Research work by Dr. Nita Parekh and her students were awarded first prizes for their oral presentation at the 3rd International Conference on Bioinformatics and Data Science (ICBDS – 2022), held from 22 – 23 December at
Bengaluru, for their research work on the following:
- Comparative Analysis of SARS-CoV-2 Variants Across Three Waves in India – Dr. Nita Parekh and her student Kushagra Agarwal.
Research works as explained by the authors:
In this study we carried out a comprehensive analysis of SARS-CoV-2 mutations and their spread in India over the past two years of the pandemic (27 January 2020 – 8 March 2022). The analysis covers four important timelines, viz., the early phase, followed by the first, second and third waves of the pandemic in the country. Phylogenetic analysis of the isolates indicated multiple independent entries of coronavirus in the country, while principal component analysis identified few state-specific clusters. Genetic analysis of isolates during the first year revealed that though lockdown helped in controlling the spread of the virus, region-specific set of shared mutations were developed during the early phase due to local community transmissions. We thus report the evolution of state-specific subclades, namely, I/GJ-20A (Gujarat), I/MH-2 (Maharashtra), I/Tel-A-20B, I/Tel-B-20B (Telangana), and I/AP-20A (Andhra Pradesh) that explain the demographic variation in the impact of COVID-19 across states. In the second year of the pandemic, India faced an aggressive second wave while the third wave was quite mild in terms of severity. Here we also discuss the prevalence and impact of different lineages and variants of Concerns/Interests, viz., Delta, Kappa, Omicron, etc. observed during this period. From the genetic analysis of mutation spectra of Indian isolates, the insights gained in its transmission, geographic distribution, containment, and impact are discussed.
- Evaluating Generalizability of Deep Learning Models Using Indian-COVID-19 CT Dataset – Suba S, Nita Parekh, Ramesh Loganathan, Vikram Pudi, Chinnababu Sunkavalli.
Research works as explained by the authors:
Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using machine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased subsets of publicly available COVID-19 data. This has raised concerns regarding the generalizability of these models on external datasets, not seen by the model during training. To address some of these issues, in this work CT scan images from confirmed COVID-19 data obtained from one of the largest public repositories, COVIDx CT 2A were used for training and in-ternal validation of machine learning models. For the external validation we generated Indian-COVID-19 CT dataset, an open-source repository contain-ing 3D CT volumes and 12096 chest CT images from 288 COVID-19 patients from India. Comparative performance evaluation of four state-of-the-art machine learning models, viz., a lightweight convolutional neural network (CNN), and three other CNN based deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in classifying CT images into three classes, viz., normal, non-covid pneumonia, and COVID-19 is carried out on these two datasets. Our analysis showed that the performance of all the models is comparable on the hold-out COVIDx CT 2A test set with 90% – 99% accuracies (96% for CNN), while on the external Indian-COVID-19 CT dataset a drop in the performance is observed for all the models (8% – 19%). The traditional machine learning model, CNN performed the best on the external dataset (accuracy 88%) in comparison to the deep learning models, indicating that a lightweight CNN is better generalizable on unseen data. The data and code are made available at https://github.com/aleesuss/c19.