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3rd Int Workshop on Interpretabil of machine Intelligence in Med Image Comp / 2nd Int Workshop on Med Image learning with Less Labels and Imperfect Data / 5th Int Workshop on Largescale Annotat of Biomed Data and Expert Label Synthesis

作者： Pooch, Eduardo H. P. Ballester, Pedro Barros, Rodrigo C. Pontificia Univ Catolica Rio Grande do Sul Sch Technol Machine Learning Theory & Applicat Res Grp MALTA Porto Alegre RS Brazil McMaster Univ Neurosci Grad Program Hamilton ON Canada

ISBN: (纸本)9783030611651;9783030611668

To train deep learning models in a supervised fashion, we need a significant amount of training data, but in most medical imaging scenarios, there is a lack of annotated data available. In this paper, we compare state-of-the-art semi-supervised classification methods in a medical imaging scenario. We evaluate the performance of different approaches in a chest radiograph classification task using the ChestX-ray14 dataset. We adapted methods based on pseudo-labeling and consistency regularization to perform multi-label classification and to use a state-of-the-art model architecture in chest radiograph classification. Our proposed approaches resulted in average AUCs up to 0.6691 with only 25 labeled samples per class, and an average AUC of 0.7182 when using only 2% of the labeled data, achieving results superior to previous approaches on semi-supervised chest radiograph classification.

关键词： Medical imaging Semi-supervised learning Deep learning

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Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer

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BRIEFINGS IN BIOINFORMATICS 2024年第2期25卷 bbae071页

作者： Lu, Yitao Chu, Qian Li, Zhen Wang, Mengdi Gatenby, Robert Zhang, Qingpeng City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China Huazhong Univ Sci & Technol Med Oncol & Radiat Oncol Tongji Hosp Tongji Med Coll Wuhan Peoples R China Huazhong Univ Sci & Technol Dept Radiol Tongji Hosp Tongji Med Coll Wuhan Peoples R China Princeton Univ Dept Elect & Comp Engn Princeton NJ USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ USA H Lee Moffitt Canc Ctr & Res Inst Dept Chair Radiol Tampa FL USA H Lee Moffitt Canc Ctr & Res Inst Canc Biol & Evolut Program Tampa FL USA Univ Hong Kong Musketeers Fdn Inst Data Sci LKS Fac Med Hong Kong Peoples R China Univ Hong Kong Dept Pharmacol & Pharm LKS Fac Med Hong Kong Peoples R China

The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, IADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed IADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of IADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.

关键词： Adaptive therapy Prostate cancer Personalized medicine Reinforcement learning

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Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2021年第31期118卷 1页

作者： Ruediger, Sten Konigorski, Stefan Rakowski, Alexander Edelman, Jonathan Antonio Zernick, Detlef Thieme, Alexander Lippert, Christoph NET CHECK GmbH Machine Leaning Unit Dept Engn D-10829 Berlin Germany Univ Potsdam Hasso Plattner Inst Digital Hlth Machine Learning D-14482 Potsdam Germany Icahn Sch Med Mt Sinai Hasso Plattner Inst Digital Hlth New York NY 10029 USA Charite Univ Med Berlin Dept Radiat Oncol D-13353 Berlin Germany Berlin Inst Hlth BIH Scientist Program D-10178 Berlin Germany Stanford Univ Dept Med Stanford CA 94305 USA Stanford Univ Dept Biomed Data Sci Stanford CA 94305 USA

Over the last months, cases of SARS-CoV-2 surged repeatedly in many countries but could often be controlled with nonpharmaceutical interventions including social distancing. We analyzed deidentified Global Positioning System (GPS) tracking data from 1.15 to 1.4 million cell phones in Germany per day between March and November 2020 to identify encounters between individuals and statistically evaluate contact behavior. Using graph sampling theory, we estimated the contact index (CX), a metric for number and heterogeneity of contacts. We found that CX, and not the total number of contacts, is an accurate predictor for the effective reproduction number R derived from case numbers. A high correlation between CX and R recorded more than 2 wk later allows assessment of social behavior well before changes in case numbers become detectable. By construction, the CX quantifies the role of superspreading and permits assigning risks to specific contact behavior. We provide a critical CX value beyond which R is expected to rise above 1 and propose to use that value to leverage the social-distancing interventions for the coming months.

