Milad Eskandari

1.2k total citations · 1 hit paper
34 papers, 783 citations indexed

About

Milad Eskandari is a scholar working on Plant Science, Genetics and Pathology and Forensic Medicine. According to data from OpenAlex, Milad Eskandari has authored 34 papers receiving a total of 783 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Plant Science, 8 papers in Genetics and 2 papers in Pathology and Forensic Medicine. Recurrent topics in Milad Eskandari's work include Soybean genetics and cultivation (28 papers), Legume Nitrogen Fixing Symbiosis (15 papers) and Plant pathogens and resistance mechanisms (10 papers). Milad Eskandari is often cited by papers focused on Soybean genetics and cultivation (28 papers), Legume Nitrogen Fixing Symbiosis (15 papers) and Plant pathogens and resistance mechanisms (10 papers). Milad Eskandari collaborates with scholars based in Canada, United States and Israel. Milad Eskandari's co-authors include Mohsen Yoosefzadeh-Najafabadi, Istvan Rajcan, Dan Tulpan, Elroy R. Cober, John Sulik, Hugh J. Earl, Sepideh Torabi, Davoud Torkamaneh, Mohsen Hesami and François Belzile and has published in prestigious journals such as PLoS ONE, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Milad Eskandari

33 papers receiving 771 citations

Hit Papers

1 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.

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

2021 159 citations Mohsen Yoosefzadeh-Najafabadi, Hugh J. Earl et al. Frontiers in Plant Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Milad Eskandari Canada	16	634	171	109	77	63	34	783
Mohsen Yoosefzadeh-Najafabadi Canada	15	618 1.0×	160 0.9×	297 2.7×	80 1.0×	75 1.2×	40	833
Zhihua Wu China	15	396 0.6×	139 0.8×	549 5.0×	27 0.4×	68 1.1×	62	860
Antje Schierholt Germany	16	651 1.0×	85 0.5×	433 4.0×	15 0.2×	26 0.4×	31	839
Heike Sprenger Germany	13	382 0.6×	46 0.3×	144 1.3×	51 0.7×	25 0.4×	31	566
Shanshan Chu China	18	847 1.3×	100 0.6×	276 2.5×	13 0.2×	5 0.1×	37	1.0k
Hao Cheng China	15	376 0.6×	48 0.3×	199 1.8×	54 0.7×	18 0.3×	36	625
Lian Wu China	14	183 0.3×	107 0.6×	110 1.0×	27 0.4×	4 0.1×	64	639
Jeong-Dong Lee South Korea	9	493 0.8×	66 0.4×	126 1.2×	10 0.1×	6 0.1×	12	617
Xiujie Liu China	10	685 1.1×	80 0.5×	287 2.6×	16 0.2×	9 0.1×	19	929

Countries citing papers authored by Milad Eskandari

Since Specialization

Citations

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

Fields of papers citing papers by Milad Eskandari

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Milad Eskandari

This figure shows the co-authorship network connecting the top 25 collaborators of Milad Eskandari. A scholar is included among the top collaborators of Milad Eskandari 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 Milad Eskandari. Milad Eskandari 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

Eskandari, Milad, et al.. (2024). Genome-wide association mapping in exotic × Canadian elite crosses: mining beneficial alleles for agronomic and seed composition traits in soybean. Frontiers in Plant Science. 15. 1490767–1490767. 1 indexed citations

Hesami, Mohsen, Marco Pepe, Ben Spitzer‐Rimon, Milad Eskandari, & Andrew Maxwell Phineas Jones. (2024). Epigenetic factors related to recalcitrance in plant biotechnology. Genome. 68. 1–11. 1 indexed citations

Yoosefzadeh-Najafabadi, Mohsen, Milad Eskandari, Sepideh Torabi, et al.. (2022). Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. International Journal of Molecular Sciences. 23(10). 5538–5538. 32 indexed citations

Yoosefzadeh-Najafabadi, Mohsen, Milad Eskandari, François Belzile, & Davoud Torkamaneh. (2022). Genome-Wide Association Study Statistical Models: A Review. Methods in molecular biology. 2481. 43–62. 22 indexed citations

