‘Paperdemic’ during the COVID-19 pandemic

Yang, Yang; Zhao, Naiqin; Ma, Ting; Yuan, Ze; Deng, Cheng

doi:10.1016/j.ejim.2022.09.030

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…[27, 6, 19] Numerous studies have explored various aspects not only from the perspective of computer science like medical text analysis [16, ? ], public health data mining [25] and GNN benchmarking [14] but also biological and medical like KVPLM [26], DNA-Pretrain [18], proteome analysis [15, 20, 11], and peptide property prediction [10] within the realm of bioinformatics. Nevertheless, the perspective of LLMs has predominantly focused on handling medical texts, making it challenging for generative artificial intelligence to find suitable entry points and engage effectively.…”

Section: Introductionmentioning

confidence: 99%

Bioinfo-Bench: A Simple Benchmark Framework for LLM Bioinformatics Skills Evaluation

Chen,

Deng

2023

Preprint

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Large Language Models (LLMs) have garnered significant recognition in the life sciences for their capacity to comprehend and utilize knowledge. The contemporary expectation in diverse industries extends beyond employing LLMs merely as chatbots; instead, there is a growing emphasis on harnessing their potential as adept analysts proficient in dissecting intricate issues within these sectors. The realm of bioinformatics is no exception to this trend. In this paper, we introduce Bioinfo-Bench, a novel yet straightforward benchmark framework suite crafted to assess the academic knowledge and data mining capabilities of foundational models in bioinformatics. Bioinfo-Bench systematically gathered data from three distinct perspectives: knowledge acquisition, knowledge analysis, and knowledge application, facilitating a comprehensive examination of LLMs. Our evaluation encompassed prominent models ChatGPT, Llama, and Galactica. The findings revealed that these LLMs excel in knowledge acquisition, drawing heavily upon their training data for retention. However, their proficiency in addressing practical professional queries and conducting nuanced knowledge inference remains constrained. Given these insights, we are poised to delve deeper into this domain, engaging in further extensive research and discourse. It is pertinent to note that project Bioinfo-Bench is currently in progress, and all associated materials will be made publicly accessible.

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Section: Introductionmentioning

confidence: 99%

Bioinfo-Bench: A Simple Benchmark Framework for LLM Bioinformatics Skills Evaluation

Chen,

Deng

2023

Preprint

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“…To date, almost 7 million confirmed deaths have been recorded as a consequence of SARS-CoV-2 infection [13]. The desire to understand the mechanisms behind SARS-CoV-2 infection and host defense, especially as it relates to its transmissibility [14, 15] and severity [16], has prompted a vast amount of research in the field of immunology and beyond [17, 18]. One of many topics of interest concerns gene programs within cell types that respond to SARS-CoV-2, specifically peripheral blood mononuclear cells (PBMCs), which are any round nucleus containing blood cells such as dendritic cells, lymphocytes, natural killer cells (NKs), or monocytes [19].…”

Section: Introductionmentioning

confidence: 99%

Metacell-based differential expression analysis identifies cell type specific temporal gene response programs in COVID-19 patient PBMCs

O’Leary,

Zheng

2023

Preprint

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BackgroundBy resolving cellular heterogeneity in a biological sample, single cell RNA sequencing (scRNA-seq) can detect gene expression and its dynamics in different cell types. Its application to time-series samples can thus identify temporal genetic programs active in different cell types, for example, immune cells’ responses to viral infection. However, current scRNA-seq analysis need improvement. Two issues are related to data generation. One is that the number of genes detected in each cell is relatively low especially when currently popular dropseq-based technology is used for analyzing thousands of cells or more. The other is the lack of sufficient replicates (often 1-2) due to high cost of library preparation and sequencing. The third issue lies in the data analysis –-usage of individual cells as independent sampling data points leads to inflated statistics.MethodsTo address these issues, we explore a new data analysis framework, specifically whether “metacells” that are carefully constructed to maintain cellular heterogeneity within individual cell types (or clusters) can be used as “replicates” for statistical methods requiring multiple replicates. Toward this, we applied SEACells to a time-series scRNA-seq dataset from peripheral blood mononuclear cells (PBMCs) after SARS-Cov-2 infection to construct metacells, which were then used in maSigPro for quadratic regression to find significantly differentially expressed genes (DEGs) over time, followed by clustering analysis of the expression velocity trends.ResultsWe found that metacells generated using the SEACells algorithm retained greater between-cell variance and produced more biologically meaningful results compared to metacells generated from random cells. Quadratic regression revealed significant DEGs through time that have been previously annotated in the SARS-CoV2 infection response pathway. It also identified significant genes that have not been annotated in this pathway, which were compared to baseline expression and showed unique expression patterns through time.ConclusionsThe results demonstrated that this strategy could overcome the limitation of 1-2 replicates, as it correctly identified the known ISG15 interferon response program in almost all PBMC cell types. Its application further led to the uncovering of additional and more cell type-specific gene expression programs that potentially modulate different levels of host response after infection.

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“…A series of studies have shown that the COVID-19 pandemic has caused widespread damage to global health, economic and social development, and international political landscape [4][5]. Furthermore, it has also greatly impacted the academic community, giving rise to phenomena such as "paperdemic" [6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Difficulties also emerged in research and development across various fields, and the budget for public research and innovation faced enormous pressure [12]. The COVID-19 pandemic has damaged international collaboration to some extent [13], but has also brought about a new trend-"paperdemic" [6][7]. The scientific community called for COVID-19 research and accelerated the review and publication of COVID-19-related papers, resulting in a large number of papers on the pandemic [14].…”

Section: Introductionmentioning

confidence: 99%

Evolution of Scholar Networks During the COVID-19 Outbreak

Kong¹,

Wu²

2023

AETR

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This paper reviews the academic collaboration network in COVID-19 research from January 2020 to December 2021. We first collected publication data from the Covidia (https://covidia.acemap.info) and constructed a coauthorship network. Then, we select network nodes ranking algorithms on importance from three aspects: information transmission in the network, network modularization, and network stability. Through community investigation and node ranking, we analyzed the evolving characteristics of the COVID-19 collaboration network, including the weakening of overall collaboration density, the strengthening of the network modularization trend, a more uniform distribution of researchers' influence, and the network's adaptation to major events such as international cooperation and the emergence of variant strains. Additionally, we investigated the sensitivity of different indices to international cooperation and variant strains, as well as the time delay between major COVID-19-related events and changes in network indices. This analysis provides insights into the dynamics of academic collaboration networks during the pandemic and can be used as a reference for future research in the field, including the effectiveness of different collaboration models and the potential of network analysis in identifying and predicting emerging infectious diseases.

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‘Paperdemic’ during the COVID-19 pandemic

Cited by 6 publications

References 10 publications

Bioinfo-Bench: A Simple Benchmark Framework for LLM Bioinformatics Skills Evaluation

Bioinfo-Bench: A Simple Benchmark Framework for LLM Bioinformatics Skills Evaluation

Metacell-based differential expression analysis identifies cell type specific temporal gene response programs in COVID-19 patient PBMCs

Evolution of Scholar Networks During the COVID-19 Outbreak

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