Exploring the potential of large language model–based chatbots in challenges of ribosome profiling data analysis: a review DOI Creative Commons
Zheyu Ding, Rong Wei,

Jianing Xia

et al.

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 26(1)

Published: Nov. 22, 2024

Abstract Ribosome profiling (Ribo-seq) provides transcriptome-wide insights into protein synthesis dynamics, yet its analysis poses challenges, particularly for nonbioinformatics researchers. Large language model–based chatbots offer promising solutions by leveraging natural processing. This review explores their convergence, highlighting opportunities synergy. We discuss challenges in Ribo-seq and how mitigate them, facilitating scientific discovery. Through case studies, we illustrate chatbots’ potential contributions, including data result interpretation. Despite the absence of applied examples, existing software underscores value large model. anticipate pivotal role future analysis, overcoming limitations. Challenges such as model bias privacy require attention, but emerging trends promise. The integration models holds immense advancing translational regulation gene expression understanding.

Language: Английский

Effective Summarization of ChatGPT User Feedback: Integrating Topic Detection with Markov Chains DOI Creative Commons
Bouchra El Akraoui, Imane Chakour, Cherki Daoui

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 25, 2024

Abstract In the era of ChatGPT, user feedback is essential for understanding this advanced technology and navigating evolving field artificial intelligence. To gain insights into risks, limitations, areas improvement, our study focuses on analyzing Twitter interactions. We address unique challenges posed by Twitters characteristicsbrevity, informality, rapid temporal changesin summarizing user-generated content related to ChatGPT. Our research emphasizes interest-based multi-tweet topic detection summarization, tackling such as brevity, noise, dynamic content. these challenges, we introduce a approach that merges Latent Semantic Analysis (LSA) Non-negative Matrix Factorization (NMF) effective in 500,036 English tweets from first three months after ChatGPT’s announcement. method rigorously compared with traditional models Dirichlet Allocation (LDA) BERTopic. The coherence scores, which reflect quality extracted topics, show values 0.68 LDA, 0.51 BERTopic, an impressive 0.92 proposed model, demonstrating 92.67 % improvement effectiveness. goals extend beyond include comprehensive summarization using Markov Decision Processes (MDP). When LexRank, TextRank, TopicRank, significantly enhances accuracy, evidenced improved ROUGE evaluation metrics. conclusion, represents significant advancement accuracy automatic summarization.

Language: Английский

Citations

0
GPT in Finance Forecasting DOI Creative Commons
Boyu Zhang

Advances in Economics Management and Political Sciences, Journal Year: 2024, Volume and Issue: 99(1), P. 73 - 80

Published: Sept. 10, 2024

As society evolves, more and people will be predicting examining the financial markets as it aids in decision making, risk management, promoting economic growth stability. Large amounts of historical data cannot keep up with rapid changes markets, which can affect accuracy forecasts made using traditional methods, but GPT uses other artificial intelligence techniques to capture complex market relationships. These analyze large deal anomalies produce accurate forecasts. In this paper, we examine how applied forecasting. Firstly, functionality technical approach is introduced, followed by a study forecasting application problems challenges are identified. It found that successfully address shortcomings evaluating textual data, capturing nonlinear correlations, performing multifactor analysis. addition, perform sentiment analysis, adapt real time, improve thoroughness When used conjunction variety sources becomes thorough accurate. improves objectivity real-time nature minimizing human bias constantly updating models. To better forecasts, integrates future.

Language: Английский

Citations

0
ERAT-DLoRA: Parameter-efficient tuning with enhanced range adaptation in time and depth aware dynamic LoRA DOI

Luo Dan,

Kangfeng Zheng, Chunhua Wu

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 614, P. 128778 - 128778

Published: Oct. 30, 2024

Language: Английский

Citations

0
Research on the Acceptance Behavior of Accounting Professionals towards Large Language Models and Generative Artificial Intelligence: An Analysis Based on ChatGPT DOI

Yawen Zhu,

Bing Bai,

Yangyang Zhang

et al.

Published: Oct. 19, 2024

Language: Английский

Citations

0
Exploring the potential of large language model–based chatbots in challenges of ribosome profiling data analysis: a review DOI Creative Commons
Zheyu Ding, Rong Wei,

Jianing Xia

et al.

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 26(1)

Published: Nov. 22, 2024

Abstract Ribosome profiling (Ribo-seq) provides transcriptome-wide insights into protein synthesis dynamics, yet its analysis poses challenges, particularly for nonbioinformatics researchers. Large language model–based chatbots offer promising solutions by leveraging natural processing. This review explores their convergence, highlighting opportunities synergy. We discuss challenges in Ribo-seq and how mitigate them, facilitating scientific discovery. Through case studies, we illustrate chatbots’ potential contributions, including data result interpretation. Despite the absence of applied examples, existing software underscores value large model. anticipate pivotal role future analysis, overcoming limitations. Challenges such as model bias privacy require attention, but emerging trends promise. The integration models holds immense advancing translational regulation gene expression understanding.

Language: Английский

Citations

0