Realistic evolutionary fitness landscapes are notoriously difficult to construct. A recent cutting-edge model of virus assembly consists of a dodecahedral capsid with corresponding packaging signals in three affinity bands. This whole genome/phenotype space consisting of genomes has been explored via comp…
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48 results for “genome assembly”
In this paper, we propose a semi-supervised deep learning method for detecting the specific types of reads that impede the de novo genome assembly process. Instead of dealing directly with sequenced reads, we analyze their coverage graphs converted to 1D-signals. We noticed that specific signal patterns occur in each r…
Recent advances in high-throughput cDNA sequencing (RNA-Seq) technology have revolutionized transcriptome studies. A major motivation for RNA-Seq is to map the structure of expressed transcripts at nucleotide resolution. With accurate computational tools for transcript reconstruction, this technology may also become us…
Common complex diseases are likely influenced by the interplay of hundreds, or even thousands, of genetic variants. Converging evidence shows that genetic variants with low marginal effects (LME) play an important role in disease development. Despite their potential significance, discovering LME genetic variants and as…
The paper studies recovering hidden nearest neighbor graphs in large networks.
problem Discovering strong ties in social networks and assembling genome subsequences.
method Maximum likelihood estimator for recovering hidden -nearest neighbor graphs.
result The maximum likelihood estimator achieves asymptotic recovery guarantees under specific conditions.
We introduce the problem of hidden Hamiltonian cycle recovery, where there is an unknown Hamiltonian cycle in an -vertex complete graph that needs to be inferred from noisy edge measurements. The measurements are independent and distributed according to $\calP_n$ for edges in the cycle and $\calQ_n$ otherwise. This …
Coarse assembly mapsmath.KT
For every strong coarse homology theory we construct a coarse assembly map as a natural transformation between coarse homology theories. We provide various conditions implying that this assembly map is an equivalence. These results generalize known results for the analytic coarse assembly map for K-homology to general …
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assemblystat.ML
Model predicts composite structures assembly quality with input uncertainty.
problem Accurate prediction of dimensional deviations and residual stress in composite structures assembly.
method Neural Network Gaussian Process considering input uncertainty.
result NNGPIU model outperforms other methods for nonsmooth, nonlinear responses.
Survey on metrics and assembly maps in positive scalar curvature.
problem Positive Scalar Curvature metrics and their relation to assembly maps.
method Gromov-Lawson index and Baum-Connes assembly map.
result Survey of results connecting metrics and assembly maps.
If a Lie algebra structures $\gG$ on a vector space is the sum of a family of mutually compatible Lie algebra structures $\gG_i$, we say that $\gG$ is \emph{simply assembled} from $\gG_s$'s. By repeating this procedure several times one gets a family of Lie algebras \emph{assembled} from $\gG_s$'s. The central result o…
We construct the geometric Baum-Connes assembly map for twisted Lie groupoids, that means for Lie groupoids together with a given groupoid equivariant principle bundle. The construction is based on the use of geometric deformation groupoids, these objects allow in particular to give a geometric construction of …
We study in this paper the maximal version of the coarse Baum-Connes assembly map for families of expanding graphs arising from residually finite groups. Unlike for the usual Roe algebra, we show that this assembly map is closely related to the (maximal) Baum-Connes assembly map for the group and is an isomorphism for …
Controlled -theory is used to show that algebraic -theory of virtually abelian groups is described by an assembly map defined using possibly-infinite hyperelementary subgroups. The Farrell-Jones summand (coming from infinite subgroups) is parameterized by the rational projective space of the group, and a reduced …
If a Lie algebra structure g on a vector space is the sum of a family of mutually compatible Lie algebra structures g_i's, we say that g is simply assembled from the g_i's. Repeating this procedure with a number of Lie algebras, themselves simply assembled from the g_i's, one obtains a Lie algebra assembled in two step…
We use assembly maps to study , the topological cyclic homology at a prime of the group algebra of a discrete group with coefficients in a connective ring spectrum . For any finite group, we prove that the assembly map for the family of cyclic subgroups is an isomorphis…
A framework for generating 3D shapes by sequentially assembling primitives.
problem Combinatorial complexity in generating 3D shapes.
method Bayesian optimization for efficient exploration and exploitation of feasible combinations.
result Successfully generates realistic combinatorial 3D shapes.
