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SSRN 2023年

作者： Kong, Yan Lu, Hui Ren, Yongyong Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China SJTU-Yale Joint Center for Biostatistics and Data Science National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China Center for Biomedical Informatics Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine Shanghai Children's Hospital Shanghai200240 China School of Life Sciences and Biotechnology India

Molecular bio-statistic workflows in oncology often rely on predictive models from multimodal data. Multimodal data fusion is becoming a fundamental but challenging task. Advances in deep learning (DL), and artificial intelligence technology (AI), have enabled the multimodal fusion from huge volume of data. Here, we present a decision level multimodal data fusion framework for integrating multi-omics and pathological tissue slide images for prognosis prediction. Basic convolution neural networks aim at extracting representation features from the perspective of perception and not fully explore the spatial information between nuclei instances. Our approach establish the spatial map of instances by connecting the neighboring nuclei in space and calculated the characteristic tensor via graph convolution layers for the input pathological tissue slide image. The subsequent Global Average Pooling calculation behind, we eliminated the heterogeneity between the feature tensors from pathological images and the multi-omics. We performed our proposed approach using beast cancer and non-small cell lung cancer datasets from the Cancer Genome Atlas (TCGA), which contains paired whole-slide images, transcriptome data, genotype, epienetic, and survival information. In a 10-fold cross-validation, the comparison results demonstrate that the multimodal fusion paradigm improves outcome predictions from single modal data alone with the average C-Index increasing from 0.61 and 0.52 to 0.75 and 0.67 for Breast cancer and Non-small cell lung cancer cohort separately. The proposed decision-level multimodal data fusion framework is hoped to provide insights and technically methodologies for the follow-up studies that seed to merge heterogeneous data in disease mechanisms. © 2023, The Authors. All rights reserved.

关键词： Tensors

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Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

arXiv

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arXiv 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Zhang, Yue Wang, Ren Shi, Xiaoshuang Xu, Kaidi Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering. However, a robust and general uncertainty measure for free-form answers has not been well-established in open-ended medical question-answering (QA) tasks, where generative inequality introduces a large number of irrelevant words and sequences within the generated set for uncertainty quantification (UQ), which can lead to biases. This paper introduces Word-Sequence Entropy (WSE), a method that calibrates uncertainty at both the word and sequence levels, considering semantic relevance. WSE quantifies uncertainty in a way that is more closely aligned with the reliability of LLMs during uncertainty quantification (UQ). We compare WSE with six baseline methods on five free-form medical QA datasets, utilizing seven popular large language models (LLMs). Experimental results demonstrate that WSE exhibits superior performance in UQ under two standard criteria for correctness evaluation. Additionally, in terms of real-world medical QA applications, the performance of LLMs is significantly enhanced (e.g., a 6.36% improvement in model accuracy on the COVID-QA dataset) by employing responses with lower uncertainty that are identified by WSE as final answers, without any additional task-specific fine-tuning or architectural modifications. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

SSRN

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SSRN 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Yao, Huaxiu Zhang, Yue Wang, Ren Xu, Kaidi Shi, Xiaoshuang Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence interaction systems, particularly in the healthcare engineering domain. However, a general method for quantifying the uncertainty of free-form answers has yet to be established in open-ended medical question-answering (QA) tasks, where irrelevant words and sequences with limited semantic information can be the primary source of uncertainty due to the presence of generative inequality. In this paper, we propose the Word-Sequence Entropy (WSE ), which calibrates the uncertainty proportion at both the word and sequence levels according to the semantic relevance, with greater emphasis placed on keywords and more relevant sequences when performing uncertainty quantification. We compare WSE with 6 baseline methods on 5 free-form medical QA datasets, utilizing 7 "off-the-shelf" large language models (LLMs), and show that WSE exhibits superior performance on accurate uncertainty measurement under two standard criteria for correctness evaluation. Additionally, in terms of the potential for real-world medical QA applications, we achieve a significant enhancement (e.g., +6.36% improvement of model accuracy on the COVID-QA dataset) in the performance of LLMs when employing sequences with lower uncertainty that are identified by WSE as final answers, without requiring additional task-specific fine-tuning or architectural modifications. © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 882-898页

