VAE improves MSI data analysis for tissue sub-types.
problem Analyzing MSI data from unprocessed samples.
method Applied Variational Autoencoders for data reduction and pattern detection.
result VAEs outperform standard methods in detecting tissue sub-types.
Deep Relevance Regularization improves neural network performance in tumor typing.
problem Confounding factors hinder neural network performance in multi-laboratory imaging mass spectrometry data.
method Introduces Deep Relevance Regularization to restrict neural network focus.
result Deep Relevance Regularization robustifies neural networks and improves interpretability.
Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Deep learning offers an approach to learn featu…
New method recovers relative rates in spatial compositional data from IMS.
problem Challenges in analyzing spatial data from IMS due to competitive sampling.
method Hierarchical Variational Graph Fused Lasso using heavy-tailed graphical lasso prior and automatic differentiation variational inference.
result Our method outperforms state-of-the-practice point estimate methodologies in IMS and has superior posterior coverage.
Neural network predicts electron-ionization mass spectra quickly.
problem Identifying unknown molecules not in existing libraries.
method Lightweight neural network model for predicting mass spectra.
result High accuracy predictions of small molecule mass spectra.
New framework distinguishes lung cancer subtypes using MALDI mass spectrometry.
problem Distinguishing between adenocarcinoma and squamous cell carcinoma subtypes in lung cancer.
method Supervised topological data analysis on MALDI mass spectrometry imaging data.
result The proposed framework successfully classifies lung cancer subtypes with competitive results.
We study a simple modification to the conventional time of flight mass spectrometry (TOFMS) where a \emph{variable} and (pseudo)-\emph{random} pulsing rate is used which allows for traces from different pulses to overlap. This modification requires little alteration to the currently employed hardware. However, it requi…
Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients…
DeepNovoV2 improves de novo peptide sequencing from mass spectrometry data.
problem De novo peptide sequencing from mass spectrometry data for personalized cancer vaccines.
method DeepNovoV2 combines T-Net and recurrent neural networks for end-to-end training and prediction.
result DeepNovoV2 achieves 13.01-23.95\% higher accuracy than previous methods.
Improved peptide identification from mass spectrometry data.
problem Lack of large ground truth datasets for protein identification.
method Deep neural networks trained on imperfect hand-coded models.
result 43% improvement over standard matching methods.
Neural networks predict substructures from mass spectra to identify chemical threats.
problem Identifying unknown chemical threats from mass spectra and formulas.
method Data-driven approach using neural networks to rank and match substructures.
result Substructure classifiers achieve over 90% micro F1-score and correctly identify structures in 88-71% of cases.
Paper presents a method for identifying isotope envelopes in MALDI-ToF data.
problem Deisotoping of isotopic peaks in MALDI-ToF molecular imaging data.
method Uses Mamdani-Assilan fuzzy system and spatial maps of molecular distribution to identify isotope envelopes.
result Proposed method detects overlapping envelopes and analyzes large data sets.
Improved protein identification in mass spectrometry data.
problem Expanding peptide scoring capabilities in tandem mass spectrometry.
method Deriving concave emission distributions for dynamic Bayesian networks.
result Efficiently learned scoring function outperforms state-of-the-art.
Developed HIquant to quantify proteoforms accurately without biases.
problem Peptide-specific biases in quantifying proteoforms by MS methods.
method First-principles model (HIquant) for quantifying proteoform stoichiometries.
result High accuracy in quantifying fractional PTM occupancy without external standards.
Mass spectrometry (MS) is an important technique for chemical profiling which calculates for a sample a high dimensional histogram-like spectrum. A crucial step of MS data processing is the peak picking which selects peaks containing information about molecules with high concentrations which are of interest in an MS in…
Deep learning aligns GC-MS peaks for biomarker discovery.
problem Aligning retention times of GC-MS peaks across different samples.
method ChromAlignNet, a deep learning model for peak alignment.
result ChromAlignNet outperforms existing methods on complex data sets.
Random small feature subsets outperform FS in diverse datasets.
problem The significance of selected features in high-dimensional datasets is questionable.
method Analysis of 28 diverse datasets (microarray, RNA-Seq, etc.).
result Any arbitrary set of features performs as well as or better than selected features across datasets.
