Content Based Document Recommender using Deep Learning

With the recent advancements in information technology there has been a huge surge in amount of data available. But information retrieval technology has not been able to keep up with this pace of information generation resulting in over spending of time for retrieving relevant information. Even though systems exist for assisting users to search a database along with filtering and recommending relevant information, but recommendation system which uses content of documents for recommendation still have a long way to mature. Here we present a Deep Learning based supervised approach to recommend similar documents based on the similarity of content. We combine the C-DSSM model with Word2Vec distributed representations of words to create a novel model to classify a document pair as relevant/irrelavant by assigning a score to it. Using our model retrieval of documents can be done in O(1) time and the memory complexity is O(n), where n is number of documents.Comment: Accepted in ICICI 2017, Coimbatore, Indi

Chemoenzymatic synthesis of an analogue of the potent antifungal mycosubtilin

Mycosubtilin is a naturally occurring antifungal obtained from Bacillus subtilis that also displays limited antibiotic activity. Structurally, mycosubtilin is a macrocycliclipoheptapeptide of sequence Asn-Tyr-Asn-Gln-Pro-Ser-Asn, with the N-terminal Asn joined by a β-amino fatty acid. Besides the antifungal and antibiotic activities,these molecules are also hemolytic in nature. Hence, the purpose of this study is to synthesize a potent antifungal analogue of mycosubtilin, with a modified β-amino fatty acid, devoid of any hemolytic activity. A chemoenzymatic approach was used to synthesize the cyclic peptide, which involved the synthesis of the linear peptide chain of desired amino acid sequence, thiophenylderivatization of the peptide at the C-terminus, followed by its enzymatic cyclization using the isolated thioesterase from B. subtilis. Thus far, we have successfully synthesized the analogue of mycosubtilin. Future work will focus on purifying the product and testing it for antifungal and hemolytic activity

Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs

Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X-ray images and provides annotations for 14 thoracic diseases; it is possible to train Deep Convolutional Neural Networks (DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we experiment a set of deep learning models and present a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published methods. Our work provides the quantitative results to answer following research questions for the dataset: 1) What loss functions to use for training DCNN from scratch on ChestX-ray14 dataset that demonstrates high class imbalance and label co occurrence? 2) How to use cascading to model label dependency and to improve accuracy of the deep learning model?Comment: Submitted to CVPR 201