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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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BabyNet＋＋: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery

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Computers in Biology and Medicine 2023年 167卷 107602-107602页

作者： Plotka, Szymon Grzeszczyk, Michal K. Brawura-Biskupski-Samaha, Robert Gutaj, Pawel Lipa, Michal Trzciński, Tomasz Išgum, Ivana Sánchez, Clara I. Sitek, Arkadiusz Sano Centre for Computational Medicine Cracow Poland Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center Amsterdam Netherlands Second Department of Obstetrics and Gynecology The Medical Centre of Postgraduate Education Warsaw Poland Department of Reproduction Poznan University of Medical Sciences Poznan Poznan Poland First Department of Obstetrics and Gynecology Medical University of Warsaw Warsaw Poland Institute of Computer Science Warsaw University of Technology Warsaw Poland Department of Radiology and Nuclear Medicine Amsterdam University Medical Center location University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Accurate prediction of fetal weight at birth is essential for effective perinatal care, particularly in the context of antenatal management, which involves determining the timing and mode of delivery. The current standard of care involves performing a prenatal ultrasound 24 hours prior to delivery. However, this task presents challenges as it requires acquiring high-quality images, which becomes difficult during advanced pregnancy due to the lack of amniotic fluid. In this paper, we present a novel method that automatically predicts fetal birth weight by using fetal ultrasound video scans and clinical data. Our proposed method is based on a Transformer-based approach that combines a Residual Transformer Module with a Dynamic Affine Feature Map Transform. This method leverages tabular clinical data to evaluate 2D+t spatio-temporal features in fetal ultrasound video scans. Development and evaluation were carried out on a clinical set comprising 582 2D fetal ultrasound videos and clinical records of pregnancies from 194 patients performed less than 24 hours before delivery. Our results show that our method outperforms several state-of-the-art automatic methods and estimates fetal birth weight with an accuracy comparable to human experts. Hence, automatic measurements obtained by our method can reduce the risk of errors inherent in manual measurements. Observer studies suggest that our approach may be used as an aid for less experienced clinicians to predict fetal birth weight before delivery, optimizing perinatal care regardless of the available expertise. © 2023 The Author(s)

关键词： Forecasting

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A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds

Artificial Intelligence Chemistry

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Artificial Intelligence Chemistry 2024年第2期2卷

作者： Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono Ryo Maezono Study Program in Informatics Engineering Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa 923-1292 Japan

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R 2 ) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1) , methyl quinoxaline-6-carboxylate (Q2) , (2 E ,3 E )-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3) , (2 E ,3 E ) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4) , ( E )-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5) , and 2-(4-methoxyphenyl)-7-methylthieno[3,2- b ] quinoxaline (Q6) , show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

关键词： Quantum neural network QSPR Corrosion inhibition Quinoxaline

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Artificial Intelligence for science in Quantum, Atomistic, and Continuum Systems

arXiv

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arXiv 2023年

作者： Zhang, Xuan Wang, Limei Helwig, Jacob Luo, Youzhi Fu, Cong Xie, Yaochen Liu, Meng Lin, Yuchao Xu, Zhao Yan, Keqiang Adams, Keir Weiler, Maurice Li, Xiner Fu, Tianfan Wang, Yucheng Strasser, Alex Yu, Haiyang Xie, YuQing Fu, Xiang Xu, Shenglong Liu, Yi Du, Yuanqi Saxton, Alexandra Ling, Hongyi Lawrence, Hannah Stärk, Hannes Gui, Shurui Edwards, Carl Gao, Nicholas Ladera, Adriana Wu, Tailin Hofgard, Elyssa F. Tehrani, Aria Mansouri Wang, Rui Daigavane, Ameya Bohde, Montgomery Kurtin, Jerry Huang, Qian Phung, Tuong Xu, Minkai Joshi, Chaitanya K. Mathis, Simon V. Azizzadenesheli, Kamyar Fang, Ada Aspuru-Guzik, Alán Bekkers, Erik Bronstein, Michael Zitnik, Marinka Anandkumar, Anima Ermon, Stefano Liò, Pietro Yu, Rose Günnemann, Stephan Leskovec, Jure Ji, Heng Sun, Jimeng Barzilay, Regina Jaakkola, Tommi Coley, Connor W. Qian, Xiaoning Qian, Xiaofeng Smidt, Tess Ji, Shuiwang Department of Computer Science & Engineering Texas A&M University College StationTX United States Department of Chemical Engineering Massachusetts Institute of Technology Cambridge United Kingdom AMLab University of Amsterdam Amsterdam Netherlands Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United Kingdom Department of Materials Science & Engineering Texas A&M University College StationTX United States Department of Physics & Astronomy Texas A&M University College StationTX United States Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY United States Department of Computer Science Stony Brook University Stony BrookNY United States Department of Computer Science Cornell University IthacaNY United States Department of Computer Science Technical University of Munich München Germany Department of Computer Science Stanford University StanfordCA United States Department of Computer Science & Engineering University of California San Diego La Jolla CA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Nvidia Santa ClaraCA United States Department of Chemistry and Chemical Biology Harvard University Cambridge United Kingdom Department of Chemistry University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Department of Computer Science University of Oxford Oxford United Kingdom Department of Biomedical Informatics Harvard University BostonMA United States Department of Computing & Mathematical Sciences California Institute of Technology PasadenaCA United States Computational Science Initiative Brookhaven National Laboratory UptonNY United States

