• Insilico screening to identify novel inhibitors targeting 30S ribosomal protein S12 in meningitis-causing organism ‘Elizabethkingia meningoseptica’ N Girdhar, V Yadav, N Kumari, N Subbarao, A Krishnamachari, Journal of Biomolecular Structure and Dynamics 43 (13), 6737-6748 (2025)

• Genome-wide binding sites of Plasmodium falciparum mini chromosome maintenance protein MCM6 show new insights into parasite DNA replication, S Shekhar, S Verma, MK Gupta, SS Roy, I Kaur, A Krishnamachari, ...Biochimica et Biophysica Acta (BBA)-Molecular Cell Research 1870 (7), 119546 (2023)

• Resonance curves and jump frequencies in a dual-frequency Paul trap on account of octopole field imperfection, I Ghosh, V Saxena, A Krishnamachari, IEEE Transactions on Plasma Science 51 (7), 1924-1931 (2023)

• Classification of lncRNA and mRNA of Eukaryotic model organism using physicochemical properties and composition of dineuclotides and trinucleotides, R Prasad, A Krishnamachari, 2023 2nd International Conference on Paradigm Shifts in Communications …(2023)

• Study of Tsallis Distribution of Plasma inside Paul Trap using 3D Color-Map Plots, I Ghosh, V Saxena, A Krishnamachari, 2023 International Conference on Device Intelligence, Computing and …

• Study of Plasma Distribution Function in a Paul Trap using Palette Mapped 3D Plots, I Ghosh, V Saxena, A Krishnamachari, 2022 IEEE International Conference of Electron Devices Society Kolkata …

• Computational characterization and analysis of molecular sequence data of Elizabethkingia meningoseptica, N Girdhar, N Kumari, A Krishnamachari, BMC Research Notes 15 (1), 133

• Challenges of small RNA technology RK Singh, A Krishnamachari, M Sharaff Plant Small RNA, 545-565

• Krishanu Bhowmick, Ankita Tehlan, Sunita, Renu Sudhakar, Inderjeet Kaur, Puran Singh Sijwali, Annangarachari Krishnamachari, Suman Kumar Dhar (2020) Journal of Cell Science, 133

 

• AK Jaiswal,and A Krishnamachari (2019) Physicochemical property based computational scheme for classifying DNA sequence elements of Saccharomyces cerevisiae Computational biology and chemistry, 79, 193-201

 

• U Singh, K Shah, S Dhar, V Singh, A Krishnamachari (2019) ORIS: An interactive software tool for prediction of replication origin in prokaryotic genomes. Journal of Open Source Software 40, 1589

 

• VK Singh, V Kumar, A Krishnamachari (2018). Prediction of replication sites in Saccharomyces cerevisiae genome using DNA segment properties: Multi-view ensemble learning (MEL) approach Biosystems , 163, 59-69

 

• M Agarwal, K Bhowmick, K Shah, A Krishnamachari, SKDhar (2017). Identification and characterization of ARS‐like sequences as putative origin(s) of replication in human malaria parasite Plasmodium falciparum. The FEBS journal, 284 (16) , 2674-2695

 

• Vinod Kumar Singh and Krishnamachari A (2016) Context based computational analysis and characterisation of  ARS consensus sequences (ACS) of Sacchromyces cerevisae genome. Genomics Data, 9, p130-136

 

• H Parikh, A Singh, A Krishnamachari, K Shah (2015) . Computational prediction of replication in bacterial genomes using correlated entropy measure (CEM) Biosystems 128, 19-25

 

• U Singh, S Chauhan, A Krishnamachari, L Vig. Ensemble of deep long short term memory networks for labelling origin of replication sequences.v`Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE ...PARIS

 

• Kushal Shah and A  Krishnamachari (2012) On the origin of three base periodicity in genomes Biosystems 107, p 142-144

 

• Kushal Shah and A  Krishnamachari (2012). Nucleotide correlation based measure for identifying origin of replication in genomic sequences . Biosystems 107 , p 52-55

• Payal Singh , Pradipta Bandhyopadhyay, Sudha Bhattacharya, Krishnamachari and Supratim Sengupta (2009) Riboswitch detection using profile hidden markov models. BMC Bioinformatics, 10 :325

 

• Ilya G. Lyakhov, Annangarachari Krishnamachari and Thomas D. Schneider  (2008) Discovery of novel tumor suppressor p53 response elements using information theory. Nucleic Acids Res. 36:3828-3833

 

• P. Pandey and A. Krishnamachari (2006). Computational analysis of plant RNA Pol-II promoters. BioSystems 83  p38–50.

 

• Karmeshu and Krishnamachari (2004) . Sequence variability and long-range dependence in DNA: An Information theoretic perspective Lecture notes in Computer Science. Vol. 3316, p1354, Springer.

                

• Krishnamachari, Vijnan moy Mondal and Karmeshu (2004) . Study of DNA binding sites using the Renyi parametric entropy measure. Journal of Theoretical Biology 227 429–436.