Sach Mukherjee

3.3k total citations
43 papers, 849 citations indexed

About

Sach Mukherjee is a scholar working on Molecular Biology, Artificial Intelligence and Statistics and Probability. According to data from OpenAlex, Sach Mukherjee has authored 43 papers receiving a total of 849 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Molecular Biology, 14 papers in Artificial Intelligence and 10 papers in Statistics and Probability. Recurrent topics in Sach Mukherjee's work include Bioinformatics and Genomic Networks (17 papers), Gene Regulatory Network Analysis (13 papers) and Gene expression and cancer classification (12 papers). Sach Mukherjee is often cited by papers focused on Bioinformatics and Genomic Networks (17 papers), Gene Regulatory Network Analysis (13 papers) and Gene expression and cancer classification (12 papers). Sach Mukherjee collaborates with scholars based in United Kingdom, Germany and United States. Sach Mukherjee's co-authors include Terence P. Speed, Chris J. Oates, Wolfgang Schröder, Steven M. Hill, Joe W. Gray, Robert J. B. Goudie, Frank Dondelinger, Yiling Lu, Gordon B. Mills and Paul T. Spellman and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Sach Mukherjee

42 papers receiving 824 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sach Mukherjee United Kingdom 18 428 181 90 79 75 43 849
Guenther Walther United States 17 256 0.6× 329 1.8× 54 0.6× 227 2.9× 28 0.4× 34 1.0k
Josep M. Oller Spain 11 228 0.5× 113 0.6× 47 0.5× 74 0.9× 33 0.4× 30 1.1k
Alejandro Murua United States 10 472 1.1× 349 1.9× 84 0.9× 58 0.7× 81 1.1× 28 889
Markus Kalisch Switzerland 14 384 0.9× 432 2.4× 25 0.3× 340 4.3× 52 0.7× 29 1.2k
Stefan Steinerberger United States 11 168 0.4× 94 0.5× 101 1.1× 21 0.3× 73 1.0× 99 657
Veronica Vinciotti United Kingdom 18 437 1.0× 236 1.3× 38 0.4× 113 1.4× 41 0.5× 56 1.2k
Claus Bendtsen United Kingdom 17 219 0.5× 75 0.4× 40 0.4× 10 0.1× 117 1.6× 47 989
Jianhua Guo China 14 139 0.3× 157 0.9× 45 0.5× 153 1.9× 17 0.2× 63 577
Sivan Sabato Israel 10 250 0.6× 208 1.1× 69 0.8× 29 0.4× 26 0.3× 31 544
Hannes Helgason United States 15 258 0.6× 60 0.3× 50 0.6× 30 0.4× 12 0.2× 23 829

Countries citing papers authored by Sach Mukherjee

Since Specialization
Citations

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

Fields of papers citing papers by Sach Mukherjee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sach Mukherjee

This figure shows the co-authorship network connecting the top 25 collaborators of Sach Mukherjee. A scholar is included among the top collaborators of Sach Mukherjee 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 Sach Mukherjee. Sach Mukherjee 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.
Young, Cameron C., Katherine Eason, Sach Mukherjee, et al.. (2024). Development and validation of a reliable DNA copy-number-based machine learning algorithm (CopyClust) for breast cancer integrative cluster classification. Scientific Reports. 14(1). 11861–11861. 2 indexed citations
2.
Drton, Mathias, et al.. (2024). High-dimensional undirected graphical models for arbitrary mixed data. Electronic Journal of Statistics. 18(1). 1 indexed citations
3.
Mukherjee, Sach, et al.. (2022). Generalization of deep recurrent optical flow estimation for particle-image velocimetry data. Measurement Science and Technology. 33(9). 94003–94003. 22 indexed citations
4.
Bonaguro, Lorenzo, Jonas Schulte-Schrepping, Benedikt Reiz, et al.. (2022). Human variation in population-wide gene expression data predicts gene perturbation phenotype. iScience. 25(11). 105328–105328. 2 indexed citations
5.
Bonaguro, Lorenzo, Jonas Schulte-Schrepping, Arik Horne, et al.. (2022). Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state. eLife. 11. 5 indexed citations
6.
Mukherjee, Sach, et al.. (2021). Deep recurrent optical flow learning for particle image velocimetry data. Nature Machine Intelligence. 3(7). 641–651. 80 indexed citations
7.
Benedetto, Umberto, et al.. (2021). Tailored Bayes: a risk modeling framework under unequal misclassification costs. Biostatistics. 24(1). 85–107. 1 indexed citations
8.
Warnat‐Herresthal, Stefanie, Konstantinos Perrakis, Bernd Taschler, et al.. (2019). Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics. iScience. 23(1). 100780–100780. 60 indexed citations
9.
Perrakis, Konstantinos, Sach Mukherjee, & The Alzheimer’s Disease Neuroimaging Initiative. (2019). Scalable Bayesian Regression in High Dimensions With Multiple Data Sources. Figshare. 1 indexed citations
10.
Mukherjee, Sach, et al.. (2018). High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking. Apollo (University of Cambridge). 23 indexed citations
11.
Dondelinger, Frank & Sach Mukherjee. (2018). Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks. Methods in molecular biology. 1883. 25–48. 7 indexed citations
12.
Oates, Chris J. & Sach Mukherjee. (2016). Causal Discovery as Semi-Supervised Learning. arXiv (Cornell University). 1 indexed citations
13.
Oates, Chris J. & Sach Mukherjee. (2016). NETWORK INFERENCE AND BIOLOGICAL DYNAMICS1. 28 indexed citations
14.
Hill, Steven M., Nicole K. Nesser, Simon E. F. Spencer, et al.. (2016). Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling. Cell Systems. 4(1). 73–83.e10. 25 indexed citations
15.
Moss, Tyler J., Elena G. Seviour, Vasudha Sehgal, et al.. (2015). Genome-wide perturbations by miRNAs map onto functional cellular pathways, identifying regulators of chromatin modifiers. npj Systems Biology and Applications. 1(1). 15001–15001. 5 indexed citations
16.
Saha, Krishanu, et al.. (2014). A stochastic model dissects cell states in biological transition processes. Scientific Reports. 4(1). 3692–3692. 20 indexed citations
17.
Städler, Nicolas & Sach Mukherjee. (2014). Multivariate gene-set testing based on graphical models. Biostatistics. 16(1). 47–59. 10 indexed citations
18.
Casale, Francesco Paolo, Giorgio Giurato, Giovanni Nassa, et al.. (2014). Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines. PLoS ONE. 9(2). e88485–e88485. 3 indexed citations
19.
Hill, Steven M., Richard M. Neve, Nora Bayani, et al.. (2012). Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology. BMC Bioinformatics. 13(1). 22 indexed citations
20.
Mukherjee, Sach, Stephen Roberts, & Mark J. van der Laan. (2005). Data-adaptive test statistics for microarray data. Bioinformatics. 21(suppl_2). ii108–ii114. 21 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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