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arXiv 2023年

作者： Palutla, Anirudh Seth, Shivansh Ashwin, S.S. Krishnan, Marimuthu Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad India

Criticality, observed during second-order phase transitions, is an emergent phenomenon. The brain operates near criticality, where complex systems exhibit high correlations. The critical brain hypothesis suggests that the brain becomes an efficient learning system in this state but poor in memory, while sub-criticality enhances memory but inhibits learning. As a system approaches criticality, it develops"domain"-like regions with competing phases and increased spatiotemporal correlations that diverge. The dynamics of these domains depend on the system’s proximity to criticality. This study investigates the phase ordering properties of a spin-lattice model derived from Alzheimer’s and cognitively normal subjects, expecting significant differences in their proximity to criticality. However, our findings show no conclusive distinction in the distal properties from criticality, as reflected in the phase ordering behavior of the Alzheimer’s and cognitively normal brain. © 2023, CC BY-NC-SA.

关键词： Criticality (nuclear fission)

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Artificial Intelligence and IBD: Where are We Now and Where Will We Be in the Future?

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Current Gastroenterology Reports 2024年第5期26卷 137-144页

作者： Ahmed, Mehwish Stone, Molly L. Stidham, Ryan W. Division of Gastroenterology Department of Internal Medicine Michigan Medicine Ann Arbor MI United States Department of Computational Medicine and Bioinformatics University of Michigan 3912 Taubman Center 1500 East Medical Center Drive Ann Arbor 48109 MI United States Michigan Institute for Data Science (MIDAS) University of Michigan Ann Arbor MI United States

Purpose of Review: Artificial intelligence (AI) is quickly demonstrating the ability to address problems and challenges in the care of IBD. This review with commentary will highlight today’s advancements in AI applications for IBD in image analysis, understanding text, and replicating clinical knowledge and experience. Recent Findings: Advancements in machine learning methods, availability of high-performance computing, and increasing digitization of medical data are providing opportunities for AI to assist in IBD care. Multiple groups have demonstrated the ability of AI to replicate expert endoscopic scoring in IBD, with expansion into automated capsule endoscopy, enterography, and histologic interpretations. Further, AI image analysis is being used to develop new endoscopic scoring with more granularity and detail than is possible using conventional methods. Advancements in natural language processing are proving to reduce laborious tasks required in the care of IBD, including documentation, information searches, and chart review. Finally, large language models and chatbots that can understand language and generate human-like replies are beginning to exhibit clinical intelligence that will revolutionize how we deliver IBD care. Summary: Today, AI is being deployed to replicate expert judgement in specific tasks where disagreement, subjectivity, and bias are common. However, the near future will herald contributions of AI doing what we cannot, including new detailed measures of IBD, enhanced analysis of images, and perhaps even fully automating care. As we speculate on future technologic capabilities that may improve how we care for IBD, this review will also consider how we will implement and fairly use AI in practice. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Artificial intelligence Clinical trials Crohn’s disease Decision support Endoscopic scoring Inflammatory bowel disease Machine learning Prediction models Segmentation Ulcerative colitis

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Development of resources for training machine learning systems for the eye

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ACTA OPHTHALMOLOGICA 2025年第S284期103卷

作者： Lachke, Salil A. Department of Biological Sciences University of Delaware Newark DE USA Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA

Regulation of distinct molecular pathways in coordinately developing tissues drives eye development. This process involves factors in signaling, transcription, translation and post-transcriptional control that together determine the proteome of individual cells. Characterizing these regulatory events and their relationships with each other in distinct ocular tissues will lead to the derivation of gene regulatory networks (GRNs) for eye development, which in turn will allow building of the “developmental oculome” (Lachke, Maas (2010) Wiley Interdiscip Rev Syst Biol Med 2(3):305-323 doi: 10.1002/wsbm.59 ). Toward this goal, we generated and analyzed various omics datasets on developing eye tissues, performed functional characterization of key molecules, and developed a web resource called iSyTE (Anand, Lachke (2017) Exp Eye Res 156:22-33 PMC5026553 ; Kakrana et al (2018) Nucleic Acids Res 46(D1): D875-D885 doi: 10.1093/nar/gkx837 ; Lachke (2022) Exp Eye Res 214:108889 PMC8792301 ). To extract further insights from these omics data, it is necessary to analyze them in the context of experimentally verified data on the eye. These data can be used for training machine learning systems to develop models for eye development. The data used for training represents a an important factor in machine learning systems, i.e. the quality of these data is a key contributor that determines the robustness of the resulting models. There is a wealth of molecular functional (i.e. experimental evidence-based) data on the eye that has been generated by researchers worldwide over the past several decades. These individual regulatory data “nodes” (regulators and targets) – currently “static” in the literature with limited connectivity – hold rich potential to define detailed, and importantly, experimentally validated GRNs that can be used for training machine learning systems. As proof of principle, we have focused on ocular lens development and curated mechanistic data from individual gene p

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Implementation of Support Vector Machines using Reaction Networks

arXiv

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arXiv 2025年

作者： Choudhary, Amey Jin, Jiaxin Deshpande, Abhishek Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad India Department of Mathematics University of Louisiana United States

Can machine learning algorithms be implemented using chemical reaction networks? We demonstrate that this is possible in the case of support vector machines (SVMs). SVMs are powerful tools for data classification, leveraging VC theory to handle high-dimensional data and small datasets effectively. In this work, we propose a reaction network scheme for implementing SVMs, utilizing the steady-state behavior of reaction network dynamics to model key computational aspects of SVMs. This approach introduces a novel biochemical framework for implementing machine learning algorithms in non-traditional computational environments. © 2025, CC BY.

关键词： Adversarial machine learning

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Integrated approach to uncover regulatory networks in eye development and disease

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ACTA OPHTHALMOLOGICA 2025年第S284期103卷

作者： Lachke, Salil A. Department of Biological Sciences University of Delaware Newark DE USA Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA

It is challenging to identify/prioritize genes involved in development of the eye and its associated defects. The application of genome-level transcript profiling (transcriptomics) to examine eye tissues and cell types has facilitated ocular gene discovery. To maximally utilize the large transcriptome data, there is a need to perform meta-analysis of independently generated public data and to present it a user-friendly and freely accessible platform. Toward these goals, we developed a web-resource tool called iSyTE ( i ntegrated Sy stems T ool for E ye gene discovery; https://***/iSyTE/ ). iSyTE has effectively expedited eye gene discovery, leading to the identification of several genes ( Caprin2 , Celf1 , Elavl1 , Ell2 , Mafg , Mafk , Pvrl3 , Rbm24 , Sep15 , Tdrd7 , etc .) associated with lens defects and/or cataract and has contributed to the understanding of many other important lens regulatory pathways ( e.g. Foxe3 , Frfr2 , n-Myc , Prox1 , Sip1 (Zeb2) , CBP , p300 , etc .). To update iSyTE, we have now meta-analyzed transcriptome data from isolated lens epithelium and fiber cells as the lens develops from early embryonic stages through aging. Further, we have applied single nucleus (sn) multiomics approach using snRNA-seq and ATAC-seq to characterize early postnatal lens development. These new analysis have identified potential new genes and pathways in lens development and cataract.

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Assessing the Impact of Vulnerabilities on Confidentiality, Integrity, and Availability in Smart Systems 2

Assessing the Impact of Vulnerabilities on Confidentiality, ...

