检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Warrell, Jonathan Alesiani, Francesco Smith, Cameron Mösch, Anja Min, Martin Renqiang NEC Laboratories America Machine Learning Department PrincetonNJ08540 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States NEC Laboratories Europe Kurfuerstenanlage 36 HeidelbergD-69115 Germany Molecular Pathology Unit Center for Cancer Research Massachusetts General Hospital Research Institute Department of Pathology Harvard Medical School BostonMA United States Broad Institute of MIT and Harvard CambridgeMA United States

Levels of selection and multilevel evolutionary processes are essential concepts in evolutionary theory, and yet there is a lack of common mathematical models for these core ideas. Here, we propose a unified mathematical framework for formulating and optimizing multilevel evolutionary processes and genetic algorithms over arbitrarily many levels based on concepts from category theory and population genetics. We formulate a multilevel version of the Wright-Fisher process using this approach, and we show that this model can be analyzed to clarify key features of multilevel selection. Particularly, we derive an extended multilevel probabilistic version of Price's Equation via the Kantorovich Monad, and we use this to characterize regimes of parameter space within which selection acts antagonistically or cooperatively across levels. Finally, we show how our framework can provide a unified setting for learning genetic algorithms (GAs), and we show how we can use a Variational Optimization and a multi-level analogue of coalescent analysis to fit multilevel GAs to simulated data. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Tumor response prediction in ⁹⁰Y radioembolization with PET-based radiomics features and absorbed dose metrics

引用

EJNMMI PHYSICS 2020年第1期7卷 74页

作者： Wei, Lise Cui, Can Xu, Jiarui Kaza, Ravi El Naqa, Issam Dewaraja, Yuni K. Univ Michigan Appl Phys Program Ann Arbor MI 48109 USA Univ Michigan Dept Elect Engn Ann Arbor MI 48109 USA Univ Michigan Dept Radiol Ann Arbor MI 48109 USA Univ Michigan Dept Radiat Oncol Ann Arbor MI 48109 USA H Lee Moffitt Canc Ctr & Res Inst Machine Learning Dept Tampa FL USA

Purpose To evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy Y-90 PET can be integrated to better predict outcomes in microsphere radioembolization of liver malignancies Methods Given the noisy nature of Y-90 PET, first, a liver phantom study with repeated acquisitions and varying reconstruction parameters was used to identify a subset of robust radiomics features for the patient analysis. In 36 radioembolization procedures, Y-90 PET/CT was performed within a couple of hours to extract 46 radiomics features and estimate absorbed dose in 105 primary and metastatic liver lesions. Robust radiomics modeling was based on bootstrapped multivariate logistic regression with shrinkage regularization (LASSO) and Cox regression with LASSO. Nested cross-validation and bootstrap resampling were used for optimal parameter/feature selection and for guarding against overfitting risks. Spearman rank correlation was used to analyze feature associations. Area under the receiver-operating characteristics curve (AUC) was used for lesion response (at first follow-up) analysis while Kaplan-Meier plots and c-index were used to assess progression model performance. Models with absorbed dose only, radiomics only, and combined models were developed to predict lesion outcome. Results The phantom study identified 15/46 reproducible and robust radiomics features that were subsequently used in the patient models. A lesion response model with zone percentage (ZP) and mean absorbed dose achieved an AUC of 0.729 (95% CI 0.702-0.758), and a progression model with zone size nonuniformity (ZSN) and absorbed dose achieved a c-index of 0.803 (95% CI 0.790-0.815) on nested cross-validation (CV). Although the combined models outperformed the radiomics only and absorbed dose only models, statistical significance was not achieved with the current limited data set to establish expected superiority. Conclusion We have developed new lesion-level response and progre

关键词： Y-90 PET imaging Liver cancer Radiomics Absorbed dose Radioembolization

来源：评论

学校读者我要写书评

暂无评论

Integrative analysis of mutated genes and mutational processes reveals novel mutational biomarkers in colorectal cancer