SMM improves signal recovery from noisy data.
problem Estimating signals from noisy and scaled observations.
method Spiked mixture model (SMM) with EM algorithm.
result SMM outperforms GMM in signal recovery.
Microbial identification is a central issue in microbiology, in particular in the fields of infectious diseases diagnosis and industrial quality control. The concept of species is tightly linked to the concept of biological and clinical classification where the proximity between species is generally measured in terms o…
ForestDSH hashes improve nearest neighbor search in high-dimensional data.
problem High-dimensional classification and nearest neighbor search.
method Distribution-sensitive hashing using a forest of decision trees.
result ForestDSH hashes outperform LSH and state-of-the-art methods in speed and accuracy.
Generative model gradients enhance MS/MS peptide identification.
problem Improving peptide identification from MS/MS spectra.
method Leverage log-likelihood gradients of generative models in a kernel-based classifier.
result Fisher kernel outperforms other methods on MS/MS datasets.
Selective prediction framework reduces errors in molecular structure identification from MS/MS.
problem High-stakes applications require reliable molecular structure identification from MS/MS data.
method Selective prediction framework using risk-coverage tradeoff and uncertainty quantification.
result First-order confidence measures and retrieval-level aleatoric uncertainty achieve strong risk-coverage tradeoffs.
Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult task from both the clustering accuracy and the result understanding points of view. This paper presents a discriminative latent mixture (DLM) model which fits the data in a latent orthonormal discrim…
Machine learning detects subhalos in lensed images with high accuracy and low false positives.
problem Detecting substructure in strongly lensed images.
method Developed a neural network for image segmentation to locate and mass estimate subhalos.
result The network can detect subhalos with masses m≳108.5M⊙ and measure the subhalo mass function. New scalable algorithm for non-negative linear regression with entropy-regularized OT loss.
problem Generalizing task-specific linear models to broader applications.
method Sinkhorn-like scaling iterations for convex penalty and datafit terms.
result Simple multiplicative updates for various penalty and datafit terms.
SML improves pancreatic mass diagnosis accuracy using CT images.
problem Improving accuracy in pancreatic mass screening using CT imaging.
method Spectral machine learning method trained on 30,000 images, choosing fundamental images based on eigenvectors and removing irrelevant pixels.
result Achieved 94.6% test accuracy in diagnosing 113 patients.
Deep learning predicts mass from images with sparse ground truth.
problem Accurately estimating mass from images with limited ground truth.
method Semi-supervised deep learning with gradient aggregation and sparse ground truth.
result Deep neural network accurately predicts mass from images.
Study uses CNNs to estimate BMI from photos.
problem Estimating BMI from photos with limited data.
method Used deep learning (CNNs) to process silhouette images.
result High correlation between estimated and actual BMI.
This work originates from a heart's images tracking which is to generate an apparent continuous motion, observable through intensity variation from one starting image to an ending one both supposed segmented. Given two images p0 and p1, we calculate an evolution process p(t, \cdot) which transports p0 to p1 by using th…
DCGANs generate realistic breast masses for mammography.
problem Lack of labelled data and imbalanced datasets in medical imaging.
method DCGANs trained on mammographic images to generate synthetic masses.
result DCGANs improve lesion detection in mammography by ~0.09 F1 score.
Probabilistic PARAFAC2 improves robustness to noise.
problem Improving robustness of PARAFAC2 to noise and determining the number of factors.
method Developed two probabilistic formulations of PARAFAC2 with variational procedures for inference.
result Probabilistic PARAFAC2 is more robust to noise and model order misspecification.
Liquid chromatography coupled with tandem mass spectrometry, also known as shotgun proteomics, is a widely-used high-throughput technology for identifying proteins in complex biological samples. Analysis of the tens of thousands of fragmentation spectra produced by a typical shotgun proteomics experiment begins by assi…
Novel process model for metabolomics data analysis.
problem Analyzing complex metabolomics data.
method Data-driven and hypothesis-driven data mining approaches using various techniques.
result Demonstrated applicability and strengths of MeKDDaM model.