作者： Wubing Zhang Shourya S.Roy Burman Jiaye Chen Katherine A.Donovan Yang Cao Chelsea Shu Boning Zhang Zexian Zeng Shengqing Gu Yi Zhang Dian Li Eric S.Fischer Collin Tokheim X.Shirley Liu Department of Data Science Dana-Farber Cancer InstituteBostonMA 02215USA Department of Biostatistics Harvard T.H.Chan School of Public HealthBostonMA 02115USA Department of Cancer Biology Dana-Farber Cancer InstituteBostonMA 02215USA Department of Biological Chemistry and Molecular Pharmacology Harvard Medical SchoolBostonMA 02115USA Department of Biomedical Informatics Harvard Medical SchoolBostonMA 02115USA Center of Growth Metabolismand AgingKey Laboratory of Bio-resource and Eco-environmentMinistry of EducationCollege of Life SciencesSichuan UniversityChengdu 610064China Research Scholar Initiative Graduate School of Arts and SciencesHarvard UniversityCambridgeMA 02138USA

Targeted protein degradation(TPD)has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell’s endogenous protein degradation ***,the susceptibility of proteins for targeting by TPD approaches,termed“degradability”,is largely ***,we developed a machine learning model,model-free analysis of protein degradability(MAPD),to predict degradability from features intrinsic to protein *** shows accurate performance in predicting kinases that are degradable by TPD compounds[with an area under the precision–recall curve(AUPRC)of 0.759 and an area under the receiver operating characteristic curve(AUROC)of 0.775]and is likely generalizable to independent non-kinase *** found five features with statistical significance to achieve optimal prediction,with ubiquitination potential being the most *** structural modeling,we found that E2-accessible ubiquitination sites,but not lysine residues in general,are particularly associated with kinase ***,we extended MAPD predictions to the entire proteome to find964 disease-causing proteins(including proteins encoded by 278 cancer genes)that may be tractable to TPD drug development.

关键词： Targeted protein degradation Degradability Protein-intrinsic feature Ubiquitination Machine learning

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QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction

arXiv

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arXiv 2024年

作者： Qiu, Peijie Chakrabarty, Satrajit Nguyen, Phuc Ghosh, Soumyendu Sekhar Sotiras, Aristeidis Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States Department of Biomedical Engineering University of Cincinnati CincinnatiOH United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States

Deep learning has made significant strides in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans in recent years. However, the reliability of these tools is hampered by the presence of poor-quality segmentation outliers, particularly in out-of-distribution samples, making their implementation in clinical practice difficult. Therefore, there is a need for quality control (QC) to screen the quality of the segmentation results. Although numerous automatic QC methods have been developed for segmentation quality screening, most were designed for cardiac MRI segmentation, which involves a single modality and a single tissue type. Furthermore, most prior works only provided subject-level predictions of segmentation quality and did not identify erroneous parts segmentation that may require refinement. To address these limitations, we proposed a novel multi-task deep learning architecture, termed QCResUNet, which produces subject-level segmentation-quality measures as well as voxel-level segmentation error maps for each available tissue class. To validate the effectiveness of the proposed method, we conducted experiments on assessing its performance on evaluating the quality of two distinct segmentation tasks. First, we aimed to assess the quality of brain tumor segmentation results. For this task, we performed experiments on one internal (Brain Tumor Segmentation (BraTS) Challenge 2021, n = 1, 251) and two external datasets (BraTS Challenge 2023 in Sub-Saharan Africa Patient Population (BraTS-SSA), n = 40;Washington University School of Medicine (WUSM), n = 175). Specifically, we first performed a three-fold cross-validation on the internal dataset using segmentations generated by different methods at various quality levels, followed by an evaluation on the external datasets. Second, we aimed to evaluate the segmentation quality of cardiac Magnetic Resonance Imaging (MRI) data from the Automated Cardiac Diagnosis Challenge (ACDC, n = 100). The pr

关键词： Magnetic resonance imaging

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T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy 27th

T-Cell Receptor Optimization with Reinforcement Learning an...