引用

BMC BIOINFORMATICS 2022年第1期23卷 138-138页

作者： Dashti, Hamed Dehzangi, Iman Bayati, Masroor Breen, James Beheshti, Amin Lovell, Nigel Rabiee, Hamid R. Alinejad-Rokny, Hamid Sharif Univ Technol Dept Comp Engn Bioinformat & Computat Biol Lab Tehran 11365 Iran Rutgers State Univ Ctr Computat & Integrat Biol CCIB Camden NJ 08102 USA South Australian Hlth & Med Res Inst Adelaide SA 5000 Australia Univ Adelaide Robinson Res Inst Adelaide SA 5006 Australia Univ Adelaide Bioinformat Hub Adelaide SA 5006 Australia Macquarie Univ Dept Comp Sydney NSW 2109 Australia UNSW Sydney Tyree Inst Hlth Engn Sydney NSW 2052 Australia UNSW Sydney Grad Sch Biomed Engn Sydney NSW 2052 Australia UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab Sydney NSW 2052 Australia Univ New South Wales UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Sydney NSW 2109 Australia

Background Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Recent studies have observed causative mutations in susceptible genes related to colorectal cancer in 10 to 15% of the patients. This highlights the importance of identifying mutations for early detection of this cancer for more effective treatments among high risk individuals. Mutation is considered as the key point in cancer research. Many studies have performed cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes. However, to the best of our knowledge, combination of these features has never been used together for this task. This highlights the potential to introduce better and more inclusive subtype classification approaches using wider range of related features to enable biomarker discovery and thus inform drug development for CRC. Results In this study, we develop a new pipeline based on a novel concept called 'gene-motif', which merges mutated gene information with tri-nucleotide motif of mutated sites, for colorectal cancer subtype identification. We apply our pipeline to the International Cancer Genome Consortium (ICGC) CRC samples and identify, for the first time, 3131 gene-motif combinations that are significantly mutated in 536 ICGC colorectal cancer samples. Using these features, we identify seven CRC subtypes with distinguishable phenotypes and biomarkers, including unique cancer related signaling pathways, in which for most of them targeted treatment options are currently available. Interestingly, we also identify several genes that are mutated in multiple subtypes but with unique sequence contexts. Conclusion Our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers. The new CRC subtypes presented in this study demonstrates distinguished phenotypic properties which can be effectively used to develop new trea

关键词： Colorectal cancer Cancer subtype identification Somatic point mutations Motif analysis Gene prioritization Therapeutic targets Personalized medicine

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Hybrid Auxiliary Field Quantum Monte Carlo for Molecular Systems

arXiv

引用

arXiv 2022年

作者： Chen, Yixiao Zhang, Linfeng Weinan, E. Car, Roberto Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100084 China Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States

We propose a quantum Monte Carlo approach to solve the many-body Schrödinger equation for the electronic ground state. The method combines optimization from variational Monte Carlo and propagation from auxiliary field quantum Monte Carlo, in a way that significantly alleviates the sign problem. In application to molecular systems, we obtain highly accurate results for configurations dominated by either dynamic or static electronic correlation. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

来源：评论

学校读者我要写书评

暂无评论

Deep learning identifies synergistic drug combinations for treating COVID-19

引用

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2021年第39期118卷 1页

作者： Jin, Wengong Stokes, Jonathan M. Eastman, Richard T. Itkin, Zina Zakharov, Alexey, V Collins, James J. Jaakkola, Tommi S. Barzilay, Regina MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA MIT Inst Med Engn & Sci Synthet Biol Ctr Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA Natl Ctr Adv Translat Sci Div Preclin Innovat Rockville MD 20850 USA MIT Abdul Latif Jameel Clin Machine Learning Hlth 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and single agent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists.

关键词： deep learning drug discovery drug synergy SARS-CoV-2

来源：评论

学校读者我要写书评

暂无评论

Eradicating Bacterial Persisters with Combinations of Strongly and Weakly Metabolism-Dependent Antibiotics

引用

CELL CHEMICAL BIOLOGY 2020年第12期27卷 1544-+页

作者： Zheng, Erica J. Stokes, Jonathan M. Collins, James J. Harvard Univ Program Chem Biol Cambridge MA 02138 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA MIT Dept Biol Engn Inst Med Engn & Sci Cambridge MA 02139 USA MIT Synthet Biol Ctr Cambridge MA 02139 USA MIT Machine Learning Pharmaceut Discovery & Synth Con Cambridge MA 02139 USA Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA

The vast majority of bactericidal antibiotics display poor efficacy against bacterial persisters, cells that are in ametabolically repressed state. Molecules that retain their bactericidal functions against such bacteria often display toxicity to human cells, which limits treatment options for infections caused by persisters. Here, we leverage insight into metabolism-dependent bactericidal antibiotic efficacy to design antibiotic combinations that sterilize both metabolically active and persister cells, while minimizing the antibiotic concentrations required. These rationally designed antibiotic combinations have the potential to improve treatments for chronic and recurrent infections.

