Pub Date : 2024-10-21DOI: 10.1038/s42256-024-00921-8
Distinguishing between real and fabricated facts has long been a societal challenge. As the Internet becomes increasingly littered with AI-generated content, the need for curation and safeguarding of high-quality data and information is more crucial than ever.
{"title":"Pick your AI poison","authors":"","doi":"10.1038/s42256-024-00921-8","DOIUrl":"10.1038/s42256-024-00921-8","url":null,"abstract":"Distinguishing between real and fabricated facts has long been a societal challenge. As the Internet becomes increasingly littered with AI-generated content, the need for curation and safeguarding of high-quality data and information is more crucial than ever.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 10","pages":"1119-1119"},"PeriodicalIF":18.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00921-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Optimizing a candidate molecule’s physiochemical and functional properties has been a critical task in drug and material design. Although the non-trivial task of balancing multiple (potentially conflicting) optimization objectives is considered ideal for artificial intelligence, several technical challenges such as the scarcity of multiproperty-labelled training data have hindered the development of a satisfactory AI solution for a long time. Prompt-MolOpt is a tool for molecular optimization; it makes use of prompt-based embeddings, as used in large language models, to improve the transformer’s ability to optimize molecules for specific property adjustments. Notably, Prompt-MolOpt excels in working with limited multiproperty data (even under the zero-shot setting) by effectively generalizing causal relationships learned from single-property datasets. In comparative evaluations against established models such as JTNN, hierG2G and Modof, Prompt-MolOpt achieves over a 15% relative improvement in multiproperty optimization success rates compared with the leading Modof model. Furthermore, a variant of Prompt-MolOpt, named Prompt-MolOptP, can preserve the pharmacophores or any user-specified fragments under the structural transformation, further broadening its application scope. By constructing tailored optimization datasets, with the protocol introduced in this work, Prompt-MolOpt steers molecular optimization towards domain-relevant chemical spaces, enhancing the quality of the optimized molecules. Real-world tests, such as those involving blood–brain barrier permeability optimization, underscore its practical relevance. Prompt-MolOpt offers a versatile approach for multiproperty and multi-site molecular optimizations, suggesting its potential utility in chemistry research and drug and material discovery. Designing molecules in drug design is challenging as it requires optimizing multiple, potentially competing qualities. Wu and colleagues present a prompt-based molecule optimization method that can be trained from single-property data.
{"title":"Leveraging language model for advanced multiproperty molecular optimization via prompt engineering","authors":"Zhenxing Wu, Odin Zhang, Xiaorui Wang, Li Fu, Huifeng Zhao, Jike Wang, Hongyan Du, Dejun Jiang, Yafeng Deng, Dongsheng Cao, Chang-Yu Hsieh, Tingjun Hou","doi":"10.1038/s42256-024-00916-5","DOIUrl":"10.1038/s42256-024-00916-5","url":null,"abstract":"Optimizing a candidate molecule’s physiochemical and functional properties has been a critical task in drug and material design. Although the non-trivial task of balancing multiple (potentially conflicting) optimization objectives is considered ideal for artificial intelligence, several technical challenges such as the scarcity of multiproperty-labelled training data have hindered the development of a satisfactory AI solution for a long time. Prompt-MolOpt is a tool for molecular optimization; it makes use of prompt-based embeddings, as used in large language models, to improve the transformer’s ability to optimize molecules for specific property adjustments. Notably, Prompt-MolOpt excels in working with limited multiproperty data (even under the zero-shot setting) by effectively generalizing causal relationships learned from single-property datasets. In comparative evaluations against established models such as JTNN, hierG2G and Modof, Prompt-MolOpt achieves over a 15% relative improvement in multiproperty optimization success rates compared with the leading Modof model. Furthermore, a variant of Prompt-MolOpt, named Prompt-MolOptP, can preserve the pharmacophores or any user-specified fragments under the structural transformation, further broadening its application scope. By constructing tailored optimization datasets, with the protocol introduced in this work, Prompt-MolOpt steers molecular optimization towards domain-relevant chemical spaces, enhancing the quality of the optimized molecules. Real-world tests, such as those involving blood–brain barrier permeability optimization, underscore its practical relevance. Prompt-MolOpt offers a versatile approach for multiproperty and multi-site molecular optimizations, suggesting its potential utility in chemistry research and drug and material discovery. Designing molecules in drug design is challenging as it requires optimizing multiple, potentially competing qualities. Wu and colleagues present a prompt-based molecule optimization method that can be trained from single-property data.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 11","pages":"1359-1369"},"PeriodicalIF":18.