Publications

  1. Balasubramanian R, Saha D, Arun A and Vinod PK*, Hypometabolism in Autism Spectrum Disorder: Insights from Brain and Blood Transcriptomics, 2024 (submitted).
  2. Jose A, Srivastava A and Vinod PK*, DeepGraphMut: A graph-based deep learning method for cancer prognosis using omics data, 2024 (submitted).
  3. Srivastava A and Vinod PK*, A single-cell network approach to decode metabolic regulation in Gynecologic and Breast Cancers, 2024 (under revision).
  4. Paranjape PS, Vinod PK, Vaidya T, Meta-analysis of the pathogen Leishmania donovani transcriptome reveals multiple modes of regulation including two reciprocally regulated gene modules, https://www.biorxiv.org/content/10.1101/2024.04.26.591251v1.full, 2024.
  5. Chauhan E, Sharma A, Uppin MS, Jawahar CV and Vinod PK*, Multiple Instance Learning for Glioma Diagnosis using Hematoxylin and Eosin Whole Slide Images: An Indian Cohort Study, https://arxiv.org/abs/2402.15832v2, 2024 (under revision).
  6. Sharma A, Chauhan E, Uppin MS, Rajasekhar L, Jawahar CV and Vinod PK*, Lupus nephritis subtype classfication with only slide-level labels, Proceedings of Machine Learning Research 1–10, Medical Imaging with Deep Learning (MIDL), Paris, https://openreview.net/forum?id=c1AfNZoSyQ, 2024.
  7. Porukala M and Vinod PK*, Gene expression signatures of stepwise progression of Hepatocellular Carcinoma, PLOS One, 18 (12), e0296454, 2023.
  8. Talwar V, Singh P, Mukhia N, Shetty A, Birur P, Desai K M, Sunkavalli C, Varma KS, Sethuraman R, Jawahar CV and Vinod PK*, AI-assisted screening of oral potentially malignant disorders using smartphone-based photographic images, Cancers, 15 (16), 4120, 2023.
  9. Shetty KS, Jose A, Bani M and Vinod PK*, Network diffusion-based approach for survival prediction and identification of biomarkers using multi-omics data of Papillary Renal Cell Carcinoma, Molecular Genetics and Genomics, 298(4):871-882, 2023.
  10. Porukala M and Vinod PK*, Network-level analysis of ageing and its relationship with diseases and tissue regeneration in the mouse liver, NPG Scientific Reports, 13: 4632, 2023.
  11. Srivastava A and Vinod PK*, Identification and characterization of metabolic subtypes of endometrial cancer using a systems-level approach, Metabolites, 13(3), 409, 2023.
  12. Borkar K, Chaturvedi A, Priyakumar UD* and Vinod PK*, PREHOST: Host Prediction of Coronaviridae Family Using Machine Learning, Heliyon, 9(2):e13646, 2023.
  13. Balasubramanian R and Vinod PK*, Inferring miRNA sponge modules across major neuropsychiatric disorders, Frontiers In Molecular Neuroscience, 15: 10096622022, 2022.
  14. Ramya G, Mitra A* and Vinod PK*, Predicting functional riboSNitches in the context of alternative splicing, Gene, 837, 146694, 2022.
  15. Das A, Bapi RS, Roy D and Vinod PK, Characterizing dynamic reorganization in healthy ageing, International Joint Conference on Neural Networks (IJCNN),1-7, 2022
  16. Chandra K, Khare Y, Dadi K, Vinod PK* and Bapi RS*, Deep Learning approach for classification and interpretation of Autism Spectrum Disorder, International Joint Conference on Neural Networks (IJCNN), 1-8, 2022.
  17. Borkar K, Chaturvedi A, Vinod PK* and Bapi RS*, Ayu -Characterization of Healthy Aging from Neuroimaging Data with Deep Learning and rsfMRI, Frontiers in Computational Neuroscience, 16: 940922, 2022.
