Paper presents a new model to predict peptide:MHC-II interactions.
problem Predicting peptide interactions with MHC-II for vaccine design and immune response understanding.
method Developed a trans-allelic prediction model using sequence and structural data.
result Model predicts interactions for all three human MHC-II loci and performs comparably to state-of-the-art methods.
PepCVAE designs novel antimicrobial peptides using a semi-supervised VAE.
problem Designing novel antimicrobial peptides for next-generation antimicrobial resistance solutions.
method Semi-supervised variational autoencoder (VAE) model that learns latent and antimicrobial attribute spaces from unlabeled and labeled data.
result PepCVAE generates novel AMPs with higher long-range diversity and closer to biological peptide distribution.
AMP0 predicts antimicrobial peptides targeting specific microbes.
problem Low-throughput screening of antimicrobial peptides.
method Zero-shot and few-shot machine learning.
result AMP0 can predict antimicrobial activity against specific microbes.
Study improves peptide design efficiency using active and meta-learning.
problem Designing novel functional peptides is inefficient due to low throughput and lack of data.
method Investigated active learning and meta-learning for optimizing peptide design experiments.
result Meta-learning improved average accuracy, but neither method outperformed random choice.
Bayesian models predict antimicrobial and multifunctional peptides with high accuracy.
problem Classifying peptides with antimicrobial and multifunctional properties.
method Bayesian network models to identify amino acid sequence motifs and predict antimicrobial activity.
result Models achieve 94% accuracy in predicting antimicrobial activity, with interpretability of parameters.
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.
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.
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…
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.
We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalize eight kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We …
This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear part…
Deep neural networks improve free energy calculations for peptide conformations.
problem Challenges in developing suitable mappings for free energy perturbation.
method Adapted machine learning approach to train deep neural networks for mapping between Boltzmann distributions.
result Accurate free energy differences calculated between thermodynamic states with spring centers separated by 1 Å and sometimes 2 Å.
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.
Machine learning predicts signaling peptides from protein star graphs.
problem Predicting signaling activity of proteins from molecular structure.
method Protein star graphs, S2SNet topological indices, Machine Learning (SVM-RFE, Laplacian kernel).
result Best model predicts 98.0% signaling pathways with AUROC 0.961.
New method designs antimicrobial peptides with high potency and low toxicity.
problem Designing potent antimicrobial drugs with low toxicity.
method CLaSS method using deep generative autoencoder and atomistic simulations.
result Design and synthesis of two novel AMPs with high potency and low toxicity.
We attempt to set a mathematical foundation of immunology and amino acid chains. To measure the similarities of these chains, a kernel on strings is defined using only the sequence of the chains and a good amino acid substitution matrix (e.g. BLOSUM62). The kernel is used in learning machines to predict binding affinit…
Boosted GFlowNets improve exploration by sequentially training GFlowNets with residual rewards.
problem GFlowNets struggle to evenly explore reward landscapes, leading to poor coverage of high-reward areas.
method Sequential training of an ensemble of GFlowNets, each optimizing a residual reward.
result Boosted GFlowNets achieve better exploration and sample diversity on multimodal benchmarks and peptide design tasks.
Generative models accelerate molecular dynamics by four orders of magnitude.
problem Femtosecond time steps limit access to slow molecular processes.
method Deep generative modeling framework that accelerates sampling.
result Quantitative characterization of equilibrium ensembles and dynamical relaxation processes.
A novel method clusters protein conformations from MD simulations.
problem Clustering long MD protein dynamics for identifying states and behavior.
method Adversarial Autoencoder (AAE) for conformation clustering.
result Identifies many salient features of the folding process.
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.
We introduce a machine learning approach for extracting fine-grained representations of protein evolution from molecular dynamics datasets. Metastable switching linear dynamical systems extend standard switching models with a physically-inspired stability constraint. This constraint enables the learning of nuanced repr…
The paper introduces tests for high-dimensional independence using maximum and average distance correlations.
problem Testing independence in high-dimensional data.
method Characterizes consistency properties, compares test statistics, examines null distributions, and presents a fast chi-square-based procedure.
result The proposed tests are non-parametric and applicable to various metrics.
Bayesian models discover CVs for complex systems, enhancing sampling methods.
problem Limitations in modeling complex systems in biochemistry and materials science.
method Formulated CV discovery as a Bayesian inference problem, using deep learning and variational inference.
result Discovered CVs improve predictive ability for alanine dipeptide and ALA-15 peptides.
BoGA combines evolutionary search with Bayesian optimization for efficient protein design.
problem Designing novel proteins with specific characteristics is challenging due to sequence space complexity.
method BoGA integrates a genetic algorithm with Bayesian optimization to efficiently explore sequence space.
result BoGA accelerates discovery of high-confidence binders for diverse protein design objectives.
Smooth flows for physical systems with smooth energies and forces.
problem Smooth energies for physical simulations and force computation.
method Smooth mixture transformations on compact intervals and hypertori, using root-finding and the inverse function theorem.
result Smooth flows allow training by force matching and use as molecular dynamics potentials.
Deep neural networks predict B-cell epitopes for SARS-CoV and SARS-CoV-2.
problem Accurate prediction of B-cell epitopes for vaccine design.
method Deep neural network model with regularization techniques and key features analysis.
result Overall accuracy of 82% in predicting COVID-19 cases.