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deciphering the relationships between genes and complex traits can enhance our understanding of phenotypic variations and disease mechanisms. However, determining the specific roles of individual genes and quantifying their direct and indirect causal effects on complex traits remains a significant challenge. Here we present a framework (called Bayesian network genome-wide association studies (BN-GWAS)) to decipher the total and direct causal effects of individual genes. BN-GWAS leverages imputed expression profiles from GWAS and raw expression data from a reference dataset to construct a directed gene–gene–phenotype causal network. It allows gene expression and disease traits to be evaluated in different samples, significantly improving the flexibility and applicability of the approach. It can be extended to decipher the joint causal network of two or more traits, and exhibits high specificity and precision (positive predictive value), making it particularly useful for selecting genes for follow-up studies. We verified the feasibility and validity of BN-GWAS by extensive simulations and applications to 52 traits across 14 tissues in the UK Biobank, revealing insights into their genetic architectures, including the relative contributions of direct, indirect and mediating causal genes. The identified (direct) causal genes were significantly enriched for genes highlighted in the Open Targets database. Overall, BN-GWAS provides a flexible and powerful framework for elucidating the genetic basis of complex traits through a systems-level, causal inference approach. Genome-wide association studies generate extensive data, but interpreting these data remains challenging. A Bayesian-network-based method is presented that uses imputed and raw gene expression data to decipher the causal effects of individual genes.
{"title":"Estimation of causal effects of genes on complex traits using a Bayesian-network-based framework applied to GWAS data","authors":"Liangying Yin, Yaning Feng, Yujia Shi, Alexandria Lau, Jinghong Qiu, Pak-Chung Sham, Hon-Cheong So","doi":"10.1038/s42256-024-00906-7","DOIUrl":"10.1038/s42256-024-00906-7","url":null,"abstract":"Deciphering the relationships between genes and complex traits can enhance our understanding of phenotypic variations and disease mechanisms. However, determining the specific roles of individual genes and quantifying their direct and indirect causal effects on complex traits remains a significant challenge. Here we present a framework (called Bayesian network genome-wide association studies (BN-GWAS)) to decipher the total and direct causal effects of individual genes. BN-GWAS leverages imputed expression profiles from GWAS and raw expression data from a reference dataset to construct a directed gene–gene–phenotype causal network. It allows gene expression and disease traits to be evaluated in different samples, significantly improving the flexibility and applicability of the approach. It can be extended to decipher the joint causal network of two or more traits, and exhibits high specificity and precision (positive predictive value), making it particularly useful for selecting genes for follow-up studies. We verified the feasibility and validity of BN-GWAS by extensive simulations and applications to 52 traits across 14 tissues in the UK Biobank, revealing insights into their genetic architectures, including the relative contributions of direct, indirect and mediating causal genes. The identified (direct) causal genes were significantly enriched for genes highlighted in the Open Targets database. Overall, BN-GWAS provides a flexible and powerful framework for elucidating the genetic basis of complex traits through a systems-level, causal inference approach. Genome-wide association studies generate extensive data, but interpreting these data remains challenging. A Bayesian-network-based method is presented that uses imputed and raw gene expression data to decipher the causal effects of individual genes.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 10","pages":"1231-1244"},"PeriodicalIF":18.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1038/s42256-024-00910-x
Enrui Zhang, Adar Kahana, Alena Kopaničáková, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis
Neural networks suffer from spectral bias and have difficulty representing the high-frequency components of a function, whereas relaxation methods can resolve high frequencies efficiently but stall at moderate to low frequencies. We exploit the weaknesses of the two approaches by combining them synergistically to develop a fast numerical solver of partial differential equations (PDEs) at scale. Specifically, we propose HINTS, a hybrid, iterative, numerical and transferable solver by integrating a Deep Operator Network (DeepONet) with standard relaxation methods, leading to parallel efficiency and algorithmic scalability for a wide class of PDEs, not tractable with existing monolithic solvers. HINTS balances the convergence behaviour across the spectrum of eigenmodes by utilizing the spectral bias of DeepONet, resulting in a uniform convergence rate and hence exceptional performance of the hybrid solver overall. Moreover, HINTS applies to large-scale, multidimensional systems; it is flexible with regards to discretizations, computational domain and boundary conditions; and it can also be used to precondition Krylov methods. Neural-network-based solvers for partial differential equations (PDEs) suffer from difficulties tackling high-frequency modes when learning complex functions, whereas for classical solvers it is more difficult to handle low-frequency modes. Zhang and colleagues propose a hybrid numerical PDE solver by combining a Deep Operator Network with traditional relaxation methods, leading to balanced convergence across the eigenmode spectrum for a wide range of PDEs.
