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Large Language Models in Psychiatry:Current Applications,Limitations,and Future Scope

Big Data Mining and Analytics 2024年第4期7卷 1148-1168页

作者： Zhe Liu Yihang Bao Shuai Zeng Ruiyi Qian Miaohan Deng An Gu Jianye Li Weidi Wang Wenxiang Cai Wenhao Li Han Wang Dong Xu Guan Ning Lin Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicineand School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200030China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA School of Information Science and Technology Institute of Computational BiologyNortheast Normal UniversityChangchun 130024China

With the advancements in Artificial Intelligence(AI)technology,Large Language Models(LLMs)provide outstanding capabilities for natural language understanding and generation,enhancing various *** psychiatry,LLMs can empower healthcare by analyzing vast amounts of medical data to improve diagnostic accuracy,enhance therapeutic communication,and personalize patient care with their strength in understanding and generating human-like *** clinical AI,developing and utilizing robust and interpretable models has been a longstanding *** survey investigates the current psychiatric practice of LLMs,along with a series of corpus resources that could be used for training psychiatric *** discuss the limitations concerning LLM reproducibility,capabilities,usability,interpretability in clinical settings,and ethical ***,we propose potential future directions for research,clinical application,and education in psychiatric ***,we discuss the challenge of integrating LLMs into the evolving landscape of healthcare in real-world scenarios.

关键词： Artificial Intelligence(AI) Large Language Model(LLM) psychiatry medical application

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systems-based approaches to study immunometabolism

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Cellular & Molecular Immunology 2022年第3期19卷 409-420页

作者： Vinee Purohit Allon Wagner Nir Yosef Vijay K.Kuchroo Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases Brigham and Women’s HospitalHarvard Medical SchoolBostonMA02115USA Broad Institute of MIT and Harvard CambridgeMA02141USA Department of Electrical Engineering and Computer Science University of CaliforniaBerkeleyCA94720USA Center for Computational Biology University of CaliforniaBerkeleyCA94720USA

Technical advances at the interface of biology and computation,such as single-cell RNA-sequencing(scRNA-seq),reveal new layers of complexity in cellular *** emerging area of investigation using the systems biology approach is the study of the metabolism of immune *** diverse spectra of immune cell phenotypes,sparsity of immune cell numbers in vivo,limitations in the number of metabolites identified,dynamic nature of cellular metabolism and metabolic fluxes,tissue specificity,and high dependence on the local milieu make investigations in immunometabolism challenging,especially at the single-cell *** this review,we define the systemic nature of immunometabolism,summarize cell-and system-based approaches,and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune *** close the review by discussing the applications and shortcomings of metabolic modeling *** systems-oriented studies of metabolism expected to become a mainstay of immunological research,an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

关键词： Immunometabolism Metabolic techniques GSMM Metabolic modeling systems biology

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DeeReCT-APA:Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

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Genomics, Proteomics & Bioinformatics 2022年第3期20卷 483-495页

作者： Zhongxiao Li Yisheng Li Bin Zhang Yu Li Yongkang Long Juexiao Zhou Xudong Zou Min Zhang Yuhui Hu Wei Chen Xin Gao King Abdullah University of Science and Technology(KAUST) Computational Bioscience Research Center(CBRC)ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionThuwal 23955-6900Saudi Arabia Department of Biology Southern University of Science and Technology(SUSTech)Shenzhen 518055China Cancer Science Institute of Singapore Singapore 117599Singapore

Alternative polyadenylation(APA)is a crucial step in post-transcriptional *** bioinformatic studies have mainly focused on the recognition of polyadenylation sites(PASs)in a given genomic sequence,which is a binary classification ***,computational methods for predicting the usage level of alternative PASs in the same gene have been ***,all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into *** address this,here we propose a deep learning architecture,Deep Regulatory Code and Tools for Alternative Polyadenylation(DeeReCT-APA),to quantitatively predict the usage of all alternative PASs of a given *** accommodate different genes with potentially different numbers of PASs,DeeReCT-APA treats the problem as a regression task with a variable-length *** on a convolutional neural network-long short-term memory(CNN-LSTM)architecture,DeeReCT-APA extracts sequence features with CNN layers,uses bidirectional LSTM to explicitly model the interactions among competing PASs,and outputs percentage scores representing the usage levels of all PASs of a *** addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene,we show that our method consistently outperforms other existing methods on three different tasks for which they are trained:pairwise comparison task,highest usage prediction task,and ranking ***,we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA *** code and data are available at https://***/lzx325/DeeReCT-APA-repo.

关键词： Polyadenylation Gene regulation Sequence analysis Deep learning Bioinformatics

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Stochastic dynamics of the logistic growth model subjected to environmental perturbations

Stochastic dynamics of the logistic growth model subjected t...

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Control Technology and Applications (CCTA),

作者： Sayeh Rezaee Cesar Nieto Zahra Vahdat Abhyudai Singh Department of Electrical and Computer Engineering University of Delaware Newark DE USA Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology University of Delaware Newark DE USA

The stochastic logistic model is widely used to capture random fluctuations arising from birth-death processes in ecological populations. We use this model to study the impact of environmental perturbations that may occur naturally or as a consequence of population harvesting. In our model formulation, environmental perturbations occur randomly as per a Poisson process, and the perturbations result in each individual dying with a certain probability of death. Moment closure schemes are employed to derive expressions for the mean and variability in population numbers. Moreover, to quantify the impact of population extinction in our model we compute the probability of extinction using the Finite State Projection (FSP) numerical method. Our analysis shows that rare environmental perturbations with a high probability of death lead to overall larger random fluctuations and extinction risk as compared to frequent perturbations with a low probability of death. Finally, we formulate the problem in the context of population harvesting to find the optimal harvesting rate that maximizes the cumulative yield.