In this article, we introduce the notion of a functor on coarse spaces being coarsely excisive- a coarse analogue of the notion of a functor on topological spaces being excisive. Further, taking cones, a coarsely excisive functor yields a topologically excisive functor, and for coarse topological spaces there is an ass…
Defines and computes geometric pairings for discrete groups using Baum-Connes assembly map.
problem Defining and computing geometric pairings for discrete countable groups.
method Constructs explicit morphisms and the Chern-Baum-Connes assembly map.
result Explicit formulation of a Chern-Connes pairing with the periodic cyclic cohomology of the group algebra.
Application of Markov Structure of Genomes to Outlier Identification and Read Classificationq-bio.GN
Paper uses genome Markov structure for outlier detection and read classification.
problem Identifying outliers and classifying reads in genome databases.
method Applying second-order Markov models to triplet base distributions.
result Improved accuracy in outlier identification and read classification.
Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. As such, architectures that fit the structure of genomics should …
Model for assembly map of bordism-invariant functors.
problem Understanding assembly maps of bordism-invariant functors.
method Categorical model using oplax colimits of stable, hermitian, and Poincaré categories.
result Explicit description of the kernel of the assembly map.
We analyze linear independence of rank one tensors produced by tensor powers of randomly perturbed vectors. This enables efficient decomposition of sums of high-order tensors. Our analysis builds upon [BCMV14] but allows for a wider range of perturbation models, including discrete ones. We give an application to recove…
We construct a higher Whitehead torsion map, using algebraic K-theory of spaces, and show that it satisfies the usual properties of the classical Whitehead torsion. This is used to describe a "geometric assembly map" defined on stabilized structure spaces in purely homotopy theoretic terms.
Model detects patterns in noisy binary data, explaining neuron activity in terms of cell assemblies.
problem Detecting structure in noisy or approximate repeats of patterns in sparse binary data.
method Probabilistic binary latent variable model based on Noisy-OR model, inferring sparse activity in latent variables.
result Model successfully extracts and explains latent structure in spiking neural data.
Build Boy's Surfacemath.HO
Easy-to-assemble 3D model of Boy's surface.
problem Visualizing Boy's surface in 3D.
method Printable card stock model kit.
result Accessible 3D representation of Boy's surface.
In this paper we introduce a homotopy theoretic technique for proving that the -theoretic assembly map is an equivalence. It is an extension of the methods used to prove split injectivity of the assembly and applies to any geometrically finite group. Our result is that there are two requirements which need to hold. …
Elastic co-clustering improves clustering of single-cell genomic data.
problem Improving clustering performance of single-cell genomic datasets.
method Elastic coupled co-clustering in an unsupervised transfer learning framework.
result Our algorithm significantly improves clustering performance over traditional methods.
Bayesian calibration for BCP self-assembly models using image data and measure transport.
problem Calibrating models of BCP self-assembly from image data with aleatory uncertainty.
method Likelihood-free inference via measure transport and summary statistics.
result Expected information gains can be computed efficiently for model calibration.
Genomic models learn DNA sequences to predict functions.
problem Understanding complex genetic interactions.
method Training LLMs on DNA sequences to predict functions.
result gLMs can predict functions of DNA elements.
The paper predicts diseases using both clinical and genomics data.
problem Clinical predictions using genomics data are not common.
method Integrated clinical and genomics datasets, machine learning, Principal Component Analysis for feature selection.
result 73% accuracy in predicting 75 disease classes.
Between the category of exact metric spaces with bounded geometry (about which much is known) and the larger category of arbitrary exact metric spaces (about which little is known) lies the intermediate category of asymptotically exact metric spaces. We show that the coarse Baum-Connes assembly map is naturally split s…
New method combines ensembling and regularization for genomic disease prediction.
problem Genomic diseases require accurate prediction and biomarker identification.
method Integrates regularization with ensembling techniques for high-dimensional binary classification.
result Identifies critical biomarkers overlooked by competing methods.