{"title":"Blending neural operators and relaxation methods in PDE numerical solvers","authors":"Enrui Zhang, Adar Kahana, Alena Kopaničáková, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis","doi":"10.1038/s42256-024-00910-x","DOIUrl":"10.1038/s42256-024-00910-x","url":null,"abstract":"Neural networks suffer from spectral bias and have difficulty representing the high-frequency components of a function, whereas relaxation methods can resolve high frequencies efficiently but stall at moderate to low frequencies. We exploit the weaknesses of the two approaches by combining them synergistically to develop a fast numerical solver of partial differential equations (PDEs) at scale. Specifically, we propose HINTS, a hybrid, iterative, numerical and transferable solver by integrating a Deep Operator Network (DeepONet) with standard relaxation methods, leading to parallel efficiency and algorithmic scalability for a wide class of PDEs, not tractable with existing monolithic solvers. HINTS balances the convergence behaviour across the spectrum of eigenmodes by utilizing the spectral bias of DeepONet, resulting in a uniform convergence rate and hence exceptional performance of the hybrid solver overall. Moreover, HINTS applies to large-scale, multidimensional systems; it is flexible with regards to discretizations, computational domain and boundary conditions; and it can also be used to precondition Krylov methods. Neural-network-based solvers for partial differential equations (PDEs) suffer from difficulties tackling high-frequency modes when learning complex functions, whereas for classical solvers it is more difficult to handle low-frequency modes. Zhang and colleagues propose a hybrid numerical PDE solver by combining a Deep Operator Network with traditional relaxation methods, leading to balanced convergence across the eigenmode spectrum for a wide range of PDEs.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 11","pages":"1303-1313"},"PeriodicalIF":18.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-07DOI: 10.1038/s42256-024-00908-5
Bohao Zou, Jingjing Wang, Yi Ding, Zhenmiao Zhang, Yufen Huang, Xiaodong Fang, Ka Chun Cheung, Simon See, Lu Zhang
Metagenome-assembled genomes (MAGs) offer valuable insights into the exploration of microbial dark matter using metagenomic sequencing data. However, there is growing concern that contamination in MAGs may substantially affect the results of downstream analysis. Current MAG decontamination tools primarily rely on marker genes and do not fully use the contextual information of genomic sequences. To overcome this limitation, we introduce Deepurify for MAG decontamination. Deepurify uses a multi-modal deep language model with contrastive learning to match microbial genomic sequences with their taxonomic lineages. It allocates contigs within a MAG to a MAG-separated tree and applies a tree traversal algorithm to partition MAGs into sub-MAGs, with the goal of maximizing the number of high- and medium-quality sub-MAGs. Here we show that Deepurify outperformed MDMclearer and MAGpurify on simulated data, CAMI datasets and real-world datasets with varying complexities. Deepurify increased the number of high-quality MAGs by 20.0% in soil, 45.1% in ocean, 45.5% in plants, 33.8% in freshwater and 28.5% in human faecal metagenomic sequencing datasets. Metagenome-assembled genomes (MAGs) provide insights into microbial dark matter, but contamination remains a concern for downstream analysis. Zou et al. develop a multi-modal deep language model that leverages microbial sequences to remove ‘unexpected’ contigs from MAGs. This approach is compatible with any contig binning tools and increases the number of high-quality bins.