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4science). Being an emerging research paradigm, AI4science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4science;namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4science. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Local and global measures of the shear moduli of jammed disk packings

arXiv

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arXiv 2023年

作者： Zhang, Shiyun Jin, Weiwei Wang, Dong Xu, Ding Zhang, Jerry Shattuck, Mark D. O’Hern, Corey S. Department of Physics University of Science and Technology of China Anhui Hefei230026 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Strain-controlled isotropic compression gives rise to jammed packings of repulsive, frictionless disks with either positive or negative global shear moduli. We carry out computational studies to understand the contributions of the negative shear moduli to the mechanical response of jammed disk packings. We first decompose the ensemble-averaged, global shear modulus as hGi = (1 − F−)hG+i + F−hG−i, where F− is the fraction of jammed packings with negative shear moduli and hG+i and hG−i are the average values from packings with positive and negative moduli, respectively. We show that hG+i and h|G−|i obey different power-law scaling relations above and below pN2 ∼ 1. For pN2 > 1, both hG+iN and h|G−|iN ∼ (pN2)β, where β ∼ 0.5 for repulsive linear spring interactions. Despite this, hGiN ∼ (pN2)β0 with β0 & 0.5 due to the contributions from packings with negative shear moduli. We show further that the probability distribution of global shear moduli P(G) collapses at fixed pN2 and different values of p and N. We calculate analytically that P(G) is a Gamma distribution in the pN2 1 limit. As pN2 increases, the skewness of P(G) decreases and P(G) becomes a skew-normal distribution with negative skewness in the pN2 1 limit. We also partition jammed disk packings into subsystems using Delanunay triangulation of the disk centers to calculate local shear moduli. We show that the local shear moduli defined from groups of adjacent triangles can be negative even when G > 0. The spatial correlation function of local shear moduli C(~r) displays weak correlations for pn2sub −2, where nsub is the number of particles within each subsystem. However, C(~r) begins to develop long-ranged spatial correlations with four-fold angular symmetry for pn2sub & 10−2 © 2023, CC BY-NC-ND.

关键词： Elastic moduli

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING

arXiv

引用

arXiv 2023年

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2023, CC BY-NC-SA.

关键词： Maximum principle

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MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI

arXiv

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arXiv 2024年

作者： Newlin, Nancy R. Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Chen, Jian Yang, Joseph Yuan-Mou Wu, Ye He, Yifei Zhang, Jiawei Zeng, Qingrun Zhang, Fan Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Ewert, Christian Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett A. Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’s Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Pediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China College of Information Engineering Zhejiang University of Technology Hangzhou China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA NashvilleTN United States Learning Research and Development Center University of Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathematics Eindhoven University of Technology Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Germany Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. Specifically, there is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Harmonized submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences. A machine learning approach that learned voxel-wise cross-acquisition relationships was the most effective at harmonizing connectomic, microst

关键词： Diffusion tensor imaging

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High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH

引用

Cell 2025年第12期188卷 3310-3328.e27页

作者： Zhu, Yanyu Balaji, Ashwin Han, Mengting Andronov, Leonid Roy, Anish R. Wei, Zheng Chen, Crystal Miles, Leanne Cai, Sa Gu, Zhengxi Tse, Ariana Yu, Betty Chentzu Uenaka, Takeshi Lin, Xueqiu Spakowitz, Andrew J. Moerner, W.E. Qi, Lei S. Department of Bioengineering Stanford University Stanford 94305 CA United States Department of Chemistry Stanford University Stanford 94305 CA United States Biophysics PhD Program Stanford University Stanford 94305 CA United States Computational Biology Program Public Health Sciences Division and Translational Data Science IRC Fred Hutchinson Cancer Center Seattle 98109 WA United States Department of Chemical Engineering Stanford University Stanford 94305 CA United States Department of Materials Science and Engineering Stanford University Stanford 94305 CA United States Institute for Stem Cell Biology & Regenerative Medicine and Department of Pathology Stanford University School of Medicine Stanford 94305 CA United States Sarafan ChEM-H Stanford University Stanford 94305 CA United States Chan Zuckerberg Biohub San Francisco 94080 CA United States

Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or require complex genome engineering. Here, we present Oligo-LiveFISH, a high-resolution, reagent-based platform for dynamically tracking non-repetitive genomic loci in diverse cell types, including primary cells. Oligo-LiveFISH utilizes fluorescent guide RNA (gRNA) oligo pools generated by computational design, in vitro transcription, and chemical labeling, delivered as ribonucleoproteins. Utilizing machine learning, we characterized the impact of gRNA design and chromatin features on imaging efficiency. Multi-color Oligo-LiveFISH achieved 20-nm spatial resolution and 50-ms temporal resolution in 3D, capturing real-time enhancer and promoter dynamics. Our measurements and dynamic modeling revealed two distinct modes of chromatin communication, and active transcription slows enhancer-promoter dynamics at endogenous genes like FOS. Oligo-LiveFISH offers a versatile platform for studying 3D genome dynamics and their links to cellular processes and disease. © 2025 Elsevier Inc.

关键词： CRISPR imaging DNA communication dynamic tracking enhancer-promoter interaction high-resolution imaging live DNA imaging non-repetitive genome imaging Oligo-LiveFISH polymer modeling primary cell

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ProHap Explorer: Visualizing Haplotypes in Proteogenomic Datasets

arXiv

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arXiv 2025年

作者： Vašíček, Jakub Skiadopoulou, Dafni Kuznetsova, Ksenia G. Käll, Lukas Vaudel, Marc Bruckner, Stefan Mohn Center for Diabetes Precision Medicine Department of Clinical Science University of Bergen Bergen Norway Computational Biology Unit Department of Informatics University of Bergen Bergen Norway Metabolism Program Broad Institute of MIT and Harvard CambridgeMA United States Science for Life Laboratory Department of Gene Technology KTH - Royal Institute of Technology Stockholm Sweden Department of Genetics and Bioinformatics Health Data and Digitalization Norwegian Institute of Public Health Oslo Norway Institute for Visual and Analytic Computing University of Rostock Rostock Germany

In mass spectrometry-based proteomics, experts usually project data onto a single set of reference sequences, overlooking the influence of common haplotypes (combinations of genetic variants inherited together from a parent). We recently introduced ProHap, a tool for generating customized protein haplotype databases. Here, we present ProHap Explorer, a visualization interface designed to investigate the influence of common haplotypes on the human proteome. It enables users to explore haplotypes, their effects on protein sequences, and the identification of non-canonical peptides in public mass spectrometry datasets. The design builds on well-established representations in biological sequence analysis, ensuring familiarity for domain experts while integrating novel interactive elements tailored to proteogenomic data exploration. User interviews with proteomics experts confirmed the tool's utility, highlighting its ability to reveal whether haplotypes affect proteins of interest. By facilitating the intuitive exploration of proteogenomic variation, ProHap Explorer supports research in personalized medicine and the development of targeted therapies. © 2025, CC BY.

关键词： Proteomics

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MPCC: Matching priors and conditionals for clustering

arXiv

引用

arXiv 2020年

作者： Astorga, Nicolás Huijse, Pablo Protopapas, Pavlos Estvez, Pablo Department of Electrical Engineering Universidad de Chile Chile Informatics Institute Faculty of Engineering Sciences Universidad Austral de Chile Chile Institute for Applied Computational Science Harvard University United States Millennium Institute of Astrophysics Chile

Clustering is a fundamental task in unsupervised learning that depends heavily on the data representation that is used. Deep generative models have appeared as a promising tool to learn informative low-dimensional data representations. We propose Matching Priors and Conditionals for Clustering (MPCC), a GAN-based model with an encoder to infer latent variables and cluster categories from data, and a flexible decoder to generate samples from a conditional latent space. With MPCC we demonstrate that a deep generative model can be competitive/superior against discriminative methods in clustering tasks surpassing the state of the art over a diverse set of benchmark datasets. Our experiments show that adding a learnable prior and augmenting the number of encoder updates improve the quality of the generated samples, obtaining an inception score of 9.49 ± 0.15 and improving the Fréchet inception distance over the state of the art by a 46.9% in CIFAR10. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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