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arXiv 2023年

作者： Leone, Sam Sun, Xingzhi Perlmutter, Michael Krishnaswamy, Smita Program for Applied Mathematics Yale University United States Department of Computer Science Yale University United States Department of Mathematics Boise State University United States Department of Genetics Yale School of Medicine United States Wu Tsai Institute Yale University United States FAIR Meta AI United States Computational Biology and Bioinformatics Program Yale University United States

Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is very high-dimensional, but its structure can be captured via an affinity graph. This allows us to utilize ideas from graph signal processing. In particular, we present algorithms for the cases where the signal is perturbed by Gaussian noise, dropout, and uniformly distributed noise. The signals are assumed to follow a prior distribution defined in the frequency domain which favors signals which are smooth across the edges of the graph. By pairing this prior distribution with our three models of noise generation, we propose Maximum A Posteriori (M.A.P.) estimates of the true signal in the presence of noisy data and provide algorithms for computing the M.A.P. Finally, we demonstrate the algorithms' ability to effectively restore signals from white noise on image data and from severe dropout in single-cell RNA sequence data. © 2023, CC BY.

关键词： Frequency domain analysis

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Characterizing the mechanical response of metallic glasses to uniaxial tension using a spring network model

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Physical Review Materials 2023年第7期7卷 073605-073605页

作者： Aya Nawano Weiwei Jin Jan Schroers Mark D. Shattuck Corey S. O'Hern Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

A coarse-grained spring network model is proposed for the prediction of the mechanical response of metallic glasses as a function of the microstructure prior to loading. This model describes the mechanical response of metallic glasses using a network of parallel springs that can break and reform, mimicking atomic rearrangements during deformation. We compare predictions of the spring network model for stress versus strain to results from numerical simulations of athermal quasistatic, uniaxial tensile deformation of Cu50Zr50 metallic glasses using Lennard-Jones (LJ) and embedded atom method (EAM) atomic interactions. We show that both the LJ and EAM models possess qualitatively similar stress σ versus strain γ curves. By specifying five parameters [ultimate strength, strain at ultimate strength, slopes of σ(γ) at γ=0 and at large strain, and strain at fracture where σ=0], we demonstrate that the spring network model can accurately describe the form of the stress-strain curves during uniaxial tension for the computational studies of Cu50Zr50, as well as recent experimental studies of several Zr-based metallic glasses.

关键词： Alloys Amorphous materials Glasses Packing & jamming problems Molecular dynamics

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Correlations between associated SNPs cause upward bias in whole-genome based heritability estimation

Correlations between associated SNPs cause upward bias in wh...

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The Global Conference of Chinese Geneticists(2012全球华人遗传学大会)

作者： Cong Li Can Yang John Ferguson Judy Cho Hongyu Zhao IBD Center Division of GastroenterologyDepartment of MedicineYale UniversityNew HavenCT06520 Program in Computational Biology and Bioinformatics Yale UniversityNew HavenCT06520 Department of BiostatisticsYale School of Public HealthNew HavenCT06520 Department of GeneticsYale UniversityNew HavenCT06520

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Learning binary latent variable models: A tensor eigenpair approach

arXiv

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arXiv 2018年

作者： Jaffe, Ariel Weiss, Roi Carmi, Shai Kluger, Yuval Nadler, Boaz Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Braun School of Public Health and Community Medicine Hebrew University of Jerusalem Jerusalem9112102 Israel Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Dept. of Pathology Yale Cancer Center Yale University School of Medicine New HavenCT06520 United States Program of Applied Mathematics Yale University New HavenCT06511 United States

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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A FLOW ARTIST FOR HIGH-DIMENSIONAL CELLULAR DATA

arXiv

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arXiv 2023年

作者： MacDonald, Kincaid Bhaskar, Dhananjay Thampakkul, Guy Nguyen, Nhi Zhang, Joia Perlmutter, Michael Adelstein, Ian Krishnaswamy, Smita Department of Mathematics Yale University United States Department of Genetics Yale School of Medicine United States Department of Computer Science Yale University United States Department of Mathematics Pomona College United States Applied Mathematics Program Yale University United States Department of Statistics University of Washington United States Department of Mathematics Boise State University United States Computational Biology and Bioinformatics Program Yale University United States

We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured, including in high-throughput biology such as single-cell transcriptomics. Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i.e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field. Here we present FlowArtist, a neural network that embeds points while jointly learning a vector field around the points. The combination allows FlowArtist to better separate and visualize velocity-informed structures. Our results, on toy datasets and single-cell RNA velocity data, illustrate the value of utilizing coordinate and velocity information in tandem for embedding and visualizing high-dimensional data. © 2023, CC BY.