{"title":"A multi-modal deep language model for contaminant removal from metagenome-assembled genomes","authors":"Bohao Zou, Jingjing Wang, Yi Ding, Zhenmiao Zhang, Yufen Huang, Xiaodong Fang, Ka Chun Cheung, Simon See, Lu Zhang","doi":"10.1038/s42256-024-00908-5","DOIUrl":"10.1038/s42256-024-00908-5","url":null,"abstract":"Metagenome-assembled genomes (MAGs) offer valuable insights into the exploration of microbial dark matter using metagenomic sequencing data. However, there is growing concern that contamination in MAGs may substantially affect the results of downstream analysis. Current MAG decontamination tools primarily rely on marker genes and do not fully use the contextual information of genomic sequences. To overcome this limitation, we introduce Deepurify for MAG decontamination. Deepurify uses a multi-modal deep language model with contrastive learning to match microbial genomic sequences with their taxonomic lineages. It allocates contigs within a MAG to a MAG-separated tree and applies a tree traversal algorithm to partition MAGs into sub-MAGs, with the goal of maximizing the number of high- and medium-quality sub-MAGs. Here we show that Deepurify outperformed MDMclearer and MAGpurify on simulated data, CAMI datasets and real-world datasets with varying complexities. Deepurify increased the number of high-quality MAGs by 20.0% in soil, 45.1% in ocean, 45.5% in plants, 33.8% in freshwater and 28.5% in human faecal metagenomic sequencing datasets. Metagenome-assembled genomes (MAGs) provide insights into microbial dark matter, but contamination remains a concern for downstream analysis. Zou et al. develop a multi-modal deep language model that leverages microbial sequences to remove ‘unexpected’ contigs from MAGs. This approach is compatible with any contig binning tools and increases the number of high-quality bins.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 10","pages":"1245-1255"},"PeriodicalIF":18.8,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1038/s42256-024-00911-w
Cas Wognum, Jeremy R. Ash, Matteo Aldeghi, Raquel Rodríguez-Pérez, Cheng Fang, Alan C. Cheng, Daniel J. Price, Djork-Arné Clevert, Ola Engkvist, W. Patrick Walters
{"title":"A call for an industry-led initiative to critically assess machine learning for real-world drug discovery","authors":"Cas Wognum, Jeremy R. Ash, Matteo Aldeghi, Raquel Rodríguez-Pérez, Cheng Fang, Alan C. Cheng, Daniel J. Price, Djork-Arné Clevert, Ola Engkvist, W. Patrick Walters","doi":"10.1038/s42256-024-00911-w","DOIUrl":"10.1038/s42256-024-00911-w","url":null,"abstract":"","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 10","pages":"1120-1121"},"PeriodicalIF":18.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1038/s42256-024-00902-x
Guruprasad Raghavan, Bahey Tharwat, Surya Narayanan Hari, Dhruvil Satani, Rex Liu, Matt Thomson
Contemporary machine learning algorithms train artificial neural networks by setting network weights to a single optimized configuration through gradient descent on task-specific training data. The resulting networks can achieve human-level performance on natural language processing, image analysis and agent-based tasks, but lack the flexibility and robustness characteristic of human intelligence. Here we introduce a differential geometry framework—functionally invariant paths—that provides flexible and continuous adaptation of trained neural networks so that secondary tasks can be achieved beyond the main machine learning goal, including increased network sparsification and adversarial robustness. We formulate the weight space of a neural network as a curved Riemannian manifold equipped with a metric tensor whose spectrum defines low-rank subspaces in weight space that accommodate network adaptation without loss of prior knowledge. We formalize adaptation as movement along a geodesic path in weight space while searching for networks that accommodate secondary objectives. With modest computational resources, the functionally invariant path algorithm achieves performance comparable with or exceeding state-of-the-art methods including low-rank adaptation on continual learning, sparsification and adversarial robustness tasks for large language models (bidirectional encoder representations from transformers), vision transformers (ViT and DeIT) and convolutional neural networks. Machine learning often includes secondary objectives, such as sparsity or robustness. To reach these objectives efficiently, the training of a neural network has been interpreted as the exploration of functionally invariant paths in the parameter space.
