Faculty Publications
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Publications by NITK Faculty
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Item Prediction of Drug Interactions Using Graph-Topological Features and GNN(Springer Science and Business Media Deutschland GmbH, 2023) Balamuralidhar, N.; Surendran, P.; Singh, G.; Bhattacharjee, S.; Shetty, R.D.The risk of side effects is sometimes inevitable every time two or more drugs are prescribed together, and these side effects of varying adversity levels can be referred to as drug-drug interactions (DDI). Massive amounts of data and the constraints of experimental circumstances result in clinical trials for medication compatibility being time-consuming, risky, expensive, and impractical. Recent research has demonstrated that DDI can be modelled as graphs and experimentally shown that deep learning on graphs can be a practical choice for determining the correlation and side effects of taking multiple medications simultaneously. We propose a novel approach to use inductive graph learning with GraphSAGE, along with topological features, to leverage the structural information of a graph along with the node attributes. An experimental study of the approach is done on a publicly available subset of the DrugBank dataset. We achieve our best results that are comparable with state-of-the-art works using degree, closeness and PageRank centrality measures as additional features with less computational complexity. This study can provide a reliable and cost-effective alternative to clinical trials to predict dangerous side effects, ensuring the safety of patients. © 2023, IFIP International Federation for Information Processing.Item Overlapping word removal is all you need: revisiting data imbalance in hope speech detection(Taylor and Francis Ltd., 2024) RamakrishnaIyer LekshmiAmmal, H.; Ravikiran, M.; Nisha, G.; Balamuralidhar, N.; Madhusoodanan, A.; Anand Kumar, A.K.; Chakravarthi, B.R.Hope speech detection is a new task for finding and highlighting positive comments or supporting content from user-generated social media comments. For this task, we have used a Shared Task multilingual dataset on Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) for three languages English, code-switched Tamil and Malayalam. In this paper, we present deep learning techniques using context-aware string embeddings for word representations and Recurrent Neural Network (RNN) and pooled document embeddings for text representation. We have evaluated and compared the three models for each language with different approaches. Our proposed methodology works fine and achieved higher performance than baselines. The highest weighted average F-scores of 0.93, 0.58, and 0.84 are obtained on the task organisers{'} final evaluation test set. The proposed models are outperforming the baselines by 3{\%}, 2{\%} and 11{\%} in absolute terms for English, Tamil and Malayalam respectively. © 2023 Informa UK Limited, trading as Taylor & Francis Group.