In this paper, we study the challenge of feature selection based on a relatively small collection of sample pairs {(xi,yi)}1≤i≤m. The observations yi∈R are thereby supposed to follow a noisy single-index model, depending on a certain set of signal variables. A major difficulty is tha…
Deep learning reduces noise in weak lensing mass maps using GANs.
problem Noise reduction in weak lensing mass maps.
method Generative adversarial networks (GANs) applied to Subaru Hyper Suprime-Cam data.
result GANs successfully reproduce non-Gaussian information in denoised maps, showing stronger cosmological dependence.
Let B be a fiber bundle with compact fiber F over a compact Riemannian n-manifold M. There is a natural Riemannian metric on the total space B consistent with the metric on M. With respect to that metric, the volume of a rectifiable section s:M--> B is the mass of the image s(M) as a rectifiable n-current in B. Theorem…
The article calculates the near horizon limit of Wang--Yau quasi-local mass.
problem Calculating the near horizon limit of quasi-local mass.
method Utilizing the properties of the mean curvature vector and optimal embedding equation.
result Existence and uniqueness of optimal embedding and continuity of quasi-local mass.
New method calibrates classifier probabilities with guaranteed coverage.
problem Inaccurate probability estimates by classifiers in high-risk applications.
method Adaptive temperature scaling algorithm for conformal prediction.
result Improves calibration error measures and standard metrics across various tasks.
Method generates joint posterior samples of source and foreground mass distributions for gravitational lensing.
problem Challenging inference problem for high-resolution, high signal-to-noise ratio gravitational lensing.
method Combines diffusion-based generative modeling and recurrent inference machines.
result Can model realistic gravitational lensing simulations down to the noise level.
In this paper, we study the boundary behaviors of compact manifolds with nonnegative scalar curvature and with nonempty boundary. Using a general version of Positive Mass Theorem of Schoen-Yau and Witten, we prove the following theorem: For any compact manifold with boundary and nonnegative scalar curvature, if it is s…
We use an idea of Wang and Yau to give a new definition of quasi-local mass for a topological sphere in an initial date set. The new definition modifies Brown-York's definition by using certain spinor norm as lapse function. And it requires mean curvature of the topological sphere satisfies apparent horizon conditions,…
Proposes batch-weight method to correct mode imbalance in domain adaptation.
problem Mode imbalance between source and target distributions affects unsupervised domain transfer.
method Proposes batch-weight method to re-weight training samples.
result Effective in several image-to-image translation tasks.
New method generates adversarial examples using Wasserstein distance.
problem Creating robust classifiers against image manipulations.
method Developed a procedure to project onto Wasserstein ball using modified Sinkhorn iteration.
result Successfully attacked image classification models with 3% accuracy within a 10% pixel mass movement.
New method converts and optimizes sampling schedules for generative models.
problem Optimizing sampling schedules for generative models like flows and diffusions.
method Unified framework for stochastic interpolants, including point mass schedules.
result Demonstrated efficient generation of images with fewer steps.
Neural networks can predict BMI from faces, and can be fooled into wrong predictions.
problem Vulnerability of BMI prediction models to adversarial attacks.
method Test-time adversarial attacks on neural networks trained to infer BMI from facial images.
result Neural networks can be tricked into predicting incorrect BMI values, posing a risk of insurance fraud.
OLCS-Ranker improves peptide identification accuracy and speed on hard datasets.
problem Efficiently identifying peptides from MS/MS data, especially on hard datasets with many false positives.
method Cost-sensitive online learning model and iterative online learning algorithm.
result OLCS-Ranker outperforms existing methods in accuracy and speed on large datasets.
We improve image perturbation defenses using a better-defined Wasserstein threat model.
problem Real-world image perturbations are not pixel-independent, unlike ℓp threat models. method We rectify flaws in the Wasserstein threat model and explore stronger attacks and defenses.
result Current Wasserstein-robust models are ineffective against real-world perturbations.
QHMC improves HMC for sampling from complex distributions.
problem Inefficiency of HMC in sampling from spiky and multimodal distributions.
method Proposes QHMC, a quantum-inspired version of HMC with a random mass matrix.
result QHMC and QSGNHT achieve more stable and accurate sampling results.