{"title":"Engineering flexible machine learning systems by traversing functionally invariant paths","authors":"Guruprasad Raghavan, Bahey Tharwat, Surya Narayanan Hari, Dhruvil Satani, Rex Liu, Matt Thomson","doi":"10.1038/s42256-024-00902-x","DOIUrl":"10.1038/s42256-024-00902-x","url":null,"abstract":"Contemporary machine learning algorithms train artificial neural networks by setting network weights to a single optimized configuration through gradient descent on task-specific training data. The resulting networks can achieve human-level performance on natural language processing, image analysis and agent-based tasks, but lack the flexibility and robustness characteristic of human intelligence. Here we introduce a differential geometry framework—functionally invariant paths—that provides flexible and continuous adaptation of trained neural networks so that secondary tasks can be achieved beyond the main machine learning goal, including increased network sparsification and adversarial robustness. We formulate the weight space of a neural network as a curved Riemannian manifold equipped with a metric tensor whose spectrum defines low-rank subspaces in weight space that accommodate network adaptation without loss of prior knowledge. We formalize adaptation as movement along a geodesic path in weight space while searching for networks that accommodate secondary objectives. With modest computational resources, the functionally invariant path algorithm achieves performance comparable with or exceeding state-of-the-art methods including low-rank adaptation on continual learning, sparsification and adversarial robustness tasks for large language models (bidirectional encoder representations from transformers), vision transformers (ViT and DeIT) and convolutional neural networks. Machine learning often includes secondary objectives, such as sparsity or robustness. To reach these objectives efficiently, the training of a neural network has been interpreted as the exploration of functionally invariant paths in the parameter space.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 10","pages":"1179-1196"},"PeriodicalIF":18.8,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00902-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142369346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1038/s42256-024-00894-8
Enrica Tricomi, Francesco Missiroli, Michele Xiloyannis, Nicola Lotti, Xiaohui Zhang, Marios Stefanakis, Maximilian Theisen, Jürgen Bauer, Clemens Becker, Lorenzo Masia
Peoples'' walking efficiency declines as they grow older, posing constraints on mobility, and affecting independence and quality of life. Although wearable assistive technologies are recognized as a potential solution for age-related movement challenges, few have proven effective for older adults, predominantly within controlled laboratory experiments. Here we present WalkON, a pair of soft robotic shorts designed to enhance walking efficiency for older individuals by assisting hip flexion. The system features a compact and lightweight tendon-driven design, using a controller based on natural leg movements to autonomously assist leg propagation. To assess WalkON''s impact on daily walking, we initially conducted a technology assessment with young adults on a demanding outdoor uphill 500 m hiking trail. We then validated our findings with a group of older adults walking on a flat outdoor 400 m track. WalkON considerably reduced the metabolic cost of transport by 17.79% for young adults during uphill walking. At the same time, participants reported high perceived control over their voluntary movements (a self-reported mean score of 6.20 out of 7 on a Likert scale). Similarly, older adults reduced their metabolic cost by 10.48% when using WalkON during level ground walking, while retaining a strong sense of movement control (mean score of 6.09 out of 7). These findings emphasize the potential of wearable assistive devices to improve efficiency in outdoor walking, suggesting promising implications for promoting physical well-being and advancing mobility, particularly during the later stages of life. Walking efficiency declines in older adults. To address this challenge, Tricomi and colleagues present a pair of lightweight, soft robotic shorts that enhance walking efficiency for older adults by assisting leg mobility. This method improves energy efficiency on outdoor tracks while maintaining the users’ natural movement control.