Yoosefzadeh-Najafabadi, Mohsen, Istvan Rajcan, & Milad Eskandari. (2022). Optimizing genomic selection in soybean: An important improvement in agricultural genomics. Heliyon. 8(11). e11873–e11873. 19 indexed citations

Rajcan, Istvan, et al.. (2022). SoyMAGIC: An Unprecedented Platform for Genetic Studies and Breeding Activities in Soybean. Frontiers in Plant Science. 13. 945471–945471. 8 indexed citations

Bruce, Robert W., et al.. (2022). Genetic analysis of sucrose concentration in soybean seeds using a historical soybean genomic panel. Theoretical and Applied Genetics. 135(4). 1375–1383. 11 indexed citations

Yoosefzadeh-Najafabadi, Mohsen, Dan Tulpan, & Milad Eskandari. (2021). Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. PLoS ONE. 16(4). e0250665–e0250665. 43 indexed citations

Yoosefzadeh-Najafabadi, Mohsen, Dan Tulpan, & Milad Eskandari. (2021). Using Hybrid Artificial Intelligence and Evolutionary Optimization Algorithms for Estimating Soybean Yield and Fresh Biomass Using Hyperspectral Vegetation Indices. Remote Sensing. 13(13). 2555–2555. 56 indexed citations

10.

Yoosefzadeh-Najafabadi, Mohsen, Sepideh Torabi, Dan Tulpan, Istvan Rajcan, & Milad Eskandari. (2021). Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods. Frontiers in Plant Science. 12. 777028–777028. 37 indexed citations

11.

Torabi, Sepideh, Arjun Sukumaran, Sangeeta Dhaubhadel, et al.. (2021). Effects of type I Diacylglycerol O-acyltransferase (DGAT1) genes on soybean (Glycine max L.) seed composition. Scientific Reports. 11(1). 2556–2556. 30 indexed citations

12.

Yoosefzadeh-Najafabadi, Mohsen, Hugh J. Earl, Dan Tulpan, John Sulik, & Milad Eskandari. (2021). Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean. Frontiers in Plant Science. 11. 624273–624273. 159 indexed citations breakdown →

13.

Torabi, Sepideh, et al.. (2020). Genomic regions associated with important seed quality traits in food-grade soybeans. BMC Plant Biology. 20(1). 485–485. 15 indexed citations

14.

Eskandari, Milad, et al.. (2020). Genome-wide association identifies several QTLs controlling cysteine and methionine content in soybean seed including some promising candidate genes. Scientific Reports. 10(1). 21812–21812. 19 indexed citations

15.

Bruce, Robert W., Davoud Torkamaneh, Christopher M. Grainger, et al.. (2020). Haplotype diversity underlying quantitative traits in Canadian soybean breeding germplasm. Theoretical and Applied Genetics. 133(6). 1967–1976. 8 indexed citations

16.

Bruce, Robert W., et al.. (2019). Trends in Soybean Trait Improvement over Generations of Selective Breeding. Crop Science. 59(5). 1870–1879. 16 indexed citations

17.

Bruce, Robert W., Davoud Torkamaneh, Christopher M. Grainger, et al.. (2019). Genome-wide genetic diversity is maintained through decades of soybean breeding in Canada. Theoretical and Applied Genetics. 132(11). 3089–3100. 18 indexed citations

18.

Eskandari, Milad, Elroy R. Cober, & Istvan Rajcan. (2013). Genetic control of soybean seed oil: II. QTL and genes that increase oil concentration without decreasing protein or with increased seed yield. Theoretical and Applied Genetics. 126(6). 1677–1687. 85 indexed citations

19.

Eskandari, Milad, Elroy R. Cober, & Istvan Rajcan. (2013). Using the candidate gene approach for detecting genes underlying seed oil concentration and yield in soybean. Theoretical and Applied Genetics. 126(7). 1839–1850. 16 indexed citations

20.

Eskandari, Milad, Elroy R. Cober, & Istvan Rajcan. (2012). Genetic control of soybean seed oil: I. QTL and genes associated with seed oil concentration in RIL populations derived from crossing moderately high-oil parents. Theoretical and Applied Genetics. 126(2). 483–495. 58 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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