  18. Chauhan R, Jawahar CV and Vinod PK*, Bytes, Pixels, & Bases: Machine Learning in Imaging-omics for Renal Cell Carcinoma, Artificial Intelligence in Cancer Diagnosis andPrognosis, Volume 1, 2022 (IOP Publishing).
  19. Nishtha P, Vinod PK*, Model scenarios for cell cycle re-entry in Alzheimer's disease, iScience (cell press), 25(7):104543, 2022.
  20. Porukala M, Vinod PK*, Modeling of temporal reorganization of transcriptome during liver regeneration, Molecular Omics (Royal Society of Chemsitry), 18, 315-327, 2022.
  21. Menon A, Piyush Singh P, Vinod PK* and Jawahar CV*, Exploring histological similarities across cancers from a deep learning perspective, Frontiers in Oncology, 12:842759, 2022.
  22. Alle, S.; Garg, A.; Karthikeyan, A…. Sethuraman, R.; Subramanian, C.; Srivastava, M.; Chakravarthi, A.; Jacob, J.; Namagiri, M.; Konala, V.;… Vinod PK* and Priyakumar UD*, COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: harnessing clinical data for public health benefits, PLOS One, 17(3):e0264785, 2022.
  23. Menon A, Piyush Singh P, Vinod PK and Jawahar CV, Interactive Learning for Assisting Whole Slide Image Annotation, ACPR 2021: Pattern Recognition pp 504–517A, 2022.
  24. Bagal V, Aggarwal R, Vinod PK and Priyakumar UD*, MolGPT: Molecular Generation Using a Transformer-Decoder Model, J. Chem. Inf. Model, 62, 9, 2064–2076, 2021.
  25. Mehta P, Alle S, Chaturvedi A... Priyakumar UD*, Vinod PK* and Pandey R*, Clinico-Genomic analysis reveals mutations associated with COVID-19 disease severity: possible modulation by RNA structure, 10(9), 1109, Pathogens, 2021.
  26. Moolamalaa STR, Balasubramanian R, Chauhan R, Priyakumar UD and Vinod PK*, Host metabolic reprogramming in response to SARS-Cov-2: A Systems Biology approach, Microb Pathog, 158:105114, 2021.
  27. Karthikeyan A, Garg A, Vinod PK* and Priyakumar UD*, Machine learning based clinical decision support system for early COVID-19 mortality prediction, Front Public Health,12;9:626697, 2021.
  28. Chauhan R, Vinod PK* and Jawahar CV*, Exploring genetic-histologic relationships in breast cancer, IEEE International Symposium on Biomedical Imaging (ISBI) 2021.
  29. Moolamalaa STR and Vinod PK*, Genome-scale metabolic network reconstruction and analysis of neuropsychiatric disorders, Comput. Biol. Med, 125, 103994, 2020.
  30. Dangarh P, Pandey N and Vinod PK*, Modeling the control of meiotic cell divisions: Entry, Progression and Exit, Biophysical Journal, 119,5,1015-1024, 2020.
  31. Pandey N, Lanke V, Vinod PK*, Network-based metabolic characterization of renal cell carcinoma, NPG Scientific Reports, 10, 5955, 2020.
  32. Noor P S, Vinod PK*, Integrative analysis of DNA methylation and gene expression in Papillary Renal Cell Carcinoma, Molecular Genetics and Genomics,295, 807–824, 2020.
  33. Kapuy O, Márton M, Bánhegyi G, and Vinod PK, Multiple system-level feedback loops control life-and-death decision in endoplasmic reticulum stress, FEBS Lett., doi: 10.1002/1873-3468.13689.2019.
  34. Tabibu S, Vinod PK* and Jawahar CV*, Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning, NPG Scientific Reports, 9, 10509, 2019.
  35. Vatika H, Bapi R S, Vinod PK* and Roy D*, Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder, Front. Hum. Neurosci., 13(6), 2019.
  36. Shubham K and Vinod PK*, Single-cell transcriptomic analysis of Pancreatic Islets in Health and Type 2 Diabetes, Int J Adv Eng Sci Appl Math, special issue on Algorithms for Network Biology (springer),11(2),105-118, 2019.
