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Single Cell Investigative Genetics: Single-Cell Data Produces Genotype Distributions Concentrated at the True Genotype Across All Mixture Complexities

SSRN

引用

SSRN 2023年

作者： Grgicak, Catherine M. Bhembe, Qhawe Sheth, Nidhi C. Duffy, Ken R. Lun, Desmond S. Department of Chemistry Rutgers University CamdenNJ08102 United States Center for Computational and Integrative Biology Rutgers University CamdenNJ08102 United States Program in Biomedical Forensic Sciences Boston University BostonMA02118 United States Department of Mathematics Northeastern University BostonMA02115 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Hamilton Institute Maynooth University Ireland Department of Computer Science Rutgers University CamdenNJ08102 United States

In the absence of a suspect or PoI the forensic aim is investigative, and the focus is one of discerning what candidate genotypes best explain the evidence. In traditional bulk systems, the list of candidate genotypes may become vast if the sample contains DNA from many donors or the information from a minor contributor is swamped by that of major contributors, leading to lower evidential value for a true donor’s contribution and, as a result, possibly overlooked or inefficient investigative *** developments in single-cell analysis offer a way forward, by producing data capable of discriminating between genotypes. This is accomplished by first clustering single-cell data by similarity without reference to a known genotype. With good clustering it is reasonable to assume that the scEPGs in a cluster are of a single contributor. With that assumption we determine the probability of a cluster’s content given each possible genotype at each locus, which is then used to determine the posterior probability mass distribution for all genotypes by application of Bayes’ rule. A decision criterion is then applied such that the sum of the ranked probabilities of all genotypes falling in the set is at least . This is the credible genotype set and is the one used to inform database search criteria and, thus, investigative *** demonstrate the salience of single cell analysis by performance testing a set of 630 previously constructed admixtures containing up to 5 donors of balanced and unbalanced contributions. We use scEPGs that were generated by isolating single cells, employing a direct-to-PCR extraction treatment, amplifying STRs that are compliant with existing national databases and applying post-PCR treatments that elicit a detection limit of one DNA copy. We determined that, for these test data, 99.3% of the true genotypes are included in the 99.8% credible set, regardless of the number of donors that comprised the mixture. We also determined that the most proba

关键词： DNA

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A General-Purpose Self-Supervised Model for computational Pathology

arXiv

引用

arXiv 2023年

作者： Chen, Richard J. Ding, Tong Lu, Ming Y. Williamson, Drew F.K. Jaume, Guillaume Chen, Bowen Zhang, Andrew Shao, Daniel Song, Andrew H. Shaban, Muhammad Williams, Mane Vaidya, Anurag Sahai, Sharifa Oldenburg, Lukas Weishaupt, Luca L. Wang, Judy J. Williams, Walt Le, Long Phi Gerber, Georg Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Systems Biology Harvard University CambridgeMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

arXiv

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arXiv 2023年

作者： Song, Andrew H. Williams, Mane Williamson, Drew F.K. Jaume, Guillaume Zhang, Andrew Chen, Bowen Serafin, Robert Liu, Jonathan T.C. Baras, Alex Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Department of Pathology The Ohio State University ColumbusOH United States

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3–6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7–18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12–14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, M

关键词： Pathology

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Fluorescent imaging technologies for in situ measurement of drug target engagement and cell signaling pathways 4

Fluorescent imaging technologies for in situ measurement of ...

引用

Visualizing and Quantifying Drug Distribution in Tissue IV 2020

作者： McMahon, Nathan P. Solanki, Allison Jones, Jocelyn Kwon, Sunjong Chang, Young-Hwan Chin, Koei Nederlof, Michel A. Gray, Joe W. Gibbs, Summer L. Department of Biomedical Engineering Oregon Health and Science University PortlandOR97201 United States Knight Cancer Institute Oregon Health and Science University PortlandOR97201 United States Center for Spatial Systems Biomedicine Oregon Health and Science University PortlandOR97201 United States Computational Biology Program Oregon Health and Science University PortlandOR97201 United States Quantitative Imaging Systems PittsburghPA15238 United States

