Ashish Anand

2.1k total citations · 1 hit paper
55 papers, 1.2k citations indexed

About

Ashish Anand is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ashish Anand has authored 55 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 16 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ashish Anand's work include Machine Learning in Bioinformatics (8 papers), Topic Modeling (8 papers) and Bioinformatics and Genomic Networks (8 papers). Ashish Anand is often cited by papers focused on Machine Learning in Bioinformatics (8 papers), Topic Modeling (8 papers) and Bioinformatics and Genomic Networks (8 papers). Ashish Anand collaborates with scholars based in India, Singapore and United States. Ashish Anand's co-authors include Kalyanmoy Deb, Dhiraj Joshi, Sunil Kumar Sahu, Ponnuthurai Nagaratnam Suganthan, Ganesan Pugalenthi, Gary B. Fogel, Kusum Singh, Desh Raj, William A. Jiranek and Venkata Dasu Veeranki and has published in prestigious journals such as Nucleic Acids Research, Analytical Biochemistry and Journal of Theoretical Biology.

In The Last Decade

Ashish Anand

50 papers receiving 1.2k 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.

A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization

2002 451 citations Ashish Anand et al. profile →

Peers

Ashish Anand

AI

MB

CTM

CVPR

EEE

Marco S. Nobile Italy

Daniela Besozzi Italy

Paolo Cazzaniga Italy

Hitoshi Iba Japan

Frank D. Francone Germany

Wen Zhu China

Zhijie Wang China

Ying Ju China

Sean B. Holden United Kingdom

Muhammad Iqbal Pakistan

Marco S. Nobile Italy

Ashish Anand

521 ×0.8

AI

401 ×1.0

MB

205 ×0.7

CTM

134 ×1.4

CVPR

65 ×0.8

EEE

Citations per year, relative to Ashish Anand Ashish Anand (= 1×) peers Marco S. Nobile

Countries citing papers authored by Ashish Anand

Since Specialization

Citations

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

Fields of papers citing papers by Ashish Anand

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashish Anand

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

1.

Lohith, T.N., et al.. (2025). Exploration of piperazine-citral sulfonyl derivatives: antibacterial and in-silico studies against methicillin-resistant Staphylococcus aureus. Archives of Microbiology. 207(3). 56–56. 1 indexed citations

2.

Anand, Ashish, et al.. (2024). Leveraging Remote Sensing and Nanotechnology to Overcome Barriers to Agroforestry Adoption by Small Holder Farmers. Journal of Scientific Research and Reports. 30(5). 113–140. 3 indexed citations

3.

Anand, Ashish, et al.. (2024). Effective Farming for Zero Wastage and Enhancement of Farmer’s Income. 1–6. 1 indexed citations

4.

Anand, Ashish, et al.. (2023). S-VQA: Sentence-Based Visual Question Answering. 1–8. 1 indexed citations

5.

Anand, Ashish, et al.. (2023). Behavioural patterns of children as risk factors of oral hygiene and dental caries- A short study. 11(1). 43–47.

6.

Rao, Mayuri Sathyanarayana, et al.. (2023). PRATUSH experiment concept and design overview. Experimental Astronomy. 56(2-3). 741–778. 11 indexed citations

7.

Singh, Kusum, et al.. (2021). SpliceViNCI: Visualizing the splicing of non-canonical introns through recurrent neural networks. Journal of Bioinformatics and Computational Biology. 19(4). 2150014–2150014. 5 indexed citations

8.

Siddique, Sana, et al.. (2020). ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora. 127–137. 6 indexed citations

9.

Sahu, Sunil Kumar & Ashish Anand. (2018). Drug-drug interaction extraction from biomedical texts using long short-term memory network. Journal of Biomedical Informatics. 86. 15–24. 134 indexed citations

10.

Raj, Desh, et al.. (2017). Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text. 311–321. 23 indexed citations

11.

Tripathi, Samarth, et al.. (2016). Predicting Online Doctor Ratings from User Reviews Using Convolutional Neural Networks. International Journal of Machine Learning and Computing. 6(2). 149–154. 15 indexed citations

12.

Anand, Ashish, Ganesan Pugalenthi, Gary B. Fogel, & Ponnuthurai Nagaratnam Suganthan. (2010). An approach for classification of highly imbalanced data using weighting and undersampling. Amino Acids. 39(5). 1385–1391. 121 indexed citations

13.

Anand, Ashish, Nikhil R. Pal, & Ponnuthurai Nagaratnam Suganthan. (2010). Integration of functional information of genes in fuzzy clustering of short time series gene expression data. 2. 1–8. 1 indexed citations

14.

Anand, Ashish & Ponnuthurai Nagaratnam Suganthan. (2009). Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates. Journal of Theoretical Biology. 259(3). 533–540. 26 indexed citations

15.

Anand, Ashish, et al.. (2008). Metal Hypersensitivity. The Journal of Arthroplasty. 24(5). 826.e25–826.e28. 24 indexed citations

16.

Anand, Ashish, Ganesan Pugalenthi, & Ponnuthurai Nagaratnam Suganthan. (2008). Predicting protein structural class by SVM with class-wise optimized features and decision probabilities. Journal of Theoretical Biology. 253(2). 375–380. 53 indexed citations

17.

Anand, Ashish, Gary B. Fogel, E. K. Tang, & Ponnuthurai Nagaratnam Suganthan. (2006). Feature Selection Approach for Quantitative Prediction of Transcriptional Activities. 1–6. 2 indexed citations

18.

Anand, Ashish. (2005). Development of a bio-sensing technique for the detection of prions in foods. OakTrust (Texas A&M University Libraries). 1 indexed citations

19.

Santti, Henrikki, Laura Mikkonen, Ashish Anand, et al.. (2005). Disruption of the murine PIASx gene results in reduced testis weight. Journal of Molecular Endocrinology. 34(3). 645–654. 43 indexed citations

20.

Henry, James, Ashish Anand, Mustafa Habib Chowdhury, et al.. (2004). Development of a nanoparticle-based surface-modified fluorescence assay for the detection of prion proteins. Analytical Biochemistry. 334(1). 1–8. 20 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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