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies. We address the problem of learning interpretable models for discrete phenotypes from whole genomes. We propose a general approach that relies on the Set Covering Machine and a k-mer representation of the genomes. We sho…
Random Fragments Classification of Microbial Marker Clades with Multi-class SVM and N-Best Algorithmq-bio.QM
Microbial clades modeling is a challenging problem in biology based on microarray genome sequences, especially in new species gene isolates discovery and category. Marker family genome sequences play important roles in describing specific microbial clades within species, a framework of support vector machine (SVM) base…
Dr.S recommends cancer drugs based on genomic data.
problem Personalizing cancer treatments using genomic information.
method Machine learning to identify optimal drug-gene associations.
result Developed a Drug Recommendation System (Dr.S) for cancer cell lines.
With different genomes available, unsupervised learning algorithms are essential in learning genome-wide biological insights. Especially, the functional characterization of different genomes is essential for us to understand lives. In this book chapter, we review the state-of-the-art unsupervised learning algorithms fo…
Deep neural networks (DNNs) are known for extracting useful information from large amounts of data. However, the representations learned in DNNs are typically hard to interpret, especially in dense layers. One crucial issue of the classical DNN model such as multilayer perceptron (MLP) is that neurons in the same layer…
In this paper, the first of a series of two, we continue the study of higher index theory for expanders. We prove that if a sequence of graphs is an expander and the girth of the graphs tends to infinity, then the coarse Baum-Connes assembly map is injective, but not surjective, for the associated metric space . Exp…
One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging multi-label classification task. There are two major biological mechan…
This paper provides a full controlled version of algebraic -theory. This includes a rich array of assembly maps; the controlled assembly isomorphism theorem identifying the controlled group with homology; and the stability theorem describing the behavior of the inverse limit as the control parameter goes to 0. There…
Generates new human genomic sequences for LAI training.
problem Lack of accessible reference data sets for LAI.
method Class-conditional VAE-GAN to generate realistic sequences.
result Generated sequences improve LAI method performance.
We prove that the Farrell-Jones assembly map for connective algebraic K-theory is rationally injective, under mild homological finiteness conditions on the group and assuming that a weak version of the Leopoldt-Schneider conjecture holds for cyclotomic fields. This generalizes a result of Bökstedt, Hsiang, and Madsen, …
The analysis of cancer genomic data has long suffered "the curse of dimensionality". Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more…
Copula-based fusion improves breast cancer risk stratification.
problem Combining clinical and genomic risk scores using simple rules fails to capture their joint relationship.
method Used copulas to model the joint relationship between clinical and genomic risk scores.
result Copula-based fusion improves risk stratification, identifying subgroups with the worst prognosis.
Private cancer prediction model trained on federated genomic data.
problem Train a private cancer prediction model on federated genomic data.
method Differentially private federated learning (FL) for genomic cancer prediction.
result Ranked 3rd in a competition for private cancer prediction.
SEISM tests neural network features for regulatory genomics.
problem Testing neural network features for regulatory genomics.
method Selective inference procedure for sequence motifs.
result Sampling under specific parameters characterizes composite null hypothesis.
Paper proposes neural networks for automatically naming assembly functions.
problem Automatically assigning names to assembly code functions.
method Formal definition of problem, baseline models (Seq2Seq, Transformer), fine-tuning neural networks.
result Neural networks can effectively predict function names in binaries, even outperforming state-of-the-art.
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium controlcond-mat.stat-mech
Study uses multi-agent reinforcement learning to control self-assembly with high-resolution external control.
problem Designing effective external control protocols for self-assembly with high-resolution control.
method Investigated a multi-agent reinforcement learning approach, comparing fully decentralized and partially decentralized strategies.
result Partially decentralized approach outperforms fully decentralized in controlling self-assembly towards target structures.