Nature Machine Intelligence最新文献

Seeking clarity rather than strong opinions on intelligence

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-12-18 DOI: 10.1038/s42256-024-00968-7

引用次数: 0

Strategies needed to counter potential AI misuse

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-12-18 DOI: 10.1038/s42256-024-00967-8

引用次数: 0

Leveraging ancestral sequence reconstruction for protein representation learning

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-12-18 DOI: 10.1038/s42256-024-00935-2

D. S. Matthews, M. A. Spence, A. C. Mater, J. Nichols, S. B. Pulsford, M. Sandhu, J. A. Kaczmarski, C. M. Miton, N. Tokuriki, C. J. Jackson

{"title":"Leveraging ancestral sequence reconstruction for protein representation learning","authors":"D. S. Matthews, M. A. Spence, A. C. Mater, J. Nichols, S. B. Pulsford, M. Sandhu, J. A. Kaczmarski, C. M. Miton, N. Tokuriki, C. J. Jackson","doi":"10.1038/s42256-024-00935-2","DOIUrl":"10.1038/s42256-024-00935-2","url":null,"abstract":"Protein language models (PLMs) convert amino acid sequences into the numerical representations required to train machine learning models. Many PLMs are large (>600 million parameters) and trained on a broad span of protein sequence space. However, these models have limitations in terms of predictive accuracy and computational cost. Here we use multiplexed ancestral sequence reconstruction to generate small but focused functional protein sequence datasets for PLM training. Compared to large PLMs, this local ancestral sequence embedding produces representations with higher predictive accuracy. We show that due to the evolutionary nature of the ancestral sequence reconstruction data, local ancestral sequence embedding produces smoother fitness landscapes, in which protein variants that are closer in fitness value become numerically closer in representation space. This work contributes to the implementation of machine learning-based protein design in real-world settings, where data are sparse and computational resources are limited. Matthews et al. present a protein sequence embedding based on data from ancestral sequences that allows machine learning to be used for tasks where training data are scarce or expensive.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 12","pages":"1542-1555"},"PeriodicalIF":18.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841572","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}

引用次数: 0

Reply to: Limitations in odour recognition and generalization in a neuromorphic olfactory circuit

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-12-16 DOI: 10.1038/s42256-024-00951-2

Roy Moyal, Nabil Imam, Thomas A. Cleland

引用次数: 0

Limitations in odour recognition and generalization in a neuromorphic olfactory circuit

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-12-16 DOI: 10.1038/s42256-024-00952-1

Nik Dennler, André van Schaik, Michael Schmuker

引用次数: 0

Stable Cox regression for survival analysis under distribution shifts 分布变化条件下用于生存分析的稳定考克斯回归

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-12-13 DOI: 10.1038/s42256-024-00932-5

Shaohua Fan, Renzhe Xu, Qian Dong, Yue He, Cheng Chang, Peng Cui

{"title":"Stable Cox regression for survival analysis under distribution shifts","authors":"Shaohua Fan, Renzhe Xu, Qian Dong, Yue He, Cheng Chang, Peng Cui","doi":"10.1038/s42256-024-00932-5","DOIUrl":"10.1038/s42256-024-00932-5","url":null,"abstract":"Survival analysis aims to estimate the impact of covariates on the expected time until an event occurs, which is broadly utilized in disciplines such as life sciences and healthcare, substantially influencing decision-making and improving survival outcomes. Existing methods, usually assuming similar training and testing distributions, nevertheless face challenges with real-world varying data sources, creating unpredictable shifts that undermine their reliability. This urgently necessitates that survival analysis methods should utilize stable features across diverse cohorts for predictions, rather than relying on spurious correlations. To this end, we propose a stable Cox model with theoretical guarantees to identify stable variables, which jointly optimizes an independence-driven sample reweighting module and a weighted Cox regression model. Through extensive evaluation on simulated and real-world omics and clinical data, stable Cox not only shows strong generalization ability across diverse independent test sets but also stratifies the subtype of patients significantly with the identified biomarker panels. Survival prediction models used in healthcare usually assume that training and test data share a similar distribution, which is not true in real-world settings. Cui and colleagues develop a stable Cox regression model that can identify stable variables for predicting survival outcomes under distribution shifts.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 12","pages":"1525-1541"},"PeriodicalIF":18.8,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00932-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815895","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}

引用次数: 0

Kernel approximation using analogue in-memory computing

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-12-13 DOI: 10.1038/s42256-024-00943-2

Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos, Corey Lammie, Manuel Le Gallo, Abbas Rahimi, Abu Sebastian

{"title":"Kernel approximation using analogue in-memory computing","authors":"Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos, Corey Lammie, Manuel Le Gallo, Abbas Rahimi, Abu Sebastian","doi":"10.1038/s42256-024-00943-2","DOIUrl":"10.1038/s42256-024-00943-2","url":null,"abstract":"Kernel functions are vital ingredients of several machine learning (ML) algorithms but often incur substantial memory and computational costs. We introduce an approach to kernel approximation in ML algorithms suitable for mixed-signal analogue in-memory computing (AIMC) architectures. Analogue in-memory kernel approximation addresses the performance bottlenecks of conventional kernel-based methods by executing most operations in approximate kernel methods directly in memory. The IBM HERMES project chip, a state-of-the-art phase-change memory-based AIMC chip, is utilized for the hardware demonstration of kernel approximation. Experimental results show that our method maintains high accuracy, with less than a 1% drop in kernel-based ridge classification benchmarks and within 1% accuracy on the long-range arena benchmark for kernelized attention in transformer neural networks. Compared to traditional digital accelerators, our approach is estimated to deliver superior energy efficiency and lower power consumption. These findings highlight the potential of heterogeneous AIMC architectures to enhance the efficiency and scalability of ML applications. A kernel approximation method that enables linear-complexity attention computation via analogue in-memory computing (AIMC) to deliver superior energy efficiency is demonstrated on a multicore AIMC chip.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 12","pages":"1605-1615"},"PeriodicalIF":18.8,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815893","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}

引用次数: 0

Envisioning better benchmarks for machine learning PDE solvers

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-12-13 DOI: 10.1038/s42256-024-00962-z

Johannes Brandstetter

引用次数: 0

Discussions of machine versus living intelligence need more clarity 关于机器智能与生活智能的讨论需要更加明确

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-12-13 DOI: 10.1038/s42256-024-00955-y

Nicolas Rouleau, Michael Levin

引用次数: 0

Reply to: Deeper evaluation of a single-cell foundation model