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NATURE BIOTECHNOLOGY 2020年第9期38卷 1098-1098页

作者： Duran-Frigola, Miquel Pauls, Eduardo Guitart-Pla, Oriol Bertoni, Martino Alcalde, Victor Amat, David Juan-Blanco, Teresa Aloy, Patrick Joint IRB-BSC-CRG Program in Computational Biology Institute for Research in Biomedicine (IRB Barcelona) The Barcelona Institute of Science and Technology Barcelona Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain

Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.

关键词： Chemical biology Chemical genetics Chemical libraries Cheminformatics Drug discovery

The Fisher Geometry and Geodesics of the Multivariate Normals, without Differential Geometry

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arXiv 2023年

作者： Lawson, Brodie A.J. Burrage, Kevin Mengersen, Kerrie Santos, Rodrigo Weber dos Centre for Data Science Queensland University of Technology Australia School of Mathematical Sciences Queensland University of Technology Australia ARC Centre of Excellence for Plant Success in Nature and Agriculture Australia Department of Computer Science University of Oxford United Kingdom Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil

Choosing the Fisher information as the metric tensor for a Riemannian manifold provides a powerful yet fundamental way to understand statistical distribution families. Distances along this manifold become a compelling measure of statistical distance, and paths of shorter distance improve sampling techniques that leverage a sequence of distributions in their operation. Unfortunately, even for a distribution as generally tractable as the multivariate normal distribution, this information geometry proves unwieldy enough that closed-form solutions for shortest-distance paths or their lengths remain unavailable outside of limited special cases. In this review we present for general statisticians the most practical aspects of the Fisher geometry for this fundamental distribution family. Rather than a differential geometric treatment, we use an intuitive understanding of the covariance-induced curvature of this manifold to unify the special cases with known closed-form solution and review approximate solutions for the general case. We also use the multivariate normal information geometry to better understand the paths or distances commonly used in statistics (annealing, Wasserstein). Given the unavailability of a general solution, we also discuss the methods used for numerically obtaining geodesics in the space of multivariate normals, identifying remaining challenges and suggesting methodological *** Codes 62B11 (primary), 62-02, 62-08 (secondary) © 2023, CC BY.

关键词： Geometry

Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why

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arXiv 2024年

作者： Xie, Pinchen Qiu, Yunrui Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry University of Wisconsin-Madison MadisonWI53706 United States AI for Science Institute Beijing100080 China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100084 China

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained conformational dynamics. Constrained by the fluctuation-dissipation theorem, the approach can build coarse-grained models in dynamical consistency with all-atom molecular dynamics. We also propose practical criteria for AIGLE to enforce long-term dynamical consistency. Case studies of a toy polymer, with 20 coarse-grained sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling coarse-grained conformational dynamics in practice. © 2024, CC BY.

关键词： Coarse-grained modeling

multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer

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PeerJ Computer science 2023年 9卷

作者： Kim, Gihyeon Park, Young Mi Yoon, Hyun Jung Choi, Jang-Hwan Department of Computational Medicine Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Molecular Medicine College of Medicine Ewha Womans University Seoul Korea Republic of Department of Radiology Veterans Health Service Medical Center Seoul Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Artificial Intelligence Ewha Womans University Seoul Korea Republic of

Predicting recurrence in patients with non-small cell lung cancer (NSCLC) before treatment is vital for guiding personalized medicine. Deep learning techniques have revolutionized the application of cancer informatics, including lung cancer timeto-event prediction. Most existing convolutional neural network (CNN) models are based on a single two-dimensional (2D) computational tomography (CT) image or three-dimensional (3D) CT volume. However, studies have shown that using multiscale input and fusing multiple networks provide promising performance. This study proposes a deep learning-based ensemble network for recurrence prediction using a dataset of 530 patients with NSCLC. This network assembles 2DCNNmodels of various input slices, scales, and convolutional kernels, using a deep learning-based feature fusion model as an ensemble strategy. The proposed framework is uniquely designed to benefit from (i) multiple 2D in-plane slices to provide more information than a single central slice, (ii) multi-scale networks and multi-kernel networks to capture the local and peritumoral features, (iii) ensemble design to integrate features from various inputs and model architectures for final prediction. The ensemble of five 2D-CNN models, three slices, and two multi-kernel networks, using 5×5 and 6×6 convolutional kernels, achieved the best performance with an accuracy of 69.62%, area under the curve (AUC) of 72.5%, F1 score of 70.12%, and recall of 70.81%. Furthermore, the proposed method achieved competitive results compared with the 2D and 3D-CNN models for cancer outcome prediction in the benchmark studies. Our model is also a potential adjuvant treatment tool for identifying NSCLC patients with a high risk of recurrence. © 2023 Kim et al.