ISBN: (纸本)9781510632011

Successful cancer treatment continues to elude modern medicine and its arsenal of therapeutic strategies. Therapy resistance is driven by significant tumor heterogeneity, complex interactions between malignant, microenvironmental and immune cells and cross talk between signaling pathways. Advances in molecular characterization technologies such as next generation sequencing have helped unravel this network of interactions and identify druggable therapeutic targets. Tyrosine kinase inhibitors (TKI) are a class of drugs seeking to inhibit signaling pathways critical to sustaining proliferative signaling, resisting cell death, and the other hallmarks of cancer. While tumors may initially respond to TKI therapy, disease progression is near universal due to mechanisms of acquired resistance largely involving cellular signaling pathway reprogramming. With the ultimate goal of improved TKI therapeutic efficacy our group has developed intracellular paired agent imaging (iPAI) to quantify drug target interactions and oligonucleotide conjugated antibody (Ab-oligo) cyclic immunofluorescence (cycIF) imaging to characterize perturbed signaling pathways in response to therapy. iPAI uses spectrally distinct, fluorescently labeled targeted and untargeted drug derivatives, correcting for non-specific drug distribution and facilitating quantitative assessment of the drug binding before and after therapy. Ab-oligo cycIF exploits in situ hybridization of complementary oligonucleotides for biomarker labeling while oligonucleotide modifications facilitate signal removal for sequential rounds of fluorescent tagging and imaging. Aboligo CycIF is capable of generating extreme multi-parametric images for quantifying total and phosphorylated protein expression to quantify protein activation, expression, and spatial distribution. Together iPAI and Ab-oligo cycIF can be applied to interrogate drug uptake and target binding as well as changes to heterogenous cell populations within tumors that d

关键词： Fluorescence imaging

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A Foundational Multimodal Vision Language AI Assistant for Human Pathology

arXiv

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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Ikamura, Kenji Gerber, Georg Liang, Ivy Phi Le, Long Ding, Tong Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The field of computational pathology has witnessed remarkable progress in the development of both taskspecific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intelligence (AI), there has been limited study on building general purpose, multimodal AI assistants tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology using an in-house developed foundational vision encoder pretrained on 100 million histology images from over 100,000 patient cases and 1.18 million pathology image-caption pairs. The vision encoder is then combined with a pretrained large language model and the whole system is finetuned on over 250,000 diverse disease agnostic visual language instructions. We compare PathChat against several multimodal vision language AI assistants as well as GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4. When relevant clinical context is provided with the histology image, PathChat achieved a diagnostic accuracy of 87% on multiple-choice questions based on publicly available cases of diverse tissue origins and disease models. Additionally, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive and general vision language AI assistant that can flexibly handle both visual and natural language inputs, PathChat can potentially find impactful applications in pathology education, research, and human-in-the-loop clinical decision making. © 2023, CC BY-NC-ND.

关键词： Pathology

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Continuous black-box optimization with an Ising machine and random subspace coding

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Physical Review Research 2022年第2期4卷 023062-023062页

作者： Syun Izawa Koki Kitai Shu Tanaka Ryo Tamura Koji Tsuda Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa 277-8561 Japan Department of Mechanical Engineering The University of Tokyo Tokyo 113-8654 Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama 223-8522 Japan Green Computing System Research Organization Waseda University Tokyo 162-0042 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0047 Japan RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan

A black-box optimization algorithm such as Bayesian optimization finds the extremum of an unknown function by alternating the inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply Ising machines to overcome the difficulty in the continuous black-box optimization. As an Ising machine specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution by Ising machines has to be translated back. Our method has the following three parts: (1) random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector, (2) a quadratic unconstrained binary optimization (QUBO) defined by the acquisition function based on the nonnegative-weighted linear regression model, which is solved by Ising machines, and (3) a penalization scheme to ensure that the solution can be translated back. It is shown with benchmark tests that its performance using the D-Wave Advantage quantum annealer and simulated annealing is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up the possibility of Ising machines and other QUBO solvers, including a quantum approximate optimization algorithm using gated-quantum computers, and may expand its range of application to continuous-valued problems.

关键词： Adiabatic quantum optimization Machine learning Quantum algorithms Quantum computation

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computational fluid dynamics applied to the study of falling liquid films in a wave film generation facility 6

Computational fluid dynamics applied to the study of falling...