Enhances diffusion-based sampling for molecular systems.
problem Inefficiency and thermodynamic mode miss in diffusion-based samplers for molecular systems.
method Introduces a sequential bias along collective variables (CVs) to encourage exploration and increase temperature in the projected space.
result Improves efficiency, mode discovery, and free energy estimation; first to demonstrate reactive sampling.
Timewarp accelerates molecular dynamics by learning to simulate long timescales.
problem Efficiently simulating long timescales in molecular dynamics.
method Uses a normalizing flow to learn large time steps in Markov chain Monte Carlo.
result Generalizes to unseen small peptides, accelerating sampling.
IFM improves recommender systems by learning flexible feature interactions.
problem Dealing with feature interactions in recommender systems can degrade performance.
method Introduces Interaction-Aware Mechanism (IAM) to learn feature and field interactions.
result Improves performance on two well-known datasets compared to state-of-the-art methods.
Surrogate-based analysis of interactions via local effect smooths
problem Detecting and characterizing feature interactions in machine learning models
method Surrogate-based analysis using generalized additive models
result Empirical validation of effectiveness for pairwise interactions
A new method detects interactions in neural networks using topological analysis.
problem Detecting interactions between input features in neural networks.
method Topological analysis of neural network connectivity to quantify interaction strength.
result The PID algorithm outperforms state-of-the-art baselines in interaction detection tasks.
Interactive learning framework for various settings.
problem Various interactive learning settings.
method Adapted active learning algorithm for interactive structure discovery.
result Noise-tolerant algorithm with favorable query complexity.
InteractE improves link prediction in KGs by increasing feature interactions.
problem Improving link prediction in knowledge graphs by inferring missing links.
method Feature permutation, novel feature reshaping, circular convolution.
result InteractE outperforms ConvE on multiple KG datasets.
Machine learning improves implicit solvent models for molecular dynamics.
problem Accurate modeling of solvent effects for biological molecules is challenging.
method Leveraging machine learning and multi-scale coarse graining, ISSNet models implicit solvent potentials.
result ISSNet models outperform traditional methods in reproducing protein thermodynamics.
Enhances MTGP for better hierarchical latent interactions.
problem Current MTGPs struggle with hierarchical latent interactions.
method Proposes a novel kernel representation for hierarchical interactions in LMC of MTGP.
result Promotes knowledge transferring in MTGP through hierarchical interactions.
iKF method uncovers complex variable interactions for scientific discovery.
problem Limited interpretability of existing models in decision-making applications.
method Iterative Kings' Forests (iKF) method to uncover multi-order interactions.
result iKF provides strong interpretive power for explainable modeling.
Graph neural network predicts vehicle interactions and trajectories for autonomous driving.
problem Predicting future motion of vehicles in traffic scenes.
method Graph neural network that jointly predicts interaction modes and 5-second future trajectories.
result Jointly predicting trajectories and interaction modes leads to lower trajectory error.
A graph neural network detects beneficial feature interactions for recommender systems.
problem Feature interactions are crucial but not all are beneficial for recommendation accuracy.
method Graph neural network with L0 activation regularization for edge prediction.
result The model outperforms baselines and automatically identifies beneficial feature interactions.
We describe and extract time-ordered multibody interactions from complex systems.
problem Complex systems with temporal and multibody dependencies.
method Decompose multivariate Markov chains into time-ordered multibody interactions. Algorithm to extract interactions from data. Measure complexity of interaction ensembles.
result Robust and efficient algorithm to infer time-ordered multibody interactions from data.
Dropout regularizes against high-order interactions by canceling interaction rates.
problem Overfitting to high-order interactions in neural networks.
method Analyzes Dropout through the lens of interaction effects, showing how it effectively cancels out the probability of surviving interactions of different orders.
result Dropout regularizes against high-order interactions by effectively canceling out the probability of surviving interactions of different orders.
Understanding how features interact with each other is of paramount importance in many scientific discoveries and contemporary applications. Yet interaction identification becomes challenging even for a moderate number of covariates. In this paper, we suggest an efficient and flexible procedure, called the interaction …
GMVAE improves clustering in molecular simulations data.
problem Clustering metastable states in multi-basin free-energy landscapes.
method Gaussian mixture variational autoencoder (GMVAE) for dimensionality reduction and clustering.
result Enhanced clustering of metastable states compared to standard VAEs.
SocialInteractionGAN generates realistic human interactions from low-dimensional data.
problem Generating realistic human interactions from limited data.
method Adversarial architecture with a recurrent encoder-decoder generator and dual-stream discriminator.
result SocialInteractionGAN produces high-quality action sequences of interacting people.
A new method detects interactions in machine learning models.
problem Interpreting non-linear and interaction effects in machine learning models.
method Regional effect plots with implicit interaction detection.
result The method quantifies and interprets feature effects reliably, less confounded by interactions.
Robot learns to imitate human interactions through deep learning.
problem Teaching robots to coordinate actions with human partners.
method Deep learning framework for motion embedding, prediction, and trajectory generation.
result Importance of predictive and adaptive components for successful imitation.
xDeepInt learns both vector-wise and bit-wise feature interactions.
problem Learning feature interactions for CTR prediction and recommendation.
method Polynomial Interaction Network (PIN) architecture with subspace-crossing mechanism.
result xDeepInt outperforms state-of-the-art models in CTR prediction and recommendation.
Bayesian neural networks detect complex interactions with uncertainty.
problem Estimating global pairwise interaction effects with uncertainty.
method Bayesian neural network for modeling interactions, GEH for estimating interaction effects.
result The method empirically outperforms alternatives and detects interpretable interactions.