Ashish Anand, PhD

ASSOCIATE PROFESSOR
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
INDIAN INSTITUTE OF TECHNOLOGY GUWAHATI
INDIA - 781039
PHONE (OFF): +91 361 258 2374
EMAIL: anand [dot] ashish [at] iitg.ernet.in

Web of KnowledgeResearch Gate Google Scholar

Technical Reports Journals Conferences Book Chapters Posters

Technical Reports

  1. Inference of splicing motifs through visualization of recurrent networks. Aparajita Dutta, Aman Dalmia, Athul R, Kusum Kumari Singh, A Anand. 2018. biorXiv:451906
  2. Rapid Reconstruction of Time-varying Gene Regulatory Networks. Saptarshi Pyne, Alok Ranjan Kumar, A Anand. 2017. biorXiv:10.1101/272484 (Accepted in IEEE TCBB)
  3. Naive semantic similarity measures for scoring confidence of protein-protein interactions using Gene Ontology. Madhusudan Paul, A Anand (Under Revision)
  4. Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition. Sunil Kumar Sahu, A Anand. 2017. arXiv:1708.03447
  5. 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 ( click here ))
  6. Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network. Sunil Kumar Sahu, A Anand. 2017. arXiv:1701.08303 (Accepted in JBI)
  7. Inferring disease correlation from healthcare data. Priyadarshini G, Anand A. 2015. arXiv:1510.03051[cs.IR]

Journals

  1. Rapid Reconstruction of Time-varying Gene Regulatory Networks. Pyne S, Kumar AR, Anand A. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018, In press. Publisher's site   Preprint  Code & data
  2. Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network. Sunil Kumar Sahu, A Anand. 2018. arXiv:1701.08303 (Accepted in JBI)
  3. What matters in a transferable neural network model for relation classification in the biomedical domain? Sunil Kumar Sahu, A Anand. Artificial Intelligence in Medicine, 2018, Vol 87: 60-66 ( click here )
  4. SpliceVec: Distributed feature representations for splice junction prediction. A Dutta, T Dubey, Kusum K Singh, A Anand. Computational Biology and Chemistry, 2018, Vol 74: 434-441 ( click here )
  5. 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.
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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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Conferences

  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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Posters

  1. Towards Genome-scale Disease Progression Modeling. Saptarshi Pyne, Alok R Kumar, Anand A. 17th International Conference on Bioinformatics (InCoB) 2018, New Delhi, India.
  2. Multi-label classification with label clustering. Pranav Gupta, Anand A. 1st Indian workshop on Machine Learning, IIT Kanpur India.
  3. 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.
  4. 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

© Ashish Anand, Developed by Mohit Kumar & Maintained by Saptarshi Pyne