Ashish Anand, PhD

INDIA - 781039
PHONE (OFF): +91 361 258 2374
EMAIL: anand [dot] ashish [at]

Technical Reports

  1. Rapid Reconstruction of Time-varying Gene Regulatory Networks. Saptarshi Pyne, Alok Ranjan Kumar, A Anand. 2017. biorXiv:10.1101/272484 (Under Revision)
  2. Naive semantic similarity measures for scoring confidence of protein-protein interactions using Gene Ontology. Madhusudan Paul, A Anand (Under Revision)
  3. Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition. Sunil Kumar Sahu, A Anand. 2017. arXiv:1708.03447 (Under Revision)
  4. What matters in a transferable neural network model for relation classification in the biomedical domain? Sunil Kumar Sahu, A Anand. 2017. arXiv:1708.03446 (Accepted in Artificial Intelligence in Medicine ( In Press ))
  5. Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network. Sunil Kumar Sahu, A Anand. 2017. arXiv:1701.08303 (Under Revision)
  6. Inferring disease correlation from healthcare data. Priyadarshini G, Anand A. 2015. arXiv:1510.03051[cs.IR]


  1. What matters in a transferable neural network model for relation classification in the biomedical domain? Sunil Kumar Sahu, A Anand. Artificial Intelligence in Medicine, ( In Press )
  2. SpliceVec: Distributed feature representations for splice junction prediction. A Dutta, T Dubey, Kusum K Singh, A Anand. Computational Biology and Chemistry, ( In Press )
  3. Detection of Highly Overlapping Communities in Complex Networks. M Paul, R Anand, A Anand. Journal of Medical Imaging and Health Informatics, 2015, Vol 5(5), 1099-1103.
  4. An approach for classification of highly imbalanced data using weighting and undersampling. A Anand, G Pugalenthi, G B Fogel, And P N Suganthan. Amino Acids, 2010, Vol 39(5), 1385-1391
  5. Identification and analysis of transcription factor family-specific features derived from DNA and protein information. A Anand, G Pugalenthi, G B Fogel, and P N Suganthan. Pattern Recognition Letters, 2010, Vol 31(14), 2097-2102
  6. Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates. A Anand, P N Suganthan. Journal of Theoretical Biology, 2009, Vol 259(3), 533-540
  7. Predicting protein structural class by SVM with class-wise optimized features and decision probabilities. A Anand, G Pugalenthi, P N Suganthan. Journal of Theoretical Biology, 2008, Vol 253(2), 375-380
  8. Disruption of the murine PIASx gene results in reduced testis weight. Santti H, Mikkonen L, Anand A; et al. Journal of Molecular Endocrinology, 2005, Vol 34(3), 645-654
  9. A computationally efficient evolutionary algorithm for real-parameter optimization. Deb K, Anand A , Joshi D. Evolutionary Computation, 2002, Vol 10(4), 371-395
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  1. SpliceVec: distributed feature representations for splice junction prediction. Aparajita Dutta, Tushar Dubey, Kusum Kumari Singh, Ashish Anand. Accepted in APBC 18. [ PDF ]
  2. Learning local and global contexts using a convolutional recurrent network model for relation classication in biomedical text. Desh Raj, Sunil Kumar Sahu, Ashish Anand. Accepted in CoNLL 17. [ PDF ]
  3. Biomedical Event Trigger Identification Using Recurrent Neural Network. Rahul, Sunil kumar Sahu, Ashish Anand. In proceeding of ACL-BioNLP 2017. [ PDF ]
  4. Representation learning of drug and disease terms for drug repositioning. Sahil Man chanda, Ashish Anand. Accepted in In proceeding of 3rd IEEE International Conference on Cybernetics 2017.[ PDF ]
  5. Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings. Abhishek, Ashish Anand and Amit Awekar. Accepted in EACL 2017 (Long Paper). [ PDF ]
  6. Relation extraction from clinical texts using domain invariant convolutional neural network. Sunil Sahu, Ashish Anand, Krishnadev Oruganty and Mahanandeeshwar Gattu. Accepted in ACL BioNLP 2016. [ PDF ]
  7. Recurrent neural network models for disease name recognition using domain invariant features. Sunil Sahu, Ashish Anand. Accepted in ACL 2016 (Long) paper. [ PDF ] (Sunil acknowledges the travel grants from Google and Microsoft for presenting the paper in the conference.)
  8. Evaluating distributed word representations for capturing semantics of biomedical concepts. Muneeb TH, S K Sahu, Anand A. Inproceedings of the ACL-BioNLP 2015 workshop, Beijing China. [ PDF ]
  9. Detection of Highly Overlapping Communities in Complex Networks. M Paul, R Anand, A Anand. 5th International Conference on Computational Systems-Biology and Bioinformatics 2014, Singapore.
  10. Inferring Disease Correlation from Healthcare Data, National Conference on Medical Informatics. G Priyadarshini, A Anand, 2014. AIIMS, New Delhi
  11. Integration of Functional Information of Genes in Fuzzy Clustering of Short Time Series Gene Expression Data. Anand A, Pal N R, Suganthan P N. In: Proceeding of IEEE World Congress on Computational Intelligence 2010, Barcelona, Spain
  12. Prediction of Transcription Factor Families Using DNA Sequence Features. Anand A, Fogel G B, Pugalenthi G, Sunagnthan P N. In: Proceedings of Pattern Recognition in Bioinformatics 2008, Melbourne, Australia
  13. A novel fuzzy and multiobjective evolutionary algorithm based gene assignment for clustering short time series expression data. Anand, A, Suganthan, P. N, Deb, K. In: IEEE Congress on Evolutionary Computation 2007, Vols 1-10, Pages: 297-304
  14. Feature selection approach for quantitative prediction of transcriptional activities. Anand A, Fogel G B, Tang E K, Suganthan P N. Computational Intelligence and Bioinformatics and Computational Biology IEEE Symposium on, 2006.
  15. Real-coded evolutionary algorithms with parent-centric recombination. Deb K, Joshi D, Anand A. In: Proceedings of the 2002 IEEE Congress on Evolutionary Computation, Hawaii
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Book Chapters

  1. Feature selection using Rough Set. M Banerjee, S Mitra, A Anand. Multiobjective machine lerarning (Eds Dr Yaochu Jin), Springer-Verlag 2006
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  1. Multi-label classification with label clustering. Pranav Gupta, Anand A. 1st Indian workshop on Machine Learning, IIT Kanpur India.
  2. A software architecture for de Novo induction of regulatory network from expression data. Ruegheimer F, Anand A, Schwikowski B. In: Proceedings of JOBIM 2011, Paris, France.
  3. Inferring a latent regulation network for Bacillus subtilis using a kernel matrix completion approach. A Anand, S Drulhe, F Gwinner, F Ruegheimer, P Bochet, B Schwikowski. 11th International Conference on Systems Biology, ICSB, 2010, Edinburgh.
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Institution Research Profile