关键词： antibiotics bacterial persisters metabolism antibiotic combinations

来源：评论

学校读者我要写书评

暂无评论

A probabilistic knowledge graph for target identification

引用

PLOS COMPUTATIONAL BIOLOGY 2024年第4期20卷 e1011945页

作者： Liu, Chang Xiao, Kaimin Yu, Cuinan Lei, Yipin Lyu, Kangbo Tian, Tingzhong Zhao, Dan Zhou, Fengfeng Tang, Haidong Zeng, Jianyang Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing Peoples R China Tsinghua Univ Sch Pharmaceut Sci Beijing Peoples R China Tsinghua Univ Sch Life Sci Joint Grad Program Peking Tsinghua NIBS Beijing Peoples R China Silexon AI Technol Co Ltd Machine Learning Dept Nanjing Jiangsu Peoples R China Jilin Univ Coll Comp Sci & Technol Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun Jilin Peoples R China Westlake Univ Sch Engn Hangzhou Peoples R China Westlake Lab Life Sci & Biomed Hangzhou Peoples R China Westlake Univ Res Ctr Ind Future Hangzhou Zhejiang Peoples R China Westlake Univ Sch Engn Hangzhou Zhejiang Peoples R China

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially the machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process. The selection of a disease target, a biological entity with which potential drugs interact to treat diseases, is often the first step of a drug discovery project. Known to be extremely costly, drug discovery projects spend tremendous resources on failed drug instances, most of which result from inadequate choices of targets. To help

关键词： Colorectal cancer Melanoma Protein interaction networks Drug discovery machine learning Cancer treatment Drug interactions Neural networks

来源：评论

学校读者我要写书评

暂无评论

Ant Colony Optimization based Medical Image Preservation and Segmentation

Ant Colony Optimization based Medical Image Preservation and...

引用

Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Priya Nandihal Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Madhumala R B Ashish Khanna V Sunil Kumar Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceara Brazil Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

ISBN: (纸本)9781665493970

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work.

关键词： Integrated optics Image segmentation Ant colony optimization Biomedical optical imaging Optical design Image edge detection Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution

引用

NATURE NEUROSCIENCE 2022年第12期25卷 1724-+页

作者： Zhu, Feng Grier, Harrison A. Tandon, Raghav Cai, Changjia Agarwal, Anjali Giovannucci, Andrea Kaufman, Matthew T. Pandarinath, Chethan Emory Univ Wallace H Coulter Dept Biomed Engn Atlanta GA 30322 USA Georgia Inst Technol Atlanta GA 30332 USA Emory Univ Grad Div Biol & Biomed Sci Neurosci Grad Program Atlanta GA 30322 USA Univ Chicago Comm Computat Neurosci Chicago IL 60637 USA Univ North Carolina Chapel Hill & North Carolina Joint Biomed Engn Dept Chapel Hill NC 27514 USA Univ North Carolina Neurosci Ctr Chapel Hill NC 27515 USA North Carolina State Univ Closed Loop Engn Adv Rehabil CLEAR Raleigh NC 27695 USA Univ Chicago Dept Organismal Biol & Anat 1025 E 57Th St Chicago IL 60637 USA Univ Chicago Neurosci Inst Chicago IL 60637 USA Emory Univ Dept Neurosurg Atlanta GA 30322 USA Georgia Inst Technol Ctr Machine Learning Atlanta GA 30332 USA

In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL), a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically recorded spikes. It incorporates a new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced. Zhu et al. develop a deep learning method to precisely infer single-trial neural dynamics from calcium imaging with subframe temporal resolution, which shows improvement over the state-of-the-art methods in capturing high-frequency dynamics and predicting behavior.

关键词： Dynamical systems Fluorescence imaging machine learning Neural decoding

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：