Publications

Survival regression with accelerated failure time model in XGBoost

Published in Arxiv, 2020

Survival month for non-small lung cancer patients depend upon which stage of lung cancer is present. Our aim is to identify smoking specific gene expression biomarkers in prognosis of lung cancer patients. In this paper, we introduce the network elastic net, a generalization of network lasso that allows for simultaneous clustering and regression on graphs. In network elastic net, we consider similar patients based on smoking cigarettes per year to form the network. We then further find the suitable cluster among patients based on coefficients of genes having different survival month structures and showed the efficacy of the clusters using stage enrichment. This can be used to identify the stage of cancer using gene expression and smoking behavior of patients without doing any tests.

Recommended citation: Barnwal, Avinash et al. “Survival regression with accelerated failure time model in XGBoost.” ArXiv abs/2006.04920 (2020): n. pag. https://arxiv.org/pdf/2006.04920.pdf

Network Elastic Net for Identifying Smoking specific gene expression for lung cancer

Published in 2019 New York Scientific Data Summit (NYSDS), 2019

Survival month for non-small lung cancer patients depend upon which stage of lung cancer is present. Our aim is to identify smoking specific gene expression biomarkers in prognosis of lung cancer patients. In this paper, we introduce the network elastic net, a generalization of network lasso that allows for simultaneous clustering and regression on graphs. In network elastic net, we consider similar patients based on smoking cigarettes per year to form the network. We then further find the suitable cluster among patients based on coefficients of genes having different survival month structures and showed the efficacy of the clusters using stage enrichment. This can be used to identify the stage of cancer using gene expression and smoking behavior of patients without doing any tests.

Recommended citation: A. Barnwal, "Network Elastic Net for Identifying Smoking specific gene expression for lung cancer," 2019 New York Scientific Data Summit (NYSDS), New York, NY, USA, 2019, pp. 1-4, doi: 10.1109/NYSDS.2019.8909802. https://ieeexplore.ieee.org/abstract/document/8909802

Stacking with Neural Network for Cryptocurrency investment

Published in 2019 New York Scientific Data Summit (NYSDS), 2015

Predicting the direction of assets have been an active area of study and difficult task. Machine learning models have been used to build robust models to model the above task. Ensemble methods are one of them resulting better than single supervised method. We have used generative and discriminative classifiers to create the stack, particularly 3 generative and 6 discriminative classifiers and optimized over one-layer Neural Network to model the direction of price cryptocurrencies. Features used are technical indicators not limited to trend, momentum, volume, volatility indicators and sentiment indicators. For Cross validation, Purged Walk forward cross validation has been used. In terms of accuracy, we have done comparative analysis of the performance of Ensemble method with Stacking and individual models. We have also developed methodology for features importance for stacked model. Important indicators are identified based on feature importance.

Recommended citation: A. Barnwal, H. P. Bharti, A. Ali and V. Singh, &quot"Stacking with Neural Network for Cryptocurrency investment"&quot 2019 New York Scientific Data Summit (NYSDS) . https://ieeexplore.ieee.org/document/8909804

Generalising the drift rate distribution for linear ballistic accumulators

Published in Journal of Mathematical Psychology Volumes 68–69, October–December 2015, Pages 49-58, 2015

The linear ballistic accumulator model is a theory of decision-making that has been used to analyse data from human and animal experiments.

Recommended citation: Andrew Terry and A.A.J. Marley and Avinash Barnwal and E.-J. Wagenmakers and Andrew Heathcote and Scott D. Brown (2015). "Generalising the drift rate distribution for linear ballistic accumulators." Journal of Mathematical Psychology . https://www.sciencedirect.com/science/article/abs/pii/S0022249615000577