{"title":"Soft robotic shorts improve outdoor walking efficiency in older adults","authors":"Enrica Tricomi, Francesco Missiroli, Michele Xiloyannis, Nicola Lotti, Xiaohui Zhang, Marios Stefanakis, Maximilian Theisen, Jürgen Bauer, Clemens Becker, Lorenzo Masia","doi":"10.1038/s42256-024-00894-8","DOIUrl":"10.1038/s42256-024-00894-8","url":null,"abstract":"Peoples'' walking efficiency declines as they grow older, posing constraints on mobility, and affecting independence and quality of life. Although wearable assistive technologies are recognized as a potential solution for age-related movement challenges, few have proven effective for older adults, predominantly within controlled laboratory experiments. Here we present WalkON, a pair of soft robotic shorts designed to enhance walking efficiency for older individuals by assisting hip flexion. The system features a compact and lightweight tendon-driven design, using a controller based on natural leg movements to autonomously assist leg propagation. To assess WalkON''s impact on daily walking, we initially conducted a technology assessment with young adults on a demanding outdoor uphill 500 m hiking trail. We then validated our findings with a group of older adults walking on a flat outdoor 400 m track. WalkON considerably reduced the metabolic cost of transport by 17.79% for young adults during uphill walking. At the same time, participants reported high perceived control over their voluntary movements (a self-reported mean score of 6.20 out of 7 on a Likert scale). Similarly, older adults reduced their metabolic cost by 10.48% when using WalkON during level ground walking, while retaining a strong sense of movement control (mean score of 6.09 out of 7). These findings emphasize the potential of wearable assistive devices to improve efficiency in outdoor walking, suggesting promising implications for promoting physical well-being and advancing mobility, particularly during the later stages of life. Walking efficiency declines in older adults. To address this challenge, Tricomi and colleagues present a pair of lightweight, soft robotic shorts that enhance walking efficiency for older adults by assisting leg mobility. This method improves energy efficiency on outdoor tracks while maintaining the users’ natural movement control.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 10","pages":"1145-1155"},"PeriodicalIF":18.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00894-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142360330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1038/s42256-024-00901-y
Ziyan Feng, Jingyang Chen, Youlong Hai, Xuelian Pang, Kun Zheng, Chenglong Xie, Xiujuan Zhang, Shengqing Li, Chengjuan Zhang, Kangdong Liu, Lili Zhu, Xiaoyong Hu, Shiliang Li, Jie Zhang, Kai Zhang, Honglin Li
Neoantigens are promising targets for immunotherapy by eliciting immune response and removing cancer cells with high specificity, low toxicity and ease of personalization. However, identifying effective neoantigens remains difficult because of the complex interactions among T cell receptors, antigens and human leucocyte antigen sequences. In this study, we integrate important physical and biological priors with the Transformer model and propose the physics-inspired sliding transformer (PISTE). In PISTE, the conventional, data-driven attention mechanism is replaced with physics-driven dynamics that steers the positioning of amino acid residues along the gradient field of their interactions. This allows navigating the intricate landscape of biosequence interactions intelligently, leading to improved accuracy in T cell receptor–antigen–human leucocyte antigen binding prediction and robust generalization to rare sequences. Furthermore, PISTE effectively recovers residue-level contact relationships even in the absence of three-dimensional structure training data. We applied PISTE in a multitude of immunogenic tumour types to pinpoint neoantigens and discern neoantigen-reactive T cells. In a prospective study of prostate cancer, 75% of the patients elicited immune responses through PISTE-predicted neoantigens. Predicting TCR–antigen–human leucocyte antigen binding opens the door to neoantigen identification. In this study, a physics-inspired sliding transformer (PISTE) system is used to guide the positioning of amino acid residues along the gradient field of their interactions, boosting binding prediction accuracy.
