Deep neural networks infer multiple cancer properties from transcriptome data.
problem Limited use of biomarkers in molecular cancer pathology due to computational challenges.
method Multi-task and transfer learning architecture encoding whole transcriptome into a latent vector.
result Significantly better at predicting tissue-of-origin, disease state, and cancer type.
Automatic Chemical Design is a framework for generating novel molecules with optimized properties. The original scheme, featuring Bayesian optimization over the latent space of a variational autoencoder, suffers from the pathology that it tends to produce invalid molecular structures. First, we demonstrate empirically …
New test identifies specific biological parameters for personalized CVD detection.
problem Ineffectual pathology tests fail to consider platelet activation and inter-individual variability.
method Stochastic platelet deposition model and approximate Bayesian computation with discriminative summary statistics.
result Inferred parameters help identify specific biological parameters for personalized CVD detection.
A new ML method predicts long-time-step molecular dynamics, preserving symplectic and time-reversible properties.
problem Limited computational efficiency in long-time-step molecular dynamics simulations.
method Learning data-driven structure-preserving maps to generate long time-step classical dynamics.
result The method eliminates artifacts like lack of energy conservation and loss of equipartition.
A faster method for optimizing DNA and protein sequences using machine learning.
problem Designing DNA and protein sequences with improved function.
method Activation maximization with a straight-through approximation and adaptive entropy variable.
result Fast SeqProp achieves up to 100-fold faster convergence and improved fitness optima.
Method predicts ODX scores for breast cancer patients based on clinical data.
problem Predicting ODX scores for breast cancer patients to aid decision-making.
method Distributional random forest approach using 9 clinico-pathological characteristics.
result Correctly predicted 92% of low risk and 40.2% of high risk patients.
PS-VAE extracts multi-parameter MRI biomarkers with uncertainty quantification.
problem Uncertainty in inverse problems limits clinical acceptance of quantitative MRI methods.
method Physics-Structured Variational Autoencoder (PS-VAE) integrating physics simulator and self-supervised learning.
result PS-VAE provides full covariance of inter-parameter correlations and accelerates multi-parametric MRI quantification.
Method learns feature maps from deep CNN layers for weakly supervised chest pathology localization.
problem Localization of chest pathologies in X-ray images is challenging due to varying sizes and appearances.
method Class-aware deep multiscale feature learning using intermediate feature maps from CNN layers.
result Improves localization performance of small pathologies like nodules and masses.
This study investigates how much knowledge from natural images can be transferred to pathology images.
problem Quantifying how much knowledge from natural images can be transferred to pathology images.
method Proposes a framework to quantify knowledge gain by a particular layer, conducts empirical investigation in pathology image centered transfer learning.
result Early layers of deep models can transfer knowledge to pathology image classification tasks.
Study classifies pathology reports using TF-IDF features and machine learning.
problem Classifying pathology reports for cancer surveillance and diagnostic workflow.
method Extracted TF-IDF features from pathology reports and classified them using SVM, XGBoost, and Logistic Regression.
result XGBoost achieved 92% accuracy in classifying pathology reports.
This paper characterizes VAE training pathologies and their effects on tasks.
problem Characterizing VAE training pathologies and their impact on downstream tasks.
method Concretely characterizing conditions for VAE training pathologies and their connection to specific downstream tasks.
result Connects VAE training pathologies to specific downstream tasks like learning compressed and disentangled representations, adversarial robustness, and semi-supervised learning.
Proposes deep learning method for GCI detection from pathological speech.
problem Detecting glottal closure instants (GCI) in pathological acoustic speech.
method Convolutional neural network with fused deep acoustic speech and linear prediction residual features.
result Significantly better than state-of-the-art methods in GCI detection.
Semi-supervised learning classifies cardiac pathology using motion features from cine MRI.
problem Classifying cardiac pathology based on motion features from cine MRI.
method Semi-supervised learning of apparent flow to generate motion features from non-segmented images.
result The model achieves 95% classification accuracy on ACDC test set.
Novel approach characterizes deep neural networks at initialization.
problem Characterizing the behavior of deep neural networks at initialization.
method A novel approach considering the evolution of statistical moments of signal and noise.
result Established that skip-connections in residual networks lead to well-behaved moments and no pathology.
Batch normalization in the last layer reduces sharpness in wide neural networks.
problem Pathological sharpness in wide neural networks.
method Quantifying the geometry of the parameter space using Fisher information matrix and analyzing deep neural networks with random initialization.
result Batch normalization in the last layer significantly decreases pathological sharpness under specific conditions.