关键词： RNA

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ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins

arXiv

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arXiv 2022年

作者： Castro, Egbert Godavarthi, Abhinav Rubinfien, Julian Givechian, Kevin Bhaskar, Dhananjay Krishnaswamy, Smita Computational Biology and Bioinformatics Program Yale University New HavenCT06511 United States Department of Applied Mathematics Yale University New HavenCT06511 United States Department of Genetics Yale University New HavenCT06511 United States Department of Computer Science Yale University New HavenCT06511 United States

The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mutagenesis, directed evolution, and next-generation sequencing have allowed for the accumulation of large amounts of labeled fitness data. Leveraging these two trends, we introduce Regularized Latent Space Optimization (ReLSO), a deep transformer-based autoencoder which features a highly structured latent space that is trained to jointly generate sequences as well as predict fitness. Through regularized prediction heads, ReLSO introduces a powerful protein sequence encoder and novel approach for efficient fitness landscape traversal. Using ReLSO, we explicitly model the sequence-function landscape of large labeled datasets and generate new molecules by optimizing within the latent space using gradient-based methods. We evaluate this approach on several publicly-available protein datasets, including variant sets of anti-ranibizumab and GFP. We observe a greater sequence optimization efficiency (increase in fitness per optimization step) by ReLSO compared to other approaches, where ReLSO more robustly generates high-fitness sequences. Furthermore, the attention-based relationships learned by the jointly-trained ReLSO models provides a potential avenue towards sequence-level fitness attribution information. © 2022, CC BY-NC-ND.

关键词： Proteins

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Bridging the collaboration gap: Real-time identification of clinical specimens for biomedical research

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Journal of Pathology Informatics 2020年第1期11卷 1-7页

作者： Durant, Thomas J.S. Gong, Guannan Price, Nathan Schulz, Wade Department of Laboratory Medicine Yale University School of Medicine New Haven CT United States Center for Outcomes Research and Evaluation Yale New Haven Hospital New Haven CT United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University School of Medicine New Haven CT United States Department of Information Technology Yale New Haven Health New Haven CT United States

Introduction: Biomedical and translational research often relies on the evaluation of patients or specimens that meet specific clinical or laboratory criteria. The typical approach used to identify biospecimens is a manual, retrospective process that exists outside the clinical workflow. This often makes biospecimen collection cost prohibitive and prevents the collection of analytes with short stability times. Emerging data architectures offer novel approaches to enhance specimen-identification practices. To this end, we present a new tool that can be deployed in a real-time environment to automate the identification and notification of available biospecimens for biomedical research. Methods: Real-time clinical and laboratory data from Cloverleaf (Infor, NY, NY) were acquired within our computational health platform, which is built on open-source applications. Study-specific filters were developed in NiFi (Apache Software Foundation, Wakefield, MA, USA) to identify the study-appropriate specimens in real time. Specimen metadata were stored in Elasticsearch (Elastic N. V., Mountain View, CA, USA) for visualization and automated alerting. Results: Between June 2018 and December 2018, we identified 2992 unique specimens belonging to 2815 unique patients, split between two different use cases. Based on laboratory policy for specimen retention and study-specific stability requirements, secure E-mail notifications were sent to investigators to automatically notify of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2000 results per second. Conclusion: This work demonstrates that real-world clinical data can be analyzed in real time to increase the efficiency of biospecimen identification with minimal overhead for the clinical laboratory. Future work will integrate additional data types, including the analysis of unstructured data, to enable more complex cases and biospecimen identification. © 2020 Journal

关键词： Biobanking biomedical research biospecimen science clinical specimens real-time identification translational research

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A software framework integrating gene expression patterns, binding site analysis and gene ontology to hypothesize gene regulation relationships

A software framework integrating gene expression patterns, b...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Pujan Joshi Baikang Pei Seung-Hyun Hong Ivo Kalajzic Dong-Ju Shin David Rowe Dong-Guk Shin Computer Science and Engineering University of Connecticut Storrs CT Computational Biology and Bioinformatics Yale University New Haven CT Genetics & Dev. Biology University of Connecticut Health Center Farmington CT Metabolic Signaling and Disease Program Sanford-Burnham Medical Research Institute Orlando FL

One known challenge in analyzing gene expression data is to combine analysis outcomes obtained disparately by applying multiple, independent meta-analysis methods. Here we present an integrative computational system that narrows down biological hypotheses by integrating gene expression patterns, transcription factor (TF) binding site analysis outcomes, and Gene Ontology (GO) enrichment analysis outcomes. This system identifies regulated genes from microarray experiments through statistical processes, categorizes similarly behaving groups of genes and then carries out binding site analysis and gene function enrichment analysis based on some significant clusters. The output is an ordered set of "putative" pair-wise relationships between TFs and their potential target genes. The relationships are ranked based on their closeness to the experimental context. We demonstrate the effectiveness of our framework using two independent microarray data sets.