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2nd International Conference on Informatics, ICI 2023

作者： Minhass, Shubham Chauhan, Ritu Kaur, Harleen Amity University Amity Institute of Information Technology UP India Amity University IoT and Artificial Intelligence Lab Center for Computational Biology and Bioinformatics UP India Jamia Hamdard Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350343830

As Internet of Things (IoT) technologies proliferate and smart devices become more and more integrated into our daily lives and workplaces, it is becoming more and more important to address the complex issues of data security and privacy within smart homes and buildings. Even though the Internet of Things (IoT) has many benefits, such as remote control, real-time monitoring, and sophisticated security features, it also raises serious concerns about how sensitive data is protected. By examining the particular problems associated with protecting sensitive data in the IoT era and guaranteeing the continuous provision of dependable services within smart environments, this research paper seeks to close a significant knowledge gap. In order to do this, the research focuses on the complex data-centric features of smart ecosystems, including data processing, generation, and storage. This paper thoroughly examines the range of current IoT technologies, data management practices, and privacy challenges by using a qualitative research approach. It also offers a thorough examination of the privacy protections and security measures that are now in place in smart buildings and homes. This research focuses primarily on the interface between technology, security, and privacy of data. This work effectively fills in the significant research vacuum by clarifying the specific issues it seeks to solve. It provides useful advice and insights that can help individuals and organizations navigate the complex world of smart technologies, securely store their data, and realize the full potential of these game-changing advancements. © 2023 IEEE.

关键词： Sensitive data

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An Analytical Paradigm for Exploration of Diabetes Using Machine Learning 19

An Analytical Paradigm for Exploration of Diabetes Using Mac...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Chauhan, Ritu Mishra, Aarushi Mani, Ruchi Jakhmola Yafi, Eiad Zuhairi, Megat F. Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Proteomics and Translational Research Lab Center for Medical Biotechnology Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Machine Learning is an approach in AI which has swiftly emerged as a transformative and powerful tool in the healthcare industry, changing the methods of diagnosing and predicting diseases. In the present situation, the application of different machine learning methods, including deep learning, ensemble techniques, and natural language processing, across various healthcare activities, such as disease prediction, medical imaging analysis, drug discovery, and tailored treatment suggestions, has offered encouraging prospects for improved diagnostics, treatment, and patient results. By utilizing a diverse range of data sources, including electronic health records, medical imaging, and wearable devices, machine learning algorithms have facilitated early detection, precise prognostication, and customized interventions. The application of machine learning algorithms into health databases is the ongoing area of research concerning future modelling predictions. The current study revolves around the utilization of descriptive analytic methods and machine learning algorithms to predict if an individual is diabetic. The effectiveness of four distinct models of machine learning, specifically Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM) and K Nearest Neighbor (KNN), is assessed and contrasted. The respective accuracies of these classifiers are noted as 89.83%, 77.16%, 84.84%, 81.16%. © 2025 IEEE.

关键词： Electronic health record

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Single-cell technologies: current and near future

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Science China(Life Sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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Artificial Intelligence as Automated Technology for Prediction of Breast Cancer 3rd

Artificial Intelligence as Automated Technology for Predicti...

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3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Choudhary, Tisya Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Analysis of feedback schemes in proliferating stem cells 63

Analysis of feedback schemes in proliferating stem cells

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Borri, A. Palumbo, P. Singh, A. Via dei Taurini 19 Roma Italy University of Milano-Bicocca Department of Biotechnologies and Biosciences Milan20126 Italy University of Delaware Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology NewarkDE19716 United States

ISBN: (纸本)9798350316339

Stem cells have emerged as a pivotal player in biomedical research, offering unprecedented opportunities for regenerative medicine, disease modeling, and drug discovery. These undifferentiated cells possess the unique ability to self-renew and differentiate into various specialized cell types, making them invaluable in tissue repair and regeneration. This note analyzes a stochastic model aiming at unraveling the control machinery providing the proper ratio of stem-versus-differentiated cells, in agreement with a couple of feedback schemes regulating cell proliferation. What emerges from the closed-loop schemes analysis is that in order to have an asymptotically stable, nontrivial accumulation of differentiated cells a negative feedback on cell proliferation is required. Numerical simulations in the stochastic setting validate the approximate theoretical results achieved to relate how random fluctuations propagate with respect to variations of model parameters. © 2024 IEEE.

关键词： Stochastic systems

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