关键词： COVID-19 network science epidemiology

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CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments

arXiv

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arXiv 2024年

作者： Huang, Kaixuan Qu, Yuanhao Cousins, Henry Johnson, William A. Yin, Di Shah, Mihir Zhou, Denny Altman, Russ Wang, Mengdi Cong, Le Department of Electrical and Computer Engineering Princeton University United States Department of Pathology Department of Genetics Stanford University School of Medicine United States Cancer Biology Program Stanford University School of Medicine United States Department of Medicine Stanford University School of Medicine United States Medical Scientist Training Program Stanford University School of Medicine United States Google DeepMind United Kingdom Department of Bioengineering Department of Genetics Stanford University United States Center for Statistics and Machine Learning Princeton University United States

The introduction of genome engineering technology has transformed biomedical research, making it possible to make precise changes to genetic information. However, creating an efficient gene-editing system requires a deep understanding of CRISPR technology, and the complex experimental systems under investigation. While Large Language Models (LLMs) have shown promise in various tasks, they often lack specific knowledge and struggle to accurately solve biological design problems. In this work, we introduce CRISPR-GPT, an LLM agent augmented with domain knowledge and external tools to automate and enhance the design process of CRISPR-based gene-editing experiments. CRISPR-GPT leverages the reasoning ability of LLMs to facilitate the process of selecting CRISPR systems, designing guide RNAs, recommending cellular delivery methods, drafting protocols, and designing validation experiments to confirm editing outcomes. We showcase the potential of CRISPR-GPT for assisting non-expert researchers with gene-editing experiments from scratch and validate the agent’s effectiveness in a real-world use case. Furthermore, we explore the ethical and regulatory considerations associated with automated gene-editing design, highlighting the need for responsible and transparent use of these tools. Our work aims to bridge the gap between beginner biological researchers and CRISPR genome engineering techniques, and demonstrate the potential of LLM agents in facilitating complex biological discovery tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Genes

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DEEP ENSEMBLE FOR ROTORCRAFT ATTITUDE PREDICTION

arXiv

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arXiv 2023年

作者： Khan, Hikmat Bouaynaya, Nidhal C. Rasool, Ghulam Travis, Tyler Thompson, Lacey Johnson, Charles C. Dept. of Electrical and Computer Engineering Rowan University GlassboroNJ United States Dept. of Machine Learning Moffitt Cancer Center TampaFL United States Federal Aviation Administration Atlantic CityNJ United States Research Engineer Flight Test Engineer and Program Lead Federal Aviation Administration Egg Harbor Township United Kingdom

Historically, the rotorcraft community has experienced a higher fatal accident rate than other aviation segments, including commercial and general aviation. To date, traditional methods applied to reduce incident rates have not proven hugely successful for the rotorcraft community. Recent advancements in artificial intelligence (AI) and the application of these technologies in different areas of our lives are both intriguing and encouraging. When developed appropriately for the aviation domain, AI techniques may provide an opportunity to help design systems that can address rotorcraft safety challenges. Our recent work demonstrated that AI algorithms could use video data from onboard cameras and correctly identify different flight parameters from cockpit gauges, e.g., indicated airspeed. These AI-based techniques provide a potentially cost-effective solution, especially for small helicopter operators, to record the flight state information and perform post-flight analyses. We also showed that carefully designed and trained AI systems can accurately predict rotorcraft attitude (i.e., pitch and yaw) from outside scenes (images or video data). Ordinary off-the-shelf video cameras were installed inside the rotorcraft cockpit to record the outside scene, including the horizon. The AI algorithm was able to correctly identify rotorcraft attitude at an accuracy in the range of 80%. In this work, we combined five different onboard camera viewpoints to improve attitude prediction accuracy to 94%. Our current approach, which is referred to as ensembled prediction, significantly increased the reliability in the predicted attitude (i.e., pitch and yaw). For example, in some camera views, the horizon may be obstructed or not visible. The proposed ensemble method can combine visual details recorded from other cameras and predict the attitude with high reliability. In our setup, the five onboard camera views included pilot windshield, co-pilot windshield, pilot Electronic Flight In