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Optimizing Precision in Cellular Clocks Through Self-Regulated Accumulation Of Molecules

Optimizing Precision in Cellular Clocks Through Self-Regulat...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Sayeh Rezaee César Nieto Abhyudai Singh Department of Electrical and Computer Engineering University of Delaware Newark DE USA Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology University of Delaware Newark DE USA

The timing of cellular events is often determined by the instant when the level of a chemical species crosses a threshold from an initial value of zero. To attenuate the inherent random fluctuations in this first-passage time, the production rate can be a function of the current species level by implementing a feedback or self-regulation. In this study, we aim to identify the self-regulation strategy that minimizes timing noise around a specified mean first-passage time. Previous results showed that no-feedback is the optimal strategy in the absence of molecule degradation. Based on this result, we study the case where the species may undergo degradation. First, we set the feedback to be a linear function of the number of molecules. We derive analytical formulas concluding that the optimal strategy involves positive feedback, whereby the production rate increases with the number of molecules. When feedback constraints are removed, our numerical methods suggest that a mixed self-regulation scheme is optimal. This strategy involves an initial positive feedback phase, followed by a sharp transition to a negative feedback scheme in proximity to the threshold. In summary, this research quantifies the fundamental limits of timing noise reduction, considering different strategies of self-regulation in situations where time-keeping species may be subjected to degradation.

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Dish-Trend: Intervention Modeling Simulator That Accounts for Trend Influences

Dish-Trend: Intervention Modeling Simulator That Accounts fo...

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2021 Winter Simulation Conference, WSC 2021

作者： Andjelkovic, Stefan Miskov-Zivanov, Natasa Computational and Systems Biology Joint Carnegie Mellon University University of Pittsburgh Computational Biology PhD Program University of Pittsburgh PittsburghPA15213 United States Electrical and Computer Engineering Bioengineering Computational and Systems Biology University of Pittsburgh PittsburghPA15213 United States

ISBN: (纸本)9781665433112

Simulation on directed graphs is an important method for understanding the dynamics in the systems where connectivity graphs contain cycles. Discrete Stochastic Heterogeneous Simulator (DiSH) is one of the simulation tools with wide application, which uses regulator values to calculate state updates of regulated elements. Here we present a new simulation approach DiSH-trend which also takes into account the trends in regulating elements. We demonstrate the features of trend-based regulation, as well as hybrid regulation, which is a combination of the trend-and level-based approaches. The modeling capabilities are demonstrated on a small toy model, showcasing different functionalities. Real-world capabilities are demonstrated on a larger network model of food insecurity in the Ethiopian region Oromia. Adding trend-based regulation to models results in increased modeling flexibility, and hybrid regulation improves qualitative dynamic behavior prediction. With appropriate data, DiSH-trend becomes a powerful tool for exploring intervention strategies. © 2021 IEEE.

关键词： Directed graphs

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Applications of Superpixels on Phase Contrast Microscopy 5

Applications of Superpixels on Phase Contrast Microscopy

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5th International Conference on Transdisciplinary AI, TransAI 2023

作者： On, Vincent Zahedi, Atena Phandthong, Rattapol California State University Bakersfield Department of Computer and Electrical Engineering and Computer Science BakersfieldCA United States Stem Cell Research Center University of California Irvine IrvineCA United States University of California Riverside Department of Molecular Cell and Systems Biology RiversideCA United States

ISBN: (纸本)9798350358018

Cellular microscopy is enhanced by computational paradigms such as image processing, computer vision, and machine learning. Image segmentation is vital for quantifying cell images, enabling tracking and subsequent applications. Superpixel algorithms offer a promising solution to challenges in phase contrast image segmentation, as they group pixels with similar characteristics into meaningful regions. By preserving essential structural information and reducing complexity, superpixels improve accuracy in cell-related measurements and facilitate the study of dynamic cellular processes. This paper presents a comparison of popular superpixel algorithms applied to cellular microscopy, highlighting their contributions to image analysis and interpretation in cell biology research. © 2023 IEEE.

关键词： Image segmentation

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Decentralized Waveform Co-Design for Integrated Sensing and Communications systems via Approximate Dynamic Programming

Decentralized Waveform Co-Design for Integrated Sensing and ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Shammi A. Doly Alex R. Chiriyath Andrew Herschfelt Md Ali Azam Shankarachary Ragi Daniel W. Bliss School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ Center for Wireless Information Systems and Computational Architectures (WISCA) Arizona State University Tempe AZ USA

The increasing demand for cost-efficient yet reliable swarms of multi-function unmanned aerial systems (UASs) has tremendous potential in both military and civilian applications. Advancements in ISACs research, such as RF Convergence exclusively provide a promising path toward implementing multi-function UASs, often limited by insufficient front-end capabilities as the network scales up. Because the interconnection bandwidth and computational complexity required to process the data among the ISACs nodes using centralized architectures are high. In this paper, we propose a decentralized waveform co-design method to reduce computational complexity while maximizing the mutual benefits of users in a multi-function UASs network based on the theory of decentralized, partially-observable Markov decision processes (Dec-POMDPs). To address the computational intractability of solving Dec-POMDPs (as with any decision-theoretic framework), we extend an approximate dynamic programming approach we recently developed-nominal belief-state optimization (NBO) in the context of radar-communications waveform co-design. We conduct a numerical study to benchmark the performance of the Dec-POMDP-based waveform design approach against a centralized decision optimization approach, which demonstrates a 50% reduction in computation time at the cost of moderate loss in ranging precision.

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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