• Publications
  • Influence
A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization
tl;dr
We propose a generic parent-centric recombination operator (PCX) and a steady-state, elite-preserving, scalable, and computationally fast population-alteration model (we call the G3 model). Expand
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  • Open Access
Real-coded evolutionary algorithms with parent-centric recombination
tl;dr
We propose a generic parent-centric recombination operator (PCX) and compare its performance with a couple of commonly-used mean-focused recombination operators (UNDX and SPX). Expand
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Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
tl;dr
The simultaneous administration of multiple drugs increases the probability of interaction among them, as one drug may affect the activities of others. Expand
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  • Open Access
Relation extraction from clinical texts using domain invariant convolutional neural network
tl;dr
In recent years extracting relevant information from biomedical and clinical texts such as research articles, discharge summaries, or electronic health records have been a subject of many research efforts and shared challenges. Expand
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Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings
tl;dr
We propose a neural network model that jointly learns entity mentions and their context representation to eliminate use of hand crafted features. Expand
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  • Open Access
Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text
tl;dr
In this paper, we propose a convolutional recurrent neural network (CRNN) architecture that combines RNNs and CNNs in sequence to solve this problem. Expand
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  • Open Access
Evaluating distributed word representations for capturing semantics of biomedical concepts
tl;dr
We evaluate the performance of two state-of-the-art word embedding methods, namely word2vec and GloVe on a basic task of reflecting semantic similarity and relatedness of biomedical concepts. Expand
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Recurrent neural network models for disease name recognition using domain invariant features
tl;dr
We propose various end-to-end recurrent neural network (RNN) models for the tasks of disease name recognition and classification into four pre-defined categories. Expand
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  • Open Access
Feature Selection Using Rough Sets
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Unsupervised Representation Learning of DNA Sequences
tl;dr
We use an autoencoder-based sequence-to-sequence LSTM model to learn a latent representation of a fixed dimension for long and variable length DNA sequences in an unsupervised manner. Expand
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  • Open Access