transplantation. New England Journal of Medicine. 350(8). 767–776. 116 indexed citations

19.

Fan, Wei, Haixun Wang, Philip S. Yu, & Shaw‐Hwa Lo. (2003). Inductive learning in less than one sequential data scan. International Joint Conference on Artificial Intelligence. 595–600. 1 indexed citations

20.

Lo, Shaw‐Hwa & Jane-Ling Wang. (1989). I.i.d. representations for the bivariate product limit estimators and the bootstrap versions. Journal of Multivariate Analysis. 28(2). 211–226. 9 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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