  37. Vatika H, Bapi R S, Vinod PK* and Roy D*, Age, disease and their interaction effects on intrinsic connectivity of children and adolescents in Autism Spectrum Disorder using functional connectomics, Brain Connectivity, 8(7), 2018.
  38. Noor P S, Bapi R S, and Vinod PK*, Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma, Comput. Biol. Med, 1;100:92-99, 2018.
  39. Pandey N and Vinod PK*, Mathematical modelling of reversible transition between quiescence and proliferation, PLoS ONE, 13(6): e0198420, 2018.
  40. Lanke V, Moolamalla S T R, Roy D and Vinod PK*, Integrative analysis of hippocampus gene expression profiles identifies network alterations in ageing and Alzheimer’s disease, Front. Aging Neurosci., 10:153, 2018.
  41. Kapuy O, Bánhegyi G, Vinod PK and Novak B, Systems-level feedback regulation of cell cycle transitions in Ostreococcus tauri, Plant Physiology and Biochemistry,126:39-46, 2018.
  42. Bhola T, Kapuy O, Vinod PK*, Computational modeling of meiotic entry and commitment, NPG Nature Scientific Reports, 8:180, 2018.
  43. Shubham K, Vinay L, Vinod PK*, Systems-level organization of non-alcoholic fatty liver disease progression network, Mol Biosyst., 3(9):1898-1911, 2017.
  44. Vinod PK, Novák B, Model scenarios for switch-like mitotic transitions, FEBS Lett., 589(6),667-671(2015).
  45. Kapuy O, Vinod PK, Mandl J, Banhegyi G, mTOR inhibition increases cell viability via autophagy induction during endoplasmic reticulum stress – An experimental and modeling study, FEBS Open Bio., 4, 704-713 (2014).
  46. Rattani A, Vinod PK, Godwin J, Tachibana-Konwalski K, Wolna M, Malumbres M, Novák B and Nasmyth K, Dependency of the spindle assembly checkpoint on Cdk1 renders the anaphase transition irreversible, Curr. Biol., 24, 1-8 (2014).
  47. Hegarat N*, Vesely C*, Vinod PK, Occasio C, Oliver A, Novák B and Hochegger H, PP2A/B55 and Fcp1 regulate Greatwall and ENSA dephosphorylation during mitotic exit, PLoS Genet.,10(1), e004004 (2014) (*equal contribution).
  48. Vinod PK, Zhou X, Zhang T, Mayer TU and Novák B, The role of APC/C inhibitor Emi2/XErp1 in oscillatory dynamics of early embryonic cell cycles, Biophys. Chem., 177, 1-6 (2013).
  49. Verdugo A*, Vinod PK*, Tyson JJ, Novák B, Molecular mechanisms creating bistable switches at cell cycle checkpoints, Open Biol., 3(3), 120179(2013) (*equal contribution).
  50. Vinod PK, Novák B, Cell cycle transitions, mitotic exit, Encyclopedia of Systems Biology, Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho, Hiroki Yokota (Eds.), Springer, (2013).
  51. Kapuy O, Vinod PK, Mandl J, Banhegyi G, A cellular stress-directed bistable switch controls the crosstalk between autophagy and apoptosis, Mol. Biosyst., 9(2), 296-306 (2013).
  52. Okaz E, Arguello MO, Bogdanova A, Vinod PK, Lipp JJ, Markova Z, Rybak I, Novák B, Zachariae W, Meiotic prophase requires proteolysis of M-phase regulators mediated by APC/C, Cell, 151(3): 603-18 (2012).
  53. Freire P, Vinod PK, Novák B, Interplay of transcriptional and proteolytic regulation in driving robust cell cycle progression, Mol. Biosyst., 8(3), 863-70(2012).
  54. Vinod PK, Freire P, Ahmed R, Ciliberto A, Uhlmann F, Novák B, Computational modeling of mitotic exit in budding yeast- the role of separase and Cdc14 endocycles, J. R. Soc. Interface, 8(61):1128-41(2011).
