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The objective of this research is to introduce a network specialized in predicting drugs that can be repurposed by investigating real-world evidence sources, such as clinical trials and biomedical literature. Specifically, it aims to generate drug combination therapies for complex diseases (e.g., cancer, Alzheimer's). We present a multilayered network medicine approach, empowered by a highly configured ChatGPT prompt engineering system, which is constructed on the fly to extract drug mentions in clinical trials. Additionally, we introduce a novel algorithm that connects real-world evidence with disease-specific signaling pathways (e.g., KEGG database). This sheds light on the repurposability of drugs if they are found to bind with one or more protein constituents of a signaling pathway. To demonstrate, we instantiated the framework for breast cancer and found that, out of 46 breast cancer signaling pathways, the framework identified 38 pathways that were covered by at least two drugs. This evidence signals the potential for combining those drugs. Specifically, the most covered signaling pathway, ID hsa:2064, was covered by 108 drugs, some of which can be combined. Conversely, the signaling pathway ID hsa:1499 was covered by only two drugs, indicating a significant gap for further research. Our network medicine framework, empowered by GenAI, shows promise in identifying drug combinations with a high degree of specificity, knowing the exact signaling pathways and proteins that serve as targets. It is noteworthy that ChatGPT successfully accelerated the process of identifying drug mentions in clinical trials, though further investigations are required to determine the relationships among the drug mentions., Comment: 9 pages double columns, 5 figures, 3 algorithms, 3 tables, and 1 listing, Submitted to IEEE MedAI'24 Conference, to be held November 15-17, Chongqing, China

The analysis for the strange and multi-strange hadrons production is presented for proton-proton (pp) collisions at $\sqrt{S_{_{NN}}} =$ 200 GeV as reported by the PYTHIA 8 event generator simulations. The strangeness enhancement is studied in details at $\sqrt{s_{_{NN}}} = $ 200 GeV in proton-proton (pp). We preform the transverse momentum distributions for high and low multiplicity events of the strange and multi-strange hadrons. Here we present the observation of strangeness enhancement in pp collisions for $K_{s}^{+}(u\bar{s})$, $K_{s}^{-}(s\bar{u})$, $\Lambda^{0}(uds)$, $\Xi^{0}(uss)$, $\Xi^{-}(dss)$, $\Omega^{-}(sss)$ production. At the RHIC energy, the strangeness content yield increases with the event multiplicity.

Addressing the global challenge of breast cancer, this research explores the fusion of generative AI, focusing on ChatGPT 3.5 turbo model, and the intricacies of breast cancer risk assessment. The research aims to evaluate ChatGPT's reasoning capabilities, emphasizing its potential to process rules and provide explanations for screening recommendations. The study seeks to bridge the technology gap between intelligent machines and clinicians by demonstrating ChatGPT's unique proficiency in natural language reasoning. The methodology employs a supervised prompt-engineering approach to enforce detailed explanations for ChatGPT's recommendations. Synthetic use cases, generated algorithmically, serve as the testing ground for the encoded rules, evaluating the model's processing prowess. Findings highlight ChatGPT's promising capacity in processing rules comparable to Expert System Shells, with a focus on natural language reasoning. The research introduces the concept of reinforcement explainability, showcasing its potential in elucidating outcomes and facilitating user-friendly interfaces for breast cancer risk assessment., Comment: 9 pages, 5 figures, 3 algorithms, 1 table, submitted to the IEEE MedAI'24 Conference

Background: The emergence of generative AI tools, empowered by Large Language Models (LLMs), has shown powerful capabilities in generating content. To date, the assessment of the usefulness of such content, generated by what is known as prompt engineering, has become an interesting research question. Objectives Using the mean of prompt engineering, we assess the similarity and closeness of such contents to real literature produced by scientists. Methods In this exploratory analysis, (1) we prompt-engineer ChatGPT and Google Bard to generate clinical content to be compared with literature counterparts, (2) we assess the similarities of the contents generated by comparing them with counterparts from biomedical literature. Our approach is to use text-mining approaches to compare documents and associated bigrams and to use network analysis to assess the terms' centrality. Results The experiments demonstrated that ChatGPT outperformed Google Bard in cosine document similarity (38% to 34%), Jaccard document similarity (23% to 19%), TF-IDF bigram similarity (47% to 41%), and term network centrality (degree and closeness). We also found new links that emerged in ChatGPT bigram networks that did not exist in literature bigram networks. Conclusions: The obtained similarity results show that ChatGPT outperformed Google Bard in document similarity, bigrams, and degree and closeness centrality. We also observed that ChatGPT offers linkage to terms that are connected in the literature. Such connections could inspire asking interesting questions and generate new hypotheses., Comment: 15 pages, 10 figures, 4 tables; and 1 algorithm

Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that ``AI generated content may exhibit a distinctive behavior that can be separated from scientific articles''. In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. As for the classification step, it was performed using 300 articles per condition. The actual label steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer's. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80% to 94%, outperforming common data mining algorithms, which scored F1 values between 38% and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant step on the way to combating fake science., Comment: 18 pages, 8 figures, 8 tables, 5 algorithms

Since the launch of various generative AI tools, scientists have been striving to evaluate their capabilities and contents, in the hope of establishing trust in their generative abilities. Regulations and guidelines are emerging to verify generated contents and identify novel uses. we aspire to demonstrate how ChatGPT claims are checked computationally using the rigor of network models. We aim to achieve fact-checking of the knowledge embedded in biological graphs that were contrived from ChatGPT contents at the aggregate level. We adopted a biological networks approach that enables the systematic interrogation of ChatGPT's linked entities. We designed an ontology-driven fact-checking algorithm that compares biological graphs constructed from approximately 200,000 PubMed abstracts with counterparts constructed from a dataset generated using the ChatGPT-3.5 Turbo model. In 10-samples of 250 randomly selected records a ChatGPT dataset of 1000 "simulated" articles , the fact-checking link accuracy ranged from 70% to 86%. This study demonstrated high accuracy of aggregate disease-gene links relationships found in ChatGPT-generated texts., Comment: Accepted in the 24th International Conference on Computational Science (ICCS'24), in April 1st 2024. Will appear in the Springer LNCS proceeding as a short paper

The impact of dispersion relations, anisotropy, and Brillouin zone structure on intrinsic phonon scattering rates has been assessed within the harmonic approximation-perturbation theory approach for lattice dynamics. Anisotropic nonlinear elastic continuum has been considered with various levels of representation of phonon dispersion and Brillouin zone shape, and with Gr\"uneisen parameter used as an average measure of crystal anharmonicity. In addition, thermal conductivity prediction of different models for the treatment of the off-diagonal elements of phonon collision operator are compared. For a model system, argon, with a relatively high anisotropy ratio, the results show that accounting for anisotropy is critical for accurate determination of available phase space for 3-phonon scattering and scattering rates. Moreover, widely spread approximations such as isotropic continuum and Single Mode Relaxation Time are found unreliable, even for cubic systems. The success of these approximations is demonstrated to be a direct result of error cancellations. By benchmarking against our iterative solution of Boltzmann Transport Equation, which achieves excellent agreement with experimental thermal conductivity data for solid argon (2-80 K), we show the essential importance of considering coupling terms of phonon scattering kernel at phonon mode level, and not in a statistical average sense as, for example, Callaway model does. Moreover, our results manifest the role played by coherent phonon scattering near the melting temperature, in agreement with molecular dynamics findings, which serves as an evidence for the crossover between heat diffusion mediated by particle-like phonons (incoherent scattering) and wave-like heat propagation due to phonon coherent scattering. Furthermore, sensitivity of conductivity prediction to phonon spectrum is revealed to change over temperature., Comment: 50 pages, 16 figures

In this paper, we introduce the JavaScript Open-source Library (\libname), a high-level grammar for representing data in visualization graphs and plots. \libname~perspective on the grammar of graphics is unique; it provides state-of-art rules for encoding visual primitives that can be used to generate a known scene or to invent a new one. \libname~has ton rules developed specifically for data-munging, mapping, and visualization through many layers, such as algebra, scales, and geometries. Additionally, it has a compiler that incorporates and combines all rules specified by a user and put them in a flow to validate it as a visualization grammar and check its requisites. Users can customize scenes through a pipeline that either puts customized rules or comes with new ones. We evaluated \libname~on a multitude of plots to check rules specification of customizing a specific plot. Although the project is still under development and many enhancements are under construction, this paper describes the first developed version of \libname, circa 2016, where an open-source version of it is available. One immediate practical deployment for JSOl is to be integrated with the open-source version of the Data Visualization Platform (DVP) \citep{Yousef2019DVP-arxiv}