The paper identifies conditions for trend reversal in classification tasks.
problem Trend reversal in classification scores and dataset values.
method Algebraic conditions and numerical results for ridge regression.
result Existence of pathological regularization regimes for certain dataset conditions.
SAPSAM trains CNNs on lung CTs with binary labels, improving CPA detection and localization.
problem Chronic Pulmonary Aspergillosis (CPA) detection and localization on CT scans using binary labels.
method Binary labels, average intensity projections, 2D RGB-like images, hierarchical CNN architectures.
result High classification accuracy, precise localization, predictive power of 2-year survival.
KL-regularized RL from expert demos can lead to slow, unstable learning.
problem Pathological training dynamics in KL-regularized RL from expert demonstrations.
method Empirical analysis and non-parametric behavioral reference policies.
result KL-regularized RL can be significantly improved by using non-parametric behavioral policies.
This work examines robust MCMC for pathological distributions.
problem Pathological behavior in target distributions affects MCMC efficiency.
method Reviewing and proposing remedies for roughness and flatness in MCMC.
result Robust MCMC algorithms can perform well even in challenging conditions.
Paper develops a BERT-based classifier to reduce pathology report annotation workload.
problem Manual annotation of pathology reports is labor-intensive and time-consuming.
method Developed an automatic text classifier using BERT and introduced a human-centric metric to identify low-confidence cases.
result The model reduces manual annotation workload by 80% to 98%.
Generative model for morphological continuum of normal and pathological states.
problem Identifying trends and features that separate normality and pathology in biomedical images.
method Wasserstein Auto-encoder with HSIC regularization for latent features.
result Model generates a continuum of morphological changes corresponding to side information.
The paper presents algorithms for diagnosing Pathological Myopia and detecting retinal structures.
problem Diagnosing Pathological Myopia and detecting retinal structures in fundus images.
method The approach uses Deep Learning techniques, including transfer learning with Xception and YOLO architecture.
result The method has shown satisfactory results in the Pathologic Myopia Challenge.
New analysis explains pathology of deep Gaussian processes.
problem Pathology of deep Gaussian processes reduces learning capacities with increased layers.
method Study nonlinear dynamic systems corresponding to DGPs, derive recurrence relations.
result Provide tighter bounds and rate of convergence for dynamic systems.
Machine learning improves EEG pathology classification.
problem Automating clinical EEG analysis using machine learning.
method Developed a comprehensive feature-based framework and compared it to deep neural networks.
result Feature-based framework achieves accuracies similar to deep neural networks.
Causal methods for GRN inference from single-cell data often fail in real-world benchmarks.
problem Understanding when and why causal methods for GRN inference from single-cell data fail in real-world benchmarks.
method Introduced a controlled diagnostic framework to isolate and measure seven pathologies.
result Causal methods dominate in clean and structurally favorable regimes but fail in specific pathologies.
Regularized Stein thinning improves MCMC output approximations.
problem Pathologies in Stein thinning leading to poor approximations.
method Theoretical analysis and regularization to improve KSD.
result Regularized Stein thinning alleviates pathologies and improves efficiency.
A new model designs molecular latent vectors for drug discovery.
problem Designing effective molecular descriptors from molecular structures.
method Proposes a denoising diffusion probabilistic model (DDPM) for variational autoencoding molecular graphs.
result Demonstrates superior prediction performance and robustness compared to existing approaches.
MOSES benchmarks molecular generation models using a standardized dataset and metrics.
problem Unclear comparison and ranking of molecular generation models.
method Developed MOSES platform with training and testing datasets, metrics.
result Suggested MOSES results as reference for advancements in generative chemistry.
For test configurations, the Donaldson-Futaki invariant F_1 is well-known. In this note, its refinement will be discussed. Then we see that Li-Xu's pathology doesn't occur, since their example of a non-normal test configuration, with trivial normalization, actually has non-vanishing F_1 in this refined sense.
MoFlow generates chemically valid molecular graphs from latent representations.
problem Generating chemically valid molecular graphs from latent representations is challenging.
method MoFlow uses a flow-based approach with Glow for bond generation and a novel graph conditional flow for atom generation, ensuring chemical validity and efficiency.
result MoFlow achieves state-of-the-art performance in molecular graph generation and optimization.
Method treats pseudo healthy synthesis as a factor decomposition problem.
problem Creating a healthy-looking image from a pathological one.
method Adversarial training with paired or unpaired settings, combining two factors (healthy and disease) to reconstruct the input.
result Method outperforms conditional GAN and CycleGAN in generating pseudo healthy images.