引用

27th International Conference on Research in Computational Molecular Biology, RECOMB 2023

作者： Chen, Ziqi Min, Martin Renqiang Guo, Hongyu Cheng, Chao Clancy, Trevor Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Machine Learning Department NEC Labs PrincetonNJ08540 United States Digital Technologies Research Centre National Research Council Canada Ontario Canada Department of Medicine Baylor College of Medicine HoustonTX77030 United States NEC Oncolmmunity AS Oslo Cancer Cluster Innovation Park Ullernchausséen 64 Oslo0379 Norway Biomedical Informatics The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

ISBN: (纸本)9783031291180

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (Formula Presented) problem, and presented a framework (Formula Presented) with a mutation policy using proximal policy optimization. (Formula Presented) mutates TCRs into effective ones that can recognize given peptides. (Formula Presented) leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared (Formula Presented) with multiple baseline methods and demonstrated that (Formula Presented) significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of(Formula Presented) for both precision immunotherapy and peptide-recognizing TCR motif discovery. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Advancing bioinformatics with large language models: components, applications and perspectives

arXiv

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arXiv 2024年

作者： Liu, Jiajia Yang, Mengyuan Yu, Yankai Xu, Haixia Wang, Tiangang Li, Kang Zhou, Xiaobo Center for Computational Systems Medicine McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Department of Cell Biology and Genetics School of Basic Medical Sciences Xi’an Jiaotong University Health Science Center Xi’an China School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu611756 China West China Biomedical Big Data Center West China Hospital Sichuan University Sichuan Chengdu610041 China McGovern Medical School The University of Texas Health Science Center at Houston HoustonTX77030 United States School of Dentistry The University of Texas Health Science Center at Houston HoustonTX77030 United States

Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will provide a comprehensive overview of the essential components of large language models (LLMs) in bioinformatics, spanning genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. Key aspects covered include tokenization methods for diverse data types, the architecture of transformer models, the core attention mechanism, and the pre-training processes underlying these models. Additionally, we will introduce currently available foundation models and highlight their downstream applications across various bioinformatics domains. Finally, drawing from our experience, we will offer practical guidance for both LLM users and developers, emphasizing strategies to optimize their use and foster further innovation in the field. © 2024, CC BY.

关键词： Self-supervised learning

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AI-Driven Antimicrobial Peptide Discovery: Mining and Generation

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Accounts of Chemical Research 2025年第12期58卷 1831-1846页

作者： Szymczak, Paulina Zarzecki, Wojciech Wang, Jiejing Duan, Yiqian Wang, Jun Coelho, Luis Pedro de la Fuente-Nunez, Cesar Szczurek, Ewa Institute of AI for Health Helmholtz Zentrum Munich Neuherberg 85764 Germany Faculty of Mathematics Informatics and Mechanics University of Warsaw Warsaw 02-097 Poland Faculty of Electronics and Information Technology Warsaw University of Technology Warsaw 00-661 Poland Institute of Microbiology Chinese Academy of Sciences Beijing 100101 China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai 200433 China Centre for Microbiome Research School of Biomedical Sciences Queensland University of Technology Translational Research Institute Woolloongabba 4102 QLD Australia Centre for Data Science Queensland University of Technology Brisbane 4001 QLD Australia Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia 19104 PA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania Philadelphia 19104 PA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania Philadelphia 19104 PA United States Penn Institute for Computational Science University of Pennsylvania Philadelphia 19104 PA United States

Conspectus The escalating threat of antimicrobial resistance (AMR) poses a significant global health crisis, potentially surpassing cancer as a leading cause of death by 2050. Traditional antibiotic discovery methods have not kept pace with the rapidly evolving resistance mechanisms of pathogens, highlighting the urgent need for novel therapeutic strategies. In this context, antimicrobial peptides (AMPs) represent a promising class of therapeutics due to their selectivity toward bacteria and slower induction of resistance compared to classical, small molecule antibiotics. However, designing effective AMPs remains challenging because of the vast combinatorial sequence space and the need to balance efficacy with low toxicity. Addressing this issue is of paramount importance for chemists and researchers dedicated to developing next-generation antimicrobial agents. Artificial intelligence (AI) presents a powerful tool to revolutionize AMP discovery. By leveraging AI, we can navigate the immense sequence space more efficiently, identifying peptides with optimal therapeutic properties. This Account explores the emerging application of AI in AMP discovery, focusing on two primary strategies: AMP mining, and AMP generation, as well as the use of discriminative methods as a valuable toolbox. AMP mining involves scanning biological sequences to identify potential AMPs. Discriminative models are then used to predict the activity and toxicity of these peptides. This approach has successfully identified numerous promising candidates, which were subsequently validated experimentally, demonstrating the potential of AI in AMP design and discovery. AMP generation, on the other hand, creates novel peptide sequences by learning from existing data through generative modeling. This class of models optimizes for desired properties, such as increased activity and reduced toxicity, potentially producing synthetic peptides that surpass naturally occurring ones. Despite the risk of generatin

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Optimizing Logistics in E-Commerce Using Deep Random Forest for Enhanced User Satisfaction

Optimizing Logistics in E-Commerce Using Deep Random Forest ...

