Ashis Kumer Biswas

518 total citations
32 papers, 349 citations indexed

About

Ashis Kumer Biswas is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Ashis Kumer Biswas has authored 32 papers receiving a total of 349 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 10 papers in Artificial Intelligence and 6 papers in Computational Theory and Mathematics. Recurrent topics in Ashis Kumer Biswas's work include Cancer-related molecular mechanisms research (6 papers), Quantum Computing Algorithms and Architecture (4 papers) and RNA modifications and cancer (4 papers). Ashis Kumer Biswas is often cited by papers focused on Cancer-related molecular mechanisms research (6 papers), Quantum Computing Algorithms and Architecture (4 papers) and RNA modifications and cancer (4 papers). Ashis Kumer Biswas collaborates with scholars based in United States, Bangladesh and China. Ashis Kumer Biswas's co-authors include Hafiz Md. Hasan Babu, Ahsan Raja Chowdhury, Md. Mahmudul Hasan, Nasimul Noman, Jean Gao, Dongchul Kim, Mingon Kang, Jian Peng, Praveen Kumar Tripathi and Xiaoyong Wu and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and BMC Bioinformatics.

In The Last Decade

Ashis Kumer Biswas

30 papers receiving 324 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ashis Kumer Biswas United States 9 135 127 96 73 20 32 349
Ya Zhao China 9 131 1.0× 47 0.4× 22 0.2× 13 0.2× 21 1.1× 23 328
Aaron Robertson United States 10 75 0.6× 118 0.9× 19 0.2× 47 0.6× 25 1.3× 29 396
Maria d’Errico Italy 9 80 0.6× 39 0.3× 139 1.4× 5 0.1× 5 0.3× 17 449
Wen-Xiang Chen China 9 98 0.7× 67 0.5× 33 0.3× 6 0.1× 36 220
Xinsheng Liu China 8 58 0.4× 19 0.1× 11 0.1× 20 0.3× 32 1.6× 71 279
Shuya Li China 10 51 0.4× 236 1.9× 377 3.9× 5 0.1× 5 0.3× 19 514
Nino Shervashidze Germany 4 121 0.9× 22 0.2× 35 0.4× 8 0.1× 4 0.2× 7 281
Xiwei Tang China 10 40 0.3× 108 0.9× 294 3.1× 13 0.2× 3 0.1× 25 395
Laurent Mouchard France 11 168 1.2× 59 0.5× 110 1.1× 5 0.1× 2 0.1× 32 280

Countries citing papers authored by Ashis Kumer Biswas

Since Specialization
Citations

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

Fields of papers citing papers by Ashis Kumer Biswas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ashis Kumer Biswas. 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 Ashis Kumer Biswas. The network helps show where Ashis Kumer Biswas may publish in the future.

Co-authorship network of co-authors of Ashis Kumer Biswas

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

All Works

20 of 20 papers shown
1.
2.
Biswas, Ashis Kumer, et al.. (2023). Fire-induced geochemical changes in soil: Implication for the element cycling. The Science of The Total Environment. 868. 161714–161714. 33 indexed citations
3.
Schwab, M. & Ashis Kumer Biswas. (2023). Invertible Neural Networks for Trustworthy AI. 480–485. 1 indexed citations
4.
Mehrpour, Omid, Christopher Hoyte, Abdullah Al Masud, et al.. (2023). Deep learning neural network derivation and testing to distinguish acute poisonings. Expert Opinion on Drug Metabolism & Toxicology. 19(6). 367–380. 5 indexed citations
5.
Mehrpour, Omid, Christopher Hoyte, Abdullah Al Masud, et al.. (2022). Classification of acute poisoning exposures with machine learning models derived from the National Poison Data System. Basic & Clinical Pharmacology & Toxicology. 131(6). 566–574. 8 indexed citations
6.
Biswas, Ashis Kumer, et al.. (2021). Data-Blind ML: Building privacy-aware machine learning models without direct data access. 95–98. 6 indexed citations
7.
Peng, Jian, et al.. (2021). Efficacy of novel Summation-based Synergetic Artificial Neural Network in ADHD diagnosis. SHILAP Revista de lepidopterología. 6. 100120–100120. 14 indexed citations
8.
Kang, Mingon, Ashis Kumer Biswas, Dongchul Kim, & Jean Gao. (2019). Semi-Supervised Discriminative Transfer Learning in Cross-Language Text Classification. 1031–1038. 2 indexed citations
9.
Babu, Hafiz Md. Hasan, et al.. (2018). A Fast FPGA-Based BCD Adder. Circuits Systems and Signal Processing. 37(10). 4384–4408. 8 indexed citations
10.
Tripathi, Praveen Kumar, et al.. (2018). Preference Aware Travel Route Recommendation with Temporal Influence. 1–9. 12 indexed citations
11.
Babu, Hafiz Md. Hasan, et al.. (2017). An Efficient Design of an FPGA-Based Multiplier Using LUT Merging Theorem. 213. 116–121. 3 indexed citations
12.
Babu, Hafiz Md. Hasan, et al.. (2017). Area and Delay Efficient Design of a Quantum Bit String Comparator. 62. 51–56. 2 indexed citations
13.
Kim, Dongchul, Mingon Kang, Ashis Kumer Biswas, Chunyu Liu, & Jean Gao. (2016). Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders. BMC Medical Genomics. 9(S2). 50–50. 5 indexed citations
14.
Kang, Mingon, Ju Young Park, Dongchul Kim, et al.. (2016). Multi-Block Bipartite Graph for Integrative Genomic Analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 14(6). 1350–1358. 4 indexed citations
15.
Babu, Hafiz Md. Hasan, et al.. (2016). Low‐power and area efficient binary coded decimal adder design using a look up table‐based field programmable gate array. IET Circuits Devices & Systems. 10(3). 163–172. 5 indexed citations
16.
Biswas, Ashis Kumer & Jean Gao. (2016). PR2S2Clust: Patched RNA-seq read segments’ structure-oriented clustering. Journal of Bioinformatics and Computational Biology. 14(5). 1650027–1650027. 2 indexed citations
17.
Kim, Dong-Chul, Mingon Kang, Ashis Kumer Biswas, Chunyu Liu, & Jean Gao. (2015). Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to Psychiatric disorders. 145–150. 1 indexed citations
18.
Biswas, Ashis Kumer, Mingon Kang, Dongchul Kim, et al.. (2015). Inferring disease associations of the long non-coding RNAs through non-negative matrix factorization. Network Modeling Analysis in Health Informatics and Bioinformatics. 4(1). 11 indexed citations
19.
Biswas, Ashis Kumer, Baoju Zhang, Xiaoyong Wu, & Jean Gao. (2013). QLZCClust: Quaternary lempel-Ziv complexity based clustering of the RNA-seq read block segments. 1–4. 1 indexed citations
20.
Biswas, Ashis Kumer, et al.. (2010). Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information. BMC Bioinformatics. 11(1). 273–273. 67 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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