关键词： Gene expression Context Sociology Statistics bioinformatics Bones

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General framework for the mechanical response of metallic glasses during strain-rate-dependent uniaxial compression

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Physical Review Materials 2023年第10期7卷 105604-105604页

作者： Weiwei Jin Amit Datye Udo D. Schwarz Mark D. Shattuck Corey S. O'Hern Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Department of Chemical and Environmental Engineering Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

Experimental data on compressive strength σmax versus strain rate ɛ̇eng for metallic glasses undergoing uniaxial compression show varying strain rate sensitivity. For some metallic glasses, σmax decreases with increasing ɛ̇eng, while for others, σmax increases with increasing ɛ̇eng, and for certain alloys σmax versus ɛ̇eng is nonmonotonic. To understand their strain rate sensitivity, we conduct molecular dynamics simulations of metallic glasses undergoing uniaxial compression at finite strain rates and coupled to heat baths with a range of temperatures T0 and damping parameters b. In the T0→0 and b→0 limits, we find that the compressive strength σmax versus temperature T obeys a “chevron-shaped” scaling relation. In the low-strain-rate regime, σmax decreases linearly with increasing T, whereas σmax grows as a power law with decreasing T in the high-strain-rate regime. For T0>0, σmax(T) deviates from the scaling curve at low strain rates, but σmax(T) rejoins the scaling curve as the strain rate increases. Enhanced dissipation reduces compression-induced heating, which causes σmax(T) to deviate from the b→0 scaling behavior for intermediate strain rates, but σmax(T) converges to the high-strain-rate power-law scaling behavior at sufficiently high strain rates. Determining σmax(T) as a function of b and T0 provides a general framework for explaining the strain rate sensitivity of metallic glasses under compression.

关键词： Compressive strength Mechanical deformation Amorphous materials Metallic glasses Molecular dynamics

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Hypostatic jammed packings of frictionless nonspherical particles

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Physical Review E 2018年第1期97卷 012909-012909页

作者： Kyle VanderWerf Weiwei Jin Mark D. Shattuck Corey S. O'Hern Department of Physics Yale University New Haven Connecticut 06520 USA Department of Mechanics and Engineering Science Peking University Beijing 100871 China Department of Mechanical Engineering & Materials Science Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

We perform computational studies of static packings of a variety of nonspherical particles including circulo-lines, circulo-polygons, ellipses, asymmetric dimers, dumbbells, and others to determine which shapes form packings with fewer contacts than degrees of freedom (hypostatic packings) and which have equal numbers of contacts and degrees of freedom (isostatic packings), and to understand why hypostatic packings of nonspherical particles can be mechanically stable despite having fewer contacts than that predicted from naive constraint counting. To generate highly accurate force- and torque-balanced packings of circulo-lines and cir-polygons, we developed an interparticle potential that gives continuous forces and torques as a function of the particle coordinates. We show that the packing fraction and coordination number at jamming onset obey a masterlike form for all of the nonspherical particle packings we studied when plotted versus the particle asphericity A, which is proportional to the ratio of the squared perimeter to the area of the particle. Further, the eigenvalue spectra of the dynamical matrix for packings of different particle shapes collapse when plotted at the same A. For hypostatic packings of nonspherical particles, we verify that the number of “quartic” modes along which the potential energy increases as the fourth power of the perturbation amplitude matches the number of missing contacts relative to the isostatic value. We show that the fourth derivatives of the total potential energy in the directions of the quartic modes remain nonzero as the pressure of the packings is decreased to zero. In addition, we calculate the principal curvatures of the inequality constraints for each contact in circulo-line packings and identify specific types of contacts with inequality constraints that possess convex curvature. These contacts can constrain multiple degrees of freedom and allow hypostatic packings of nonspherical particles to be mechanically stable.

关键词： Granular materials Granular packing Jamming

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