引用

Automation and Computation (AUTOCOM), International Conference on

作者： G.Anne Nisha Sreetha P Seetha A K. Manickam Sharath Kumar Jagannathan Sekar S D Department of Informatics Kyoto Computer Gakuin Kyoto Japan Department of Biomedical Engineering KPR Institute of Engineering and Technology Tamil Nadu India Department of Information Technology S. A. Engineering College Thiruverkadu Chennai India Department of Mathematics Sona College of Technology Tamil Nadu India Data Science Institute Saint Peter's University New Jersery Department of Mechanical Engineering R.M.K.Engineering College Chennai Tamilnadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Operational efficiency is one of the most important factors affecting customer satisfaction directly in e-commerce. Still, constant addressing of timely delivery, path simplification, and reduction of delays remains challenging problems. Deep Random Forest (DRF) is a new approach presented in this work to examine and forecast significant logistics metrics including delivery times and satisfaction scores. Development of this approach targeted to solve these issues. The model makes use of e-commerce past performance including order volumes, customer comments, and delivery times. Findings: The suggested approach achieves 10% increase in user satisfaction, 12% cost reduction, and 15% faster delivery times than baseline methods. This is accomplished by means of DRF, applied to examine trends and carry out predictive modifications. Results and Methodology: These results outperform those of the benchmarks established by Gradient Boosting and XGBoost, which displayed respective rises in satisfaction of 9% and 8%. Apart from improving the quality of service, the use of DRF not only strengthened user loyalty but also contributed to improve the transportation efficiency. This approach exposes the transforming opportunities of artificial intelligence in logistics management for e-commerce systems. This approach guarantees operations with a customer-centric concentration benefiting from measurable advantages.

关键词： Accuracy Costs Transportation Boosting Real-time systems Computational efficiency Electronic commerce Random forests Optimization Logistics

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Humans Continue to Outperform Large Language Models in Complex Clinical Decision-Making: A Study with Medical Calculators

arXiv

引用

arXiv 2024年

作者： Wan, Nicholas Jin, Qiao Chan, Joey Xiong, Guangzhi Applebaum, Serina Gilson, Aidan McMurry, Reid Andrew Taylor, R. Zhang, Aidong Chen, Qingyu Lu, Zhiyong BethesdaMD United States Department of Computer Science University of Virginia CharlottesvilleVA United States Department of Ophthalmology & Visual Science Yale School of Medicine New HavenCT United States Massachusetts Eye and Ear Harvard Medical School BostonMA United States Department of Emergency Medicine Boston Medical Center BostonMA United States Department of Biomedical Informatics & Data Science Yale School of Medicine New HavenCT United States Department of Emergency Medicine Yale School of Medicine New HavenCT United States

Although large language models (LLMs) have been assessed for general medical knowledge using medical licensing exams, their ability to effectively support clinical decision-making tasks, such as selecting and using medical calculators, remains uncertain. Here, we evaluate the capability of both medical trainees and LLMs to recommend medical calculators in response to various multiple-choice clinical scenarios such as risk stratification, prognosis, and disease diagnosis. We assessed eight LLMs, including open-source, proprietary, and domain-specific models, with 1,009 question-answer pairs across 35 clinical calculators and measured human performance on a subset of 100 questions. While the highest-performing LLM, GPT-4o, provided an answer accuracy of 74.3% (CI: 71.5-76.9%), human annotators, on average, outperformed LLMs with an accuracy of 79.5% (CI: 73.5-85.0%). With error analysis showing that the highest-performing LLMs continue to make mistakes in comprehension (56.6%) and calculator knowledge (8.1%), our findings emphasize that humans continue to surpass LLMs on complex clinical tasks such as calculator recommendation. © 2024, CC BY-SA.

关键词： Diagnosis

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