关键词： Instrument displays

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A foundation model of transcription across human cell types

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NATURE 2025年第8047期637卷 1-9页

作者： Fu, Xi Mo, Shentong Buendia, Alejandro Laurent, Anouchka P. Shao, Anqi Alvarez-Torres, Maria del Mar Yu, Tianji Tan, Jimin Su, Jiayu Sagatelian, Romella Ferrando, Adolfo A. Ciccia, Alberto Lan, Yanyan Owens, David M. Palomero, Teresa Xing, Eric P. Rabadan, Raul Columbia Univ Dept Syst Biol Program Math Genom New York NY 10027 USA Columbia Univ Dept Biomed Informat New York NY 10027 USA Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Carnegie Mellon Univ Dept Machine Learning Pittsburgh PA 15213 USA Columbia Univ Inst Canc Genet New York NY USA Columbia Univ Dept Dermatol New York NY USA NYU Inst Syst Genet Grossman Sch Med New York NY USA Regeneron Regeneron Genet Ctr Tarrytown NY USA Columbia Univ Dept Genet & Dev New York NY USA Tsinghua Univ Inst AI Ind Res Beijing Peoples R China Tsinghua Univ Beijing Frontier Res Ctr Biol Struct Beijing Peoples R China Columbia Univ Dept Pathol & Cell Biol New York NY USA

Transcriptional regulation, which involves a complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate to unseen cell types and conditions. Here we introduce GET (general expression transformer), an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types1,2. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types3. GET also shows remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovers universal and cell-type-specific transcription factor interaction networks. We evaluated its performance in prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors and found that it outperforms current models4 in predicting lentivirus-based massively parallel reporter assay readout5,6. In fetal erythroblasts7, we identified distal (greater than 1 Mbp) regulatory regions that were missed by previous models, and, in B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukaemia risk predisposing germline mutation8, 9-10. In sum, we provide a generalizable and accurate model for transcription together with catalogues of gene regulation and transcription factor interactions, all with cell type specificity.

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Inferring population structure in biobank-scale genomic data

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AMERICAN JOURNAL OF HUMAN GENETICS 2022年第4期109卷 727-737页

作者： Chiu, Alec M. Molloy, Erin K. Tan, Zilong Talwalkar, Ameet Sankararaman, Sriram Univ Calif Los Angeles Bioinformat Interdept Program Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA Facebook Inc Menlo Pk CA 94025 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Univ Calif Los Angeles Dept Human Genet Los Angeles CA 90095 USA Univ Calif Los Angeles David Geffen Sch Med Dept Computat Med Los Angeles CA 90095 USA

Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. Although a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.

关键词： admixture ancestry biobank genetic structure population structure scalability

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A comparison between Artificial Neural Network and Hybrid Intelligent Genetic Algorithm in predicting the severity of fixed object crashes among elderly drivers

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ACCIDENT ANALYSIS AND PREVENTION 2020年第0期138卷 105468-000页

作者： Amiri, Amir Mohammadian Sadri, Amirhossein Nadimi, Navid Shams, Moe McMaster Univ McMaster Inst Transportat & Logist MITL Hamilton ON Canada Iran Univ Sci & Technol Civil Engn Dept Tehran Iran Shahid Bahonar Univ Fac Engn Tehran Iran McGill Univ Sch Continuous Studies Data Sci & Machine Learning Program Montreal PQ Canada

