News Archive

  1. November 2016: Our paper is accepted to Nature Scientific Reports. It is on using Deep Neural Network to predict the location of regulatory elements called enhancers. The paper is titled "EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm" and co-authored by Seong Gon Kim, Mrudul Harwani, Ananth Grama, Somali Chaterji. Here is the paper. [ pdf ]

  2. July 2016: The kickoff meeting for our NIH R01 project happened at Argonne National Lab near Chicago. [ picture (jpg) ]

  3. June 2016: Our BMC Systems Biology paper is accepted. It is on predicting genomic enhancers by doing deep learning and further shows how the results of the data analytic classifier can be made interpretable. [ Paper (in pdf) ]

  4. June 2016: Our BMC Systems Biology paper is accepted. It is on predicting genomic enhancers by doing deep learning and further shows how the results of the data analytic classifier can be made interpretable. [ Paper (in pdf) ]

  5. March 2016: Our ICS paper on a domain specific language for genomics applications has just been accepted. It is titled "SARVAVID: A Domain Specific Language for Developing Scalable Computational Genomics Applications" and is authored by Kanak Mahadik, Christopher Wright, Jinyi Zhang, Milind Kulkarni, Saurabh Bagchi, and me, and is 13 pages long. 32 of 183 submissions were accepted, for an acceptance rate of 17.5%. [ Paper in pdf ]

  6. November 2015: The CS news story on our best paper award at ACM BCB is up now. This was our award for the paper that deciphers interactions between microRNAs and genes using sequence and thermodynamic features in a distributed SVM. [ News story ] [ pic ]

  7. October 2015: I huffed and puffed and completed my first half marathon. This was at the Purdue half marathon held on October 17, 2015. Here is a pic of me shortly after my finish, after I have had a chance to catch my breath. [ picture (jpg) ]

  8. October 2015: Our paper submitted to Comsnets 16 is accepted. This paper lays out how to use a distributed Support Vector Machine for large-scale genomics data. It brings out the computational and the networking resource usages and performance implications in running such a code on an actual computational cluster. The paper is titled: "Computational and network cost of training distributed Support Vector Machines for large genomics data" and the co-authors are: Nawanol Theera-Ampornpunt, Seong Gon Kim, Asish Ghoshal, Saurabh Bagchi, Ananth Grama, and Somali Chaterji. The acceptance rate was 39/143 = 27.3%.

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