关键词： Neural network models

Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes

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arXiv 2025年

作者： Wu, Da Wang, Zhanliang Nguyen, Quan Wang, Kai Raymond G. Perelman Center for Cellular and Molecular Therapeutics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Applied Mathematics and Computational Science Graduate Program University of Pennsylvania PhiladelphiaPA19104 United States Bioengineering Graduate Program University of Pennsylvania PhiladelphiaPA19104 United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

Background: Several studies show that large language models (LLMs) struggle with phenotype-driven gene prioritization for rare diseases. These studies typically use Human Phenotype Ontology (HPO) terms to prompt foundation models like GPT and LLaMA to predict candidate genes. However, in real-world settings, foundation models are not optimized for domain-specific tasks like clinical diagnosis, yet inputs are unstructured clinical notes rather than standardized terms. How LLMs can be instructed to predict candidate genes or disease diagnosis from unstructured clinical notes remains a major challenge. Methods: We introduce RAG-driven CoT and CoT-driven RAG, two methods that combine Chain-of-Thought (CoT) and Retrieval Augmented Generation (RAG) to analyze clinical notes. A five-question CoT protocol mimics expert reasoning, while RAG retrieves data from sources like HPO and OMIM (Online Mendelian Inheritance in Man). We evaluated these approaches on rare disease datasets, including 5,980 Phenopacket-derived notes, 255 literature-based narratives, and 220 in-house clinical notes from Children’s Hospital of Philadelphia. Results: We found that recent foundations models, including Llama 3.3-70B-Instruct and DeepSeek-R1-Distill-Llama-70B, outperformed earlier versions such as Llama 2 and GPT-3.5. We also showed that RAG-driven CoT and CoT-driven RAG both outperform foundation models in candidate gene prioritization from clinical notes;in particular, both methods with DeepSeek backbone resulted in a top-10 gene accuracy of over 40% on Phenopacket-derived clinical notes. RAG-driven CoT works better for high-quality notes, where early retrieval can anchor the subsequent reasoning steps in domain-specific evidence, while CoT-driven RAG has advantage when processing lengthy and noisy notes. Conclusions: Integrating CoT and RAG enhances LLMs’ understanding of clinical notes in the context of rare disease diagnosis, and can facilitate various downstream medical tasks. © 2025, CC

关键词： Chains

An IoT-based Immersive Smart Home System for Seniors with Neurocognitive Disorders 3

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An IoT-based Immersive Smart Home System for Seniors with Ne...

3rd International Workshop on Empowering People in Dealing with Internet of Things Ecosystems, EMPATHY 2022

作者： Alabood, Lorans Maurer, Frank The Computational Media Design program University of Calgary 528 ICT Building 856 Campus Pl NW CalgaryABT2N 4V8 Canada The Department of Computer Science University of Calgary 550 ICT Building 856 Campus Pl NW CalgaryABT2N 4V8 Canada

Commercial Internet of Things (IoT) devices allow for creating affordable and customized smart home systems for seniors with neurocognitive disorders (SwNCDs), such as dementia and Alzheimer's. An IoT-based system can support SwNCDs in completing daily living activities by sending reminders and notifications via smartphone applications. However, this approach comes with two main challenges that negatively affect the overall system usability. Firstly, customizing and interacting with IoT devices requires a certain level of technology literacy, which many SwNCDs and caregivers don't have. Secondly, relying solely on smartphone notifications is not suitable for homecare purposes. In response to these challenges, we present the design, development and architecture of our immersive IoT-based smart home system prototype. Our proposed system allows senior users to interact with the smart home system effortlessly and hands-free via a head-mounted mixed reality device. In addition, the current prototype supports two use cases: medication reminders and cooking safety. © 2022 Copyright for this paper by its authors.