引用

6th ECCOMAS European Conference on computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th ECCOMAS European Conference on computational Fluid Dynamics, ECFD 2018

作者： Iglesias, Susana M. Dominguez, Dany S. Escrivá, Alberto Muñoz-Cobo, José L. Berna, César Cuadros, José L. Rivera, Yago Universidade Estadual de Santa Cruz Postgraduate Program of Computational Modeling in Science and Technology Rodovia Jorge Amado km 16 Salobrinho Ilhéus BA45662-900 Brazil Universitat Politècnica de València Institute for Energy Engineering Camino de Vera 14 Valencia46022 Spain

ISBN: (纸本)9788494731167

Two phase gas-liquid flow appears in several engineering applications, such as boiling nuclear reactors, oil and gas industry, chemical plants, among others. For this phenomenon's study the experimental facility Wave Film Generator (GEPELON, Generador de Película Ondulatoria) was constructed. It uses conductivity sensors for the multi-phase flow analysis in vertical pipes. In this work, we propose the use of computational Fluid Dynamics for modelling the falling liquid films regime in this facility. Specifically, this air-water flow regime consists of a water film that falls down the pipe wall and a central region with stagnant air. We build up a simplified installation's geometrical model, considering a mass flow boundary condition in the inlet and constant pressure in the outlet. To capture the water-air interface a non-uniform structured mesh with fine elements close to the wall was constructed. Using a Volume of Fluid (VOF) model and the FLUENT solver, we simulate different flow rates and from the water volumetric fraction we estimate the film thickness in all the domain. The simulations are stationary and due to the hydrodynamic complexity, a pseudo-transient approach was used to stabilize the iterative process. The simulation values were compared with the experimental results obtained in the GEPELON facility. A mesh independence analysis was performed and several mass flows were simulated, showed good agreement with the experimental values for lower flows and reasonable for higher flows. copyright © Crown copyright (2018).All right reserved.

关键词： computational fluid dynamics

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Assessment of scoring functions for computational models of protein-protein interfaces

arXiv

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arXiv 2024年

作者： Sumner, Jacob Meng, Grace Brandt, Naomi Grigas, Alex T. Córdoba, Andrés Shattuck, Mark D. O’Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Chemistry Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States Benjamin Levich Institute Physics Department City College of New York New YorkNY10031 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

A goal of computational studies of protein-protein interfaces (PPIs) is to predict the binding site between two monomers that form a heterodimer. The simplest version of this problem is to rigidly re-dock the bound forms of the monomers, which involves generating computational models of the heterodimer and then scoring them to determine the most native-like models. Scoring functions have been assessed previously using rank- and classification-based metrics, however, these methods are sensitive to the number and quality of models in the scoring function training set. We assess the accuracy of seven PPI scoring functions by comparing their scores to a measure of structural similarity to the x-ray crystal structure (i.e. the DockQ score) for a non-redundant set of heterodimers from the Protein Data Bank. For each heterodimer, we generate re-docked models uniformly sampled over DockQ and calculate the Spearman correlation between the PPI scores and DockQ. For some targets, the scores and DockQ are highly correlated;however, for many targets, there are weak correlations. Several physical features can explain the difference between difficult- and easy-to-score targets. For example, strong correlations exist between the score and DockQ for targets with highly intertwined monomers and many interface contacts. We also develop a new score based on only three physical features that matches or exceeds the performance of current PPI scoring functions. These results emphasize that PPI prediction can be improved by focusing on correlations between the PPI score and DockQ and incorporating more discriminating physical features into PPI scoring functions. © 2024, CC BY.

关键词： Monomers

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A machine learning-based high-precision density functional method for drug-like molecules

Artificial Intelligence Chemistry

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Artificial Intelligence Chemistry 2024年第1期2卷

作者： Jin Xiao YiXiao Chen LinFeng Zhang Han Wang Tong Zhu Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development Shanghai Frontiers Science Center of Molecule Intelligent Syntheses School of Chemistry and Molecular Engineering East China Normal University Shanghai 200062 P.R. China Program in Applied and Computational Mathematics Princeton University Princeton NJ 08544 USA AI for Science Institute Beijing 100080 P.R. China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 P.R. China NYU-ECNU Center for Computational Chemistry at NYU Shanghai Shanghai 200062 P.R. China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518005 P.R. China

In computer-aided drug discovery, accurately determining the structure and properties of drug-like molecules is of utmost importance. This necessitates the use of precise and efficient electronic structure methods. Here, we developed two deep learning-based density functional methods, namely DeePHF and DeePKS, specifically tailored for drug-like molecules. Notably, DeePKS incorporates self-consistency into its framework. With a limited dataset labelled at the CCSD(T)/def2-TZVP level, both models have been able to achieve chemical accuracy in calculating molecular energies and have demonstrated excellent transferability. We anticipate that further advancements in this field will lead to the development of high-quality density functional methods designed specifically for drug discovery purposes. This research showcases the capabilities of deep learning approaches in simplifying the construction complexity associated with traditional DFT methods.

关键词： Machine Learning Density Functional Theory DeePHF DeePKS

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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