Run-off-road (ROR) crashes have always been a major concern as this type of crash is usually associated with a considerable number of serious injury and fatal crashes. A substantial portion of ROR fatalities occur in collisions with fixed objects at the roadside. Thus, this study seeks to investigate the severity of ROR crashes where elderly drivers, aged 65 years or more, hit a fixed object. The reason why the present study investigates this issue among older drivers is that, comparing to younger drivers, this age group of drivers have different psychological and physical features. Because of these differences, they are more likely to get injured in ROR types of crashes. This paper applies two types of Artificial Intelligence (AI) techniques, including hybrid Intelligent Genetic Algorithm and Artificial Neural Network (ANN) using the crashe information of California in 2012 obtained from Highway Safety Information System (HSIS) database. Although the results showed that the developed ANN outperformed the hybrid Intelligent Genetic Algorithm, the hybrid approach was more capable of predicting high-severity crashes. This is rooted in the way the hybrid model was trained by taking advantage of the Genetic Algorithm (GA). The results also indicated that the light condition has been the most significant parameter in evaluating the level of severity associated with fixed object crashes among elderly drivers, which is followed by the existence of the right and left shoulders. Following these three contributing factors, cause of collision, Average Annual Daily Traffic (AADT), number of involved vehicles, age, road surface condition, and gender have been identified as the most important variables in the developed ANN, respectively. This helps to identify gaps and improve public safety towards improving the overall highway safety situation of older drivers.

关键词： Hybrid models Elderly drivers Fixed object crashes Crash severity

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Empowering Optimal Control with machine learning: A Perspective from Model Predictive Control

arXiv

引用

arXiv 2022年

作者： Weinan, E. Han, Jiequn Long, Jihao AI for Science Institute Beijing China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing China Center for Computational Mathematics Flatiron Institute New York10010 United States Program of Applied and Computational Mathematics Princeton University Princeton08544 United States

Solving complex optimal control problems have confronted computational challenges for a long time. Recent advances in machine learning have provided us with new opportunities to address these challenges. This paper takes model predictive control, a popular optimal control method, as the primary example to survey recent progress that leverages machine learning techniques to empower optimal control solvers. We also discuss some of the main challenges encountered when applying machine learning to develop more robust optimal control algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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Quantum Markov blankets for meta-learned classical inferential paradoxes with suboptimal free energy

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BEHAVIORAL AND BRAIN SCIENCES 2024年 47卷 e150页

作者： Clark, Kevin B. Cures Within Reach Chicago IL 60602 USA Felidae Conservat Fund Mill Valley CA 94941 USA Natl Sci Fdn Adv Cyberinfrastruct Coordinat Ecosys Campus & Domain Champ Program Multitier Assistance Training & Computat Help MATC Alexandria VA 22314 USA Univ Penn Penn Ctr Innovat Expert Network Philadelphia PA 19104 USA NASA Ames Res Ctr NASA Astrobiol Program Network Life Detect NfoLD Mountain View CA 94035 USA NASA Ames Res Ctr Multi & Syst Biol NASA GeneLab Mountain View CA 94035 USA NASA Ames Res Ctr Artificial Intelligence & Machine Learning Anal Wo NASA GeneLab Mountain View CA 94035 USA NASA Ames Res Ctr Frontier Dev Lab Mountain View CA 94035 USA SETI Inst Mountain View CA 94043 USA Peace Innovat Inst NL-2511 The Hague Netherlands Stanford Univ Palo Alto CA 94305 USA Max Planck Alumni Assoc Shared Interest Grp Nat & Artificial Intelligence Berlin Germany Inst Elect & Elect Engineers IEEE Biometr & Nanotechnol Councils New York NY 10016 USA

Quantum active Bayesian inference and quantum Markov blankets enable robust modeling and simulation of difficult-to-render natural agent-based classical inferential paradoxes interfaced with task-specific environments. Within a non-realist cognitive completeness regime, quantum Markov blankets ensure meta-learned irrational decision making is fitted to explainable manifolds at optimal free energy, where acceptable incompatible observations or temporal Bell-inequality violations represent important verifiable real-world outcomes.

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