  55. Novák B, Vinod PK, Freire P, Kapuy O, Systems-level feedback in cell-cycle control, Biochem. Soc. Trans., 38(5):1242-6(2010).
  56. Vinod PK, Venkatesh KV, Quantification of the effect of amino acids on an integrated mTOR and insulin signaling pathway, Mol. Biosyst., 5(10):1163-73(2009).
  57. Vinod PK, Venkatesh KV, A steady state model for the transcriptional regulation of filamentous growth in Saccharomyces cerevisiae, In Silico Biol., 8(3-4):207-22(2008).
  58. Vinod PK, Sengupta N, Bhat PJ, Venkatesh KV, Integration of global signaling pathways, cAMP-PKA, MAPK and TOR in the regulation of FLO11, PLoS One. 3(2):e1663 (2008).
  59. Vinod PK, Venkatesh KV, Quantification of biological signaling and regulatory networks, J. IISc, 88:1(2008) (National).
  60. Vinod PK, Venkatesh KV, Specificity of MAPK signalling towards FLO11 expression is established by crosstalk from cAMP pathway, Syst. Synth. Biol., 1(2):99-108(2007).
  61. Sengupta N*, Vinod PK*, Venkatesh KV, Crosstalk between cAMP-PKA and MAP kinase pathways is a key regulatory design necessary to regulate FLO11 expression, Biophys. Chem., 125(1):59-71(2007) (*equal contribution).
  62. Vinod PK, Venkatesh KV, Steady state analysis to study signaling specificity involved in the regulation of FLO11 regulation, Foundations of Systems Biology in Engineering (FOSBE proceedings), Stuttgart, Germany (2007).
  63. Vinod PK, Konkimalla B, Chandra N, In-silico pharmacodynamics: correlation of adverse effects of H2-antihistamines with histamine N-methyl transferase binding potential, Appl Bioinformatics, 5(3):141-50(2006).

Conferences/Presentations (from IIIT)

  1. Moolamalla STN, Vinod PK*, Metabolic Network Modelling of Neuro-Psychiatric Disorders, 20th International Conference on Systems biology (ICSB2019), Okinawa, Japan, November, 2019.
  2. Dangarh P, Pandey N, Vinod PK*, Systems-level modeling of meiosis regulatory network, 20th International Conference on Systems biology (ICSB2019), Okinawa, Japan, November, 2019.
  3. Vinod PK*, Network and trajectory inference approaches to understand the behavior of pancreatic cells in healthy and Type 2 diabetes, Mathematical and statistical explorations in disease modelling and public health, ICTS, Bangalore, 2019.
  4. Kapuy O, Holczer1 M, Márton M, Besze B, Hajdú B, Vinod PK* and Bánhegyi G, A Systems Biological Analysis of Cellular Life-and-death Decision in Neurodegenerative Diseases, FEBS3+ conference ‘From molecules to living systems’, Siófok, Hungary, 2018.
  5. Noor P S, Bapi R S, and Vinod PK*, Identification of Biomarkers for Cancer Stage Progression, NGBT Conference, Bhubaneswar, 2017.
  6. Vinod PK, Integrative modelling of adipose tissue inflammation in non-alcoholic fatty liver disease, Indo-European Meeting on Mathematical Models of Infection, Inflammation and Immunity, IISc Bangalore, April 2017.
  7. Shubham K, Vinay L, Vinod PK, Systems-level organization of fatty liver disease network, The fifteenth theoretical chemistry symposium (TCS), Hyderabad, 2016.
  8. Tanvi Bhola, Vinod PK, Mathematical modelling of mitosis to meiosis transition, NNMCB National Meeting, Pune, 2015.
  9. Vatika Harlalka, Vinod PK, Dipanjan R, Relationship between regional firing rate and resting-state functional connectivity in the cortex - A computational study, 3rd International Conference on Cognition, Brain and Computation, IIT Gandhinagar, 2015.