Study benchmarks contextual bandit algorithms for precision oncology using in vitro data.
problem Designing effective protocols for individual treatment assignment in precision oncology.
method Proposed a benchmark dataset of in vitro drug responses to evaluate contextual bandit algorithms.
result Bayesian bandit algorithms performed better than a rule-based baseline in minimizing regret.
GeneDisco benchmarks experimental design for drug discovery.
problem Vast experimental design space in drug discovery.
method Machine learning for optimal experimental design.
result Standardised benchmark suite for active learning.
AbDiffuser generates full-atom antibodies with sequence and structure fidelity.
problem Generating high-fidelity antibodies with both structure and sequence information.
method Equivariant and physics-informed diffusion model with novel protein structure representation.
result AbDiffuser generates antibodies with sequence and structural properties matching a reference set.
Paper predicts IVF pregnancy rates from basic patient info.
problem Predicting IVF pregnancy rates from patient characteristics.
method Clustering patients into groups, then SVM models for each group.
result Support vector machine models achieve best overall performance.
Neural networks have shown great potential in many applications like speech recognition, drug discovery, image classification, and object detection. Neural network models are inspired by biological neural networks, but they are optimized to perform machine learning tasks on digital computers. The proposed work explores…
Paper predicts embryo implantation probability from IVF time-lapse imaging.
problem Manual embryo selection in IVF has low success rates.
method Data-driven system trained on time-lapse imaging videos.
result Algorithm improves positive and negative predictive values.
The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference. In vitro neurons, on the other hand, exhibit a highly deterministic response to various types of stimulation. We show that an ensemble of deterministic le…
Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error approach by individual experiences of pharmaceutical scientists, which is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important cap…
Mechanistic models of single-neuron dynamics have been extensively studied in computational neuroscience. However, identifying which models can quantitatively reproduce empirically measured data has been challenging. We propose to overcome this limitation by using likelihood-free inference approaches (also known as App…
Develops a scalable model for drug combination prediction in cancer.
problem Accurate prediction of drug combinations for cancer treatment.
method Permutation invariant multi-output Gaussian Processes with variational approximation and deep generative model.
result Model efficiently borrows information across drug combinations and provides uncertainty quantification.
Method infers TCR effects on disease using patient data.
problem Estimating causal effects of TCR sequences on patient outcomes.
method Corrects for confounders using pre-selection TCR data and neural-network estimator.
result Identifies potentially therapeutic TCRs for COVID-19 severity.
Semi-supervised learning improves QSAR model predictions for novel compounds.
problem Improving model predictions for compounds not in the training set and adjusting for selection bias.
method Semi-supervised learning framework to estimate model quality and adjust for selection bias.
result Predictions for novel compounds are improved by accounting for compound similarity and selection bias.
We address the problem of parameter estimation in models of systems biology from noisy observations. The models we consider are characterized by simultaneous deterministic nonlinear differential equations whose parameters are either taken from in vitro experiments, or are hand-tuned during the model development process…
AI framework uses multi-omics data to personalize cancer treatment suggestions.
problem Leveraging AI for personalized cancer treatment based on complex patient characteristics.
method Modular machine learning framework trained on diverse multi-omics technologies.
result Superior performance in personalized counterfactual treatment suggestions.
The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we …
Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as system calls and permissions. However, recent findings have highlighted the fragili…
Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recent advances in producing large drug screens against cancer cell lines provided an opportunity to apply machine learning methods for this purpose. In addition to cytotoxi…
LaMBO optimizes biological sequences using autoencoders and Bayesian optimization.
problem Bayesian optimization for drug design is limited by discrete, high-dimensional decision variables.
method Jointly trains denoising autoencoder with a Gaussian process head for gradient-based optimization in latent space.
result LaMBO outperforms genetic optimizers and requires no large pretraining corpus.
Deep learning architectures have proved versatile in a number of drug discovery applications, including the modelling of in vitro compound activity. While controlling for prediction confidence is essential to increase the trust, interpretability and usefulness of virtual screening models in drug discovery, techniques t…
New edge features improve GNN performance in biological datasets.
problem Inefficient use of edge features in GNNs.
method Self-supervised and unsupervised learning for new edge features, incorporating Forman-Ricci curvature.
result Improved node classification performance over baseline GNN models.
A new hierarchy quantifies agency in systems based on information processing.
problem Lack of a measurable, universal definition for agency in intelligent systems.
method Developed a bottom-up framework based on information processing hierarchy.
result Identified three orders of information processing (I, II, III) as necessary for agency.
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.
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.
Bayesian optimization of antibodies learns from immune system evolution.
problem Efficiently optimizing antibody sequences in a large space of possibilities.
method Bayesian optimization guided by a generative model of evolving antibody sequences.
result CloneBO optimizes antibodies more efficiently than previous methods.
Deep generative model discovers inhibitors for unknown targets.
problem Discovering novel inhibitor molecules for unknown drug targets.
method Deep generative framework trained on protein sequences, small molecules, and interactions.
result Micromolar-level inhibition observed for two out of four synthesized candidates, including activity against SARS-CoV-2 variants.
Framework designs antiviral drugs using deep learning and RL.
problem Designing effective antiviral drugs for SARS-CoV-2.
method Deep learning framework with conditional molecular generator and RL.
result Framework generates more antiviral ligands than a VAE baseline.
In this paper we introduce the transductive linear bandit problem: given a set of measurement vectors X⊂Rd, a set of items Z⊂Rd, a fixed confidence δ, and an unknown vector θ∗∈Rd, the goal is to infer $\text{argmax}_{z\in \mathcal{Z}} z^\t…
Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. [...] Biomedical literature mining can aid DDI research by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, …
AntBO optimizes antibody design using Bayesian optimization for efficient and effective CDRH3 sequence generation.
problem Designing