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A framework for the analysis and synthesis of Swarm Intelligence algorithms

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2021年第4期33卷 659-681页

作者： Cruz, Davila Patricia Ferreira Maia, Renato Dourado de Castro, Leandro Nunes Univ Prebiteriana Mackenzie Nat Comp & Machine Learning Lab Lcon Grad Program Elect Engn & Comp Consolacao St 930 BR-01302000 Sao Paulo SP Brazil Univ Estadual Montes Claros Comp Sci Dept Montes Claros MG Brazil

Over the last decades, the number of Swarm Intelligence algorithms proposed in the literature has increased considerably. However, most algorithms do not follow an adequate scientific rigour neither the principles of Swarm Intelligence, reproducing similar computational procedures of many other approaches, but by means of a different metaphor. In this scenario, the aim of this paper is to propose a framework for the analysis and synthesis of Swarm Intelligence algorithms that can contribute to a structured understanding (analysis) and development (synthesis) of these algorithms. The main objective of the proposed framework is to guide the construction of Swarm Intelligence metaphors in a consistent and well-founded way, as well as to contribute with the analysis and development of new algorithms that had better encompass the features and abilities of insects' societies.

关键词： Swarm Intelligence bio-inspired algorithms natural computing information processing decision-making social insects

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CellRank 2: unified fate mapping in multiview single-cell data

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NATURE METHODS 2024年第7期21卷 1196-1205页

作者： Weiler, Philipp Lange, Marius Klein, Michal Pe'er, Dana Theis, Fabian Helmholtz Munich Inst Computat Biol Dept Computat Hlth Munich Germany Tech Univ Munich Sch Computat Informat & Technol Munich Germany Swiss Fed Inst Technol Dept Biosyst Sci & Engn Basel Switzerland Apple Machine Learning Res Paris France Mem Sloan Kettering Canc Ctr Sloan Kettering Inst Program Computat & Syst Biol New York NY USA Howard Hughes Med Inst Chevy Chase MD USA Tech Univ Munich TUM Sch Life Sci Weihenstephan Munich Germany

Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories;however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies. CellRank 2 is a comprehensive framework of cell fate analysis using large-scale multiview single-cell data.

关键词： Computational models Software Transcriptomics

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Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen

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MABS 2024年第1期16卷 2362775页

作者： Hutchinson, Mark Ruffolo, Jeffrey A. Haskins, Nantaporn Iannotti, Michael Vozza, Giuliana Pham, Tony Mehzabeen, Nurjahan Shandilya, Harini Rickert, Keith Croasdale-Wood, Rebecca Damschroder, Melissa Fu, Ying Dippel, Andrew Gray, Jeffrey J. Kaplan, Gilad AstraZeneca Biol Engn R&D Gaithersburg MD 20852 USA Johns Hopkins Univ Program Mol Biophys Baltimore MD 21218 USA Profluent Bio Machine Learning Berkeley CA USA Amgen Inc Prot Engn R&D Rockville MD USA Honigman LLP Intellectual Property Washington DC USA AstraZeneca Biopharmaceut Dev R&D Cambridge England AstraZeneca Biol Engn R&D Cambridge England Johns Hopkins Univ Dept Chem & Biomol Engn Baltimore MD 21218 USA

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (T-onset, T-m, T-agg), affinity (K-D) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in T-m1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal struc

关键词： Antibodies computational design developability machine learning thermostability

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Population burst propagation across interacting areas of the brain

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JOURNAL OF NEUROPHYSIOLOGY 2022年第6期128卷 1578-1592页

作者： Chen, Yu Douglas, Hannah Medina, Bryan J. Olarinre, Motolani Siegle, Joshua H. Kass, Robert E. Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Neurosci Inst Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Stat & Data Sci Pittsburgh PA 15213 USA Allen Inst MindScope Program Seattle WA USA Univ Cent Florida Dept Comp Sci Orlando FL USA

For many perceptual and behavioral tasks, a prominent feature of neural spike trains involves high firing rates across relatively short intervals of time. We call these events "population bursts." Because during a population burst information is, presumably, transmitted from one part of the brain to another, burst timing should reveal activity related to the flow of information across neural circuits. We developed a statistical method (based on a point process model) of determining, accurately, the time of the maximum (peak) population firing rate on a trial-by-trial basis and used it to characterize burst propagation across areas. We then examined the tendency of peak firing rates in distinct brain areas to shift earlier or later in time, together, across repeated trials, and found this trial-to-trial coupling of peak times to be a sensitive indicator of interaction across populations. In the data we examined, from the Allen Brain Observatory, we found many very strong correlations (95% confidence intervals above 0.75) in cases where standard methods were unable to demonstrate cross-area correlation. The statistical model introduced cross-area covariation only through population-level trial-dependent time shifts and gain constants (values of which were learned from the data), yet it provided very good fits to data histograms, including histograms of spike count correlations within and across visual areas. Our results demonstrate the utility of carefully assessing timing and propagation, across brain regions, of transient bursts in neural population activity, based on multiple spike train *** & NOTEWORTHY We developed a novel statistical method for identifying coordinated propagation of activity across popu-lations of spiking neurons, with high temporal accuracy. Using simultaneous recordings from three visual areas we document precise timing relationships on a trial-by-trial basis, and we show how previously existing techniques can fail to discover coord

关键词： cross-region communication multiple spike trains neuropixels point process trial-to-trial variation

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Protein codes promote selective subcellular compartmentalization

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Science (New York, N.Y.) 2025年第6738期387卷 1095-1101页

作者： Kilgore, Henry R. Chinn, Itamar Mikhael, Peter G. Mitnikov, Ilan Van Dongen, Catherine Zylberberg, Guy Afeyan, Lena Banani, Salman F. Wilson-Hawken, Susana Lee, Tong Ihn Barzilay, Regina Young, Richard A. Whitehead Institute for Biomedical Research Cambridge MA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology (MIT) Cambridge MA United States Abdul Latif Jameel Clinic for Machine Learning in Health MIT Cambridge MA United States Department of Biology MIT Cambridge MA United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Computational and Systems Biology Program MIT Cambridge MA United States

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. In this study, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. We developed a protein language model, ProtGPS, that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

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Systematic identification and characterization of compounds with reliable activity against multiple target proteins from different classes

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RESULTS IN CHEMISTRY 2024年 7卷

作者： Srinivasan, Sanjana Bajorath, Juergen Rhein Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Rhein Friedrich Wilhelms Univ Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Rhein Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

Polypharmacology provides a basis for the treatment of diseases originating from perturbed target and pathway networks. It depends on the use of compounds with suitable multi-target activity and pharmacological effects resulting from multi-target engagement. In addition to their central role for polypharmacology, multi-target compounds are also of interest for basic research, for example, to better understand their binding characteristics or interaction patterns. While it is not unlikely that active compounds might bind to closely related targets, we have been interested in identifying compounds that are active against distantly or unrelated proteins, representing more unexpected instances of multi-target activity. Therefore, we have carried out a systematic search for compounds with activity against proteins from different classes, termed multi-class compounds (MC-CPDs). To this end, currently available compound activity data from medicinal chemistry were carefully curated. Subsequent analysis of similar to 120,000 qualifying compounds with high-confidence target annotations identified 689 MC-CPDs with activity against at least three targets from two different protein classes. The target distribution of these compounds was determined and structural relationships were explored together with activity profiles. MCCPDs covered many target combinations and established a wealth of compound-based target relationships, some of which could be rationalized by considering ligand binding characteristics of distinct targets. As a part of our study, the collection of newly identified MC-CPDs and associated target information is made publicly available.

关键词： Multi-target compounds Polypharmacology Structural relationships Protein classes Unrelated targets Compound-based target relationships

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Chemical and biological language models in molecular design: opportunities, risks and scientific reasoning

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FUTURE SCIENCE OA 2024年第1期10卷 FSO957页

作者： Bajorath, Juergen Rhein Friedrich Wilhelms Univ Lamarr Inst Machine Learning & Artificial Intellig Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med ChemB IT Friedrich Hirzebruch Allee 5-6 Bonn D-53115 Germany

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A comprehensive survey of golden jacal optimization and its applications

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COMPUTER SCIENCE REVIEW 2025年 56卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Rahmani, Amir Masoud Alanazi, Abed Zaidi, Monji Mohamed Aurangzeb, Khursheed Alinejad-Rokny, Hamid Porntaveetus, Thantrira Lee, Sang-Woong Duy Tan Univ Sch Comp Sci Da Nang Vietnam Jadara Univ Jadara Univ Res Ctr Irbid Jordan Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Yunlin Taiwan Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci POB 151 Al Kharj 11942 Saudi Arabia King Khalid Univ Coll Engn Dept Elect Engn Abha 61421 Saudi Arabia King Khalid Univ Ctr Engn & Technol Innovat Abha 61421 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Comp Engn POB 51178 Riyadh 11543 Saudi Arabia UNSW Sydney Grad Sch Biomed Engn UNSW BioMed Machine Learning Lab BML Sydney NSW 2052 Australia Chulalongkorn Univ Ctr Excellence Genom & Precis Dent Geriatr Dent & Special Patients Care Int Program Clin Res CtrDept PhysiolFac Dent Bangkok Thailand Gachon Univ Pattern Recognit & Machine Learning Lab Seongnam 13120 South Korea

In recent decades, there has been an increasing interest from the research community in various scientific and engineering fields, including robotic control, signal processing, image processing, feature selection, classification, clustering, and other issues. Many optimization problems are inherently complicated and complex. They cannot be solved by traditional optimization methods, such as mathematical programming, because most conventional optimization methods focus on evaluating first derivatives. On the other hand, metaheuristic algorithms have high ability and adaptability in finding near-optimal solutions in a reasonable time for different optimization problems due to parallel search and balance between exploration and exploitation. This study discusses the basic principles and mechanisms of the GJO algorithm and its challenges. This review aims to provide valuable insights into the potential of the GJO algorithm for real-world and scientific optimization tasks. In this paper, a complete review of the Golden Jackal Optimization (GJO) algorithm for various optimization problems is done. The GJO algorithm is one of the metaheuristic algorithms invented in 2022 and inspired by the life of natural jackals. This paper's complete classification of GJO in hybrid, improved, binary, multi-objective, and optimization problems is done. The analysis shows that the percentage of studies conducted in the four fields of hybrid, improved variants of GJO (binary, multi-objective), and optimization are 11 %, 44 %, 9 %, and 36 %, respectively. Studies have shown that this algorithm performs well in real-world challenges. GJO is a powerful tool for solving scientific and engineering problems flexibly.

关键词： Optimization problems Metaheuristic algorithms Golden Jackal Optimization Improved

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Rethinking Wireless Communication Security in Semantic Internet of Things

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IEEE WIRELESS COMMUNICATIONS 2023年第3期30卷 36-43页

作者： Du, Hongyang Wang, Jiacheng Niyato, Dusit Kang, Jiawen Xiong, Zehui Guizani, Mohsen Kim, Dong In Nanyang Technol Univ Energy Res Inst NTU Sch Comp Sci & Engn Interdisciplinary Grad Program Singapore Singapore Nanyang Technol Univ Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Guangdong Univ Technol Guangzhou Peoples R China Singapore Univ Technol & Design Singapore Singapore Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning & Associate Provost Abu Dhabi U Arab Emirates Sungkyunkwan Univ Coll Informat & Commun Engn Seoul South Korea

Semantic communication is an important component in the next generation of wireless networking. Enabled by this novel paradigm, the conventional Internet-of-Things (IoT) is evolving toward the semantic IoT (SIoT) to achieve significant system performance improvements. However, traditional wireless communication security techniques for bit transmission cannot be applied directly to the SIoT that focuses on semantic information transmission. One key reason is the lack of new security performance indicators. Thus, we have to rethink the wireless communication security in the SIoT. As such, in this article, we analyze and compare classical security techniques, such as physical layer security, covert communications, and encryption, from the perspective of semantic information security. We highlight the differences among these security techniques when applied to the SIoT. Novel performance indicators, such as semantic secrecy outage probability (for physical layer security techniques) and detection failure probability (for covert communication techniques) are proposed. Considering that semantic communications can raise new security issues, we then review attack and defense methods at the semantic level. Lastly, we present several promising directions for future secure SIoT research.

关键词： Wireless communication System performance Semantics Physical layer security Probability Social Internet of Things Encryption

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Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs

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BIOINFORMATICS 2024年第Sup2期40卷 ii111-ii119页

作者： Song, Tianci Cosatto, Eric Wang, Gaoyuan Kuang, Rui Gerstein, Mark Min, Martin Renqiang Warrell, Jonathan Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA NEC Labs Amer Machine Learning Dept Princeton NJ 08540 USA Yale Univ Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA Yale Univ Dept Comp Sci New Haven CT 06520 USA Yale Univ Dept Stat & Data Sci New Haven CT 06520 USA Yale Univ Dept Biomed Informat & Data Sci New Haven CT 06520 USA

Motivation Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue *** Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological *** and implementation https://***/song0309/asGNN/

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