Jishnu Das

• Osorio, Daniel; Capasso, Anna; Eckhardt, S. Gail; Giri, Uma; Somma, Alexander & Pitts, Todd M. [Show all 14 contributors for this article] (2024). Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data. Nature Computational Science. doi: 10.1038/s43588-024-00597-5. Full text in Research Archive Show summary

  Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION’s scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

• Chatterjee, Ayan; Riegler, Michael; Johnson, Miriam S.; Das, Jishnu; Pahari, Nibedita & Ramachandra, Raghavendra [Show all 9 contributors for this article] (2024). Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology. Scientific Reports. 14(1). doi: 10.1038/s41598-024-74539-6. Full text in Research Archive Show summary

  Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study’s methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.

• Das, Jishnu; Hausvik, Geir Inge & Moe, Carl Erik (2024). A Conceptual Framework and Design Principles for Decision Support in Clinical Practice: Managing Knowledge and Uncertainty. In Laurence, Brooks; Angelopoulos, Spyros; Pan, Yu-Chun & Zamani, Efpraxia D. (Ed.), Proceedings of the 29th UK Academy for Information Systems (UKAIS) International Conference. Association for Information Systems (AIS). ISSN 9781739087517. p. 659–668. Full text in Research Archive
• Chatterjee, Ayan; Prinz, Andreas; Riegler, Michael & Das, Jishnu (2023). A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care. BMC Health Services Research. 23(1). doi: 10.1186/s12913-023-10047-z. Full text in Research Archive Show summary

  e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients.The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic. The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients.

• Chatterjee, Ayan; Prinz, Andreas; Pahari, Nibedita; Das, Jishnu & Riegler, Michael (2022). Sleep Monitoring with Wearable Sensor Data in an eCoach Recommendation System: A Conceptual Study with Machine Learning Approach, Frontiers of ICT in Healthcare. Springer Nature. ISSN 9789811951916. p. 551–564. doi: 10.1007/978-981-19-5191-6_44. Full text in Research Archive Show summary

  The collective effects of sleep loss and sleep disorders are correlated with many adverse health consequences, including increased risk of high blood pressure, obesity, diabetes, depressive state, and cardiovascular symptoms. Research in eHealth can provide methods to enrich personal health care with information and communication technologies (ICTs). An eCoach system may allow people to manage a healthy lifestyle with extended health state monitoring (e.g., sleep) and tailored recommendation generation. Using supervised machine learning (ML) techniques, this study investigated the possibility of classifying sleep stages at night for adults on hourly and daily basis. The daily total sleep minutes and hourly total sleep minutes for defined sleeping period served as input for the classification models. We first used publicly available Fitbit dataset to build the initial classification models. Second, using the transfer learning approach, we re-used the top five best-performing models on a real dataset as collected from the MOX2-5 wearable medical-grade activity device. We found that support vector classifier (SVC) with “linear” kernel outdated other classifiers with a mean accuracy score of 99.92% for hourly sleep classification and a K-nearest neighbor (KNN) outpaced other classifiers with a mean accuracy score of 99.47% for daily sleep classification, for the public Fitbit datasets. Moreover, to determine the practical efficacy of the classifier models, we conceptualized to use the classifier models in an eCoach prototype system to attain tailored sleep goals (e.g., a weekly goal of 49–63 h of sleeping).

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• Chatterjee, Ayan; Riegler, Michael; Johnson, Miriam S.; Das, Jishnu; Pahari, Nibedita & Ramachandra, Raghavendra [Show all 9 contributors for this article] (2025). Erratum: Author Correction: Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology (Scientific reports (2024) 14 1 DOI: 10.1038/s41598-024-74539-6.). Scientific Reports. 15(1), p. 4976–4976. doi: 10.1038/s41598-025-86904-0. Full text in Research Archive

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