This paper proposes synthetic augmentation for nuclei image segmentation in medical pathology.
problem Rare and time-consuming labeling of tumor nuclei images for semantic segmentation.
method Label-to-image translation to generate synthetic images.
result Synthetic augmentation improves segmentation accuracy.
Study compares GNNs and classical molecular featurisations for molecular property and cliff prediction.
problem Comparing GNNs and classical featurisations for molecular property and cliff prediction.
method Systematic exploration and comparison of PDVs, ECFPs, and GNNs; introduction of substructure pooling.
result Sort & Slice outperforms hash-based folding in ECFP vectorization.
Novel RL approach for molecular design using quantum mechanics.
problem Existing RL methods for molecular design are limited in scope and reward function.
method Formulation in Cartesian coordinates, direct use of quantum mechanics for reward function, translation and rotation invariant state-action space.
result Agent efficiently learns to solve molecular design tasks from scratch.
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules w…
LSS learns molecular trajectories from MD data.
problem Limited integration time steps in MD simulations.
method Three deep learning networks for slow collective variables, dynamics, and configuration reconstruction.
result Generates ultra-long synthetic folding trajectories.
RL-VAE uses RL to decode molecular graphs from latent embeddings.
problem Efficiently decoding molecular graphs from latent embeddings.
method Repurposed simple graph generator for efficient decoding.
result Decoding molecular graphs from latent embeddings is possible with a simple graph generator.
Bayesian neural networks quantify uncertainties in molecular property predictions.
problem Poor predictions in molecular property predictions due to unreliable training data.
method Bayesian neural networks to estimate model-driven and data-driven uncertainties.
result Uncertainty quantification is necessary for reliable molecular applications.
Graph neural networks improve molecular property prediction.
problem Efficiently predicting molecular properties with high accuracy and scalability.
method Gated Graph Recursive Neural Networks (GGNN) with skip connections.
result GGNN achieves state-of-the-art performance on molecular property prediction benchmarks.
Study reveals pathological eigenvalue spectra in FIM and its variants of DNNs.
problem Understanding sharp local shapes in DNN loss landscapes.
method Analysis of FIM and its variants in regression and classification DNNs.
result Pathological eigenvalue spectra appear in FIM and its variants, indicating sharp local shapes in specific directions.
Machine learning improves molecular dynamics simulations by reducing costs and enhancing accuracy.
problem Inaccurate and costly molecular dynamics simulations hinder chemical system description.
method Adaptive sampling of reference data points and machine learning models for predicting molecular properties.
result Machine learning models can predict molecular dipole moments and infrared spectra accurately.
Generative model learns molecular geometry from graph representations.
problem Generating equilibrium states for molecular systems is computationally expensive.
method Probabilistic model based on Euclidean distance geometry.
result Generative model achieves state-of-the-art accuracy in molecular conformation generation.
AniDS improves molecular force field modeling by learning anisotropic noise.
problem Molecular force field modeling suffers from oversimplified assumptions about atomic motions.
method AniDS introduces anisotropic noise generation for better modeling of directional and structural variability.
result AniDS outperforms existing methods on benchmarks, achieving significant improvements in force prediction accuracy.
Machine learning models simulate molecular spectra and reactions in solvents.
problem Accurate simulation of molecular spectra and reactions in solvent environments.
method Introduced FieldSchNet, a deep neural network for modeling molecular interactions with external fields.
result Demonstrated significant lowering of Claisen rearrangement reaction activation barrier using FieldSchNet.
BBRT improves molecular properties through iterative translation.
problem Optimizing molecular structures for improved biochemical properties.
method Iterative translation of molecules using a black box approach.
result Improvement in molecular properties with each iteration of the translator.
LaPool improves molecular graph representation learning by capturing interaction importance.
problem Lack of efficient intermediate pooling steps in GNNs leads to poor molecular substructure representation.
method LaPool is a novel, data-driven, and interpretable hierarchical graph pooling method that considers node features and graph structure.
result LaPool outperforms recent GNNs on molecular graph prediction and understanding tasks.
3DGCN predicts molecular properties and biochemical activities using 3D molecular graph.
problem Predicting molecular properties and biochemical activities from 3D molecular graphs.
method Unified graph convolution with learning operations to handle spatial information, distinguishing 3D rotations.
result Significantly higher performance on various molecular tasks compared to other deep-learning models.
Framework for training-free guidance in discrete diffusion models for molecular generation.
problem No equivalent training-free guidance methods for discrete diffusion models.
method Framework using guidance functions for discrete data.
result Demonstrated utility on molecular graph generation tasks.