关键词： Internet of things

MultiCalib4DEB: A toolbox exploiting multimodal optimisation in Dynamic Energy Budget parameters calibration

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arXiv 2023年

作者： Robles, Juan Francisco Chica, Manuel Filgueira, Ramón Agüera, Antonio Damas, Sergio Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Benthic resources Norwegian Institute of Marine Research BergenNO-5817 Norway Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Marine Affairs Program Life Sciences Centre Dalhousie University 1459 Oxford Street HalifaxNSB3H 4R2 Canada

1. Calibration is a crucial step for the validation of computational models and a challenging task to accomplish. 2. Dynamic Energy Budget (DEB) theory has experienced an exponential rise in the number of published papers, which in large part has been made possible by the DEBtool toolbox. Multimodal evolutionary optimisation could provide DEBtool with new capabilities, particularly relevant on the provisioning of equally optimal and diverse solutions. 3. In this paper we present MultiCalib4DEB, a MATLAB toolbox directly integrated into the existing DEBtool toolbox, which uses multimodal evolutionary optimisation algorithms to find multiple global and local optimal and diverse calibration solutions for DEB models. 4. MultiCalib4DEB adds powerful calibration mechanisms, statistical analysis, and visualisation methods to the DEBtool toolbox and provides a wide range of outputs, different calibration alternatives, and specific tools to strengthen the DEBtool calibration module and to aid DEBtool users to evaluate the performance of the calibration results. © 2023, CC BY-NC-ND.

关键词： Calibration

SYNAPTIC SPINE HEAD MORPHODYNAMICS FROM GRAPH GRAMMAR RULES FOR ACTIN DYNAMICS

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arXiv 2025年

作者： Hur, Matthew Bartol, Thomas Rangamani, Padmini Sejnowski, Terrence Mjolsness, Eric Program in Mathematical Computational and Systems Biology Univeristy of California Irvine IrvineCA United States Computational Neurobiology Laboratory Salk Institute for Biological Studies La Jolla CA United States Departments of Pharmacology and Mechanical and Aerospace Engineering University of California San Diego La Jolla CA United States Deparment of Neurobiology University of California San Diego La Jolla CA United States Departments of Computer Science and Mathematics Univeristy of California Irvine IrvineCA United States

There is a morphodynamic component to synaptic learning by which changes in dendritic (postsynaptic) spine head size are associated with the strengthening or weakening of the synaptic connection between two neurons, in response to the temporal correlation of local presynaptic and postsynaptic signals. These morphological factors are in turn sculpted by the dynamics of the actin cytoskeleton. In this paper, we use Dynamical Graph Grammars (DGGs) implemented within a computer algebra system to model how networks of actin filaments can dynamically grow or shrink, reshaping the spine head. Dynamical Graph Grammars (DGGs) provide a well-defined way to accommodate dynamically changing system structure such as active cytoskeleton represented using dynamic graphs, within nonequilibrium statistical physics under the master equation. We show that DGGs can also incorporate biophysical forces between graph-connected objects at a finer time scale, with specialized DGG kinetic rules obeying biophysical constraints of Galilean invariance, conservation of momentum, and dissipation of conserved global energy. We use graph-local energy functions for cytoskeleton networks interacting with membranes, and derive DGG rules from the specialization of dissipative stochastic dynamics - separated into dissipative and thermal noise rule types - to a mutually exclusive and exhaustive collection of graph-local neighborhood types for the rule left hand sides. The dissipative rules comprise a stochastic version of gradient descent dynamics. The thermal noise rules use a Gaussian approximation of each position coordinate to sample jitter-like displacements. For the spine head model we designed and implemented DGG grammar mathematical sub-models including actin network growth, non-equilibrium statistical mechanics, and filament-membrane mechanical interaction to regulate the re-writing of graph objects. We simulate emergent biophysics of simplified networks of actin polymers and their interactions

关键词： Stochastic models

Exploring the mutation tendency of a specific region in the virus taxonomic groups using data mining techniques

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Exploring the mutation tendency of a specific region in the ...

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Je, Mikyung Cho, Myeongji Kim, Yoon Jung Son, Hyeon S. Seoul National University Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul Korea Republic of Honam National Institute of Biological Resources Division of Bio It Mokpo Korea Republic of Seoul National University Lab. of Computational Virology and Viroinformatics Graduate School of Public Health Seoul Korea Republic of Seoul National University Institute of Health and Environment Seoul Korea Republic of

ISBN: (纸本)9781665480451

The GDNQ motif is mainly found in nonsegmented (-)ssRNA viruses and plays a role in the catalytic and polymerase activity pathways. However, in some recently reported dsRNA mycoviruses, the GDNQ motif of the (-)ssRNA virus group is found instead of the GDD motif commonly found in the dsRNA virus group. Therefore, in this study, data mining techniques were used to explore the mutation tendency of a specific region in the virus taxonomic groups. Viral protein sequences were obtained from the NCBI Virus data repository, and the performance of several known string-searching algorithms (BF, KMP, and BM) was evaluated to establish an optimal strategy for extracting useful information from sequence big data. Then, the pattern-matching performance of the algorithm was tested to detect whether the conserved region sequence was a common motif type or a mutated motif type. As a result of the analysis, it was confirmed that the BF method produced the fastest results. However, the BM method recorded higher accuracy. Through the virus mutation tendency derived from the analysis results, it was confirmed that 7.1% of dsRNA viruses had a mutated motif type based on the total data analyzed and that it was relatively recent that ssRNA virus motif variants began to be discovered in dsRNA viruses. It is expected that additional in-depth analysis of the host range and infection route of viruses with the mutation will help understand the evolution of dsRNA and ssRNA mycoviruses. © 2022 IEEE.

关键词： Big data

CNEReg Interprets Ruminant-specific Conserved Non-coding Elements by Developmental Gene Regulatory Network

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Genomics, Proteomics & Bioinformatics 2023年第3期21卷 632-648页

作者： Xiangyu Pan Zhaoxia Ma Xinqi Sun Hui Li Tingting Zhang Chen Zhao Nini Wang Rasmus Heller Wing Hung Wong Wen Wang Yu Jiang Yong Wang Key Laboratory of Animal Genetics Breeding and Reproduction of Shaanxi ProvinceCollege of Animal Science and TechnologyNorthwest A&F UniversityYangling 712100China Department of Medical Research Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Guangdong Cardiovascular Institute Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources College of Animal Science and TechnologyGuangxi UniversityNanning 530005China Section for Computational and RNA Biology Department of BiologyUniversity of CopenhagenCopenhagen DK-2100Denmark Department of Statistics Department of Biomedical Data ScienceBio-X ProgramStanford UniversityStanfordCA 94305USA Center for Ecological and Environmental Sciences Northwestern Polytechnical UniversityXi’an 710072China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of ZoologyChinese Academy of SciencesKunming 650223China Center for Excellence in Animal Evolution and Genetics Chinese Academy of SciencesKunming 650223China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

The genetic information coded in DNA leads to trait innovation via a gene regulatory network(GRN)in ***,we developed a conserved non-coding element interpretation method to integrate multi-omics data into gene regulatory network(CNEReg)to investigate the ruminant multi-chambered stomach *** generated paired expression and chromatin accessibility data during rumen and esophagus development in sheep,and revealed 1601 active ruminantspecific conserved non-coding elements(active-RSCNEs).To interpret the function of these activeRSCNEs,we defined toolkit transcription factors(TTFs)and modeled their regulation on rumenspecific genes via batteries of active-RSCNEs during *** developmental GRN revealed 18 TTFs and 313 active-RSCNEs regulating 7 rumen functional ***,6 TTFs(OTX1,SOX21,HOXC8,SOX2,TP63,and PPARG),as well as 16 active-RSCNEs,functionally distinguished the rumen from the *** study provides a systematic approach to understanding how gene regulation evolves and shapes complex traits by putting evo-devo concepts into practice with developmental multi-omics data.

关键词： Trait innovation Gene regulatory network Conserved non-coding element Toolkit transcription factor Ruminant