新抗原具有高特异性、低毒性和易于个性化等特点，可诱发免疫反应并清除癌细胞，是很有前途的免疫疗法靶点。然而，由于 T 细胞受体、抗原和人类白细胞抗原序列之间存在复杂的相互作用，识别有效的新抗原仍然十分困难。在这项研究中，我们将重要的物理和生物先验与变压器模型相结合，提出了物理启发滑动变压器（PISTE）。在 PISTE 中，传统的数据驱动注意力机制被物理驱动动力学所取代，物理驱动动力学引导氨基酸残基沿着其相互作用的梯度场进行定位。这样就能智能地浏览错综复杂的生物序列相互作用，从而提高 T 细胞受体-抗原-人类白细胞抗原结合预测的准确性，并对罕见序列进行稳健的泛化。此外，即使在没有三维结构训练数据的情况下，PISTE 也能有效地恢复残基级接触关系。我们将 PISTE 应用于多种免疫原性肿瘤类型，以确定新抗原并识别新抗原反应 T 细胞。在一项前瞻性前列腺癌研究中，75% 的患者通过 PISTE 预测的新抗原产生了免疫反应。
{"title":"Sliding-attention transformer neural architecture for predicting T cell receptor–antigen–human leucocyte antigen binding","authors":"Ziyan Feng, Jingyang Chen, Youlong Hai, Xuelian Pang, Kun Zheng, Chenglong Xie, Xiujuan Zhang, Shengqing Li, Chengjuan Zhang, Kangdong Liu, Lili Zhu, Xiaoyong Hu, Shiliang Li, Jie Zhang, Kai Zhang, Honglin Li","doi":"10.1038/s42256-024-00901-y","DOIUrl":"10.1038/s42256-024-00901-y","url":null,"abstract":"Neoantigens are promising targets for immunotherapy by eliciting immune response and removing cancer cells with high specificity, low toxicity and ease of personalization. However, identifying effective neoantigens remains difficult because of the complex interactions among T cell receptors, antigens and human leucocyte antigen sequences. In this study, we integrate important physical and biological priors with the Transformer model and propose the physics-inspired sliding transformer (PISTE). In PISTE, the conventional, data-driven attention mechanism is replaced with physics-driven dynamics that steers the positioning of amino acid residues along the gradient field of their interactions. This allows navigating the intricate landscape of biosequence interactions intelligently, leading to improved accuracy in T cell receptor–antigen–human leucocyte antigen binding prediction and robust generalization to rare sequences. Furthermore, PISTE effectively recovers residue-level contact relationships even in the absence of three-dimensional structure training data. We applied PISTE in a multitude of immunogenic tumour types to pinpoint neoantigens and discern neoantigen-reactive T cells. In a prospective study of prostate cancer, 75% of the patients elicited immune responses through PISTE-predicted neoantigens. Predicting TCR–antigen–human leucocyte antigen binding opens the door to neoantigen identification. In this study, a physics-inspired sliding transformer (PISTE) system is used to guide the positioning of amino acid residues along the gradient field of their interactions, boosting binding prediction accuracy.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 10","pages":"1216-1230"},"PeriodicalIF":18.8,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00901-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1038/s42256-024-00895-7
Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, B. Aditya Prakash
The COVID-19 pandemic emphasized the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy. Forecasting epidemic progression is a non-trivial task due to multiple confounding factors, such as human behaviour, pathogen dynamics and environmental conditions. However, the surge in research interest and initiatives from public health and funding agencies has fuelled the availability of new data sources that capture previously unobservable aspects of disease spread, paving the way for a spate of ‘data-centred’ computational solutions that show promise for enhancing our forecasting capabilities. Here we discuss various methodological and practical advances and introduce a conceptual framework to navigate through them. First we list relevant datasets, such as symptomatic online surveys, retail and commerce, mobility and genomics data. Next we consider methods, focusing on recent data-driven statistical and deep learning-based methods, as well as hybrid models that combine domain knowledge of mechanistic models with the flexibility of statistical approaches. We also discuss experiences and challenges that arise in the real-world deployment of these forecasting systems, including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline to enable robust future pandemic preparedness. Forecasting epidemic progression is a complex task influenced by various factors, including human behaviour, pathogen dynamics and environmental conditions. Rodríguez, Kamarthi and colleagues provide a review of machine learning methods for epidemic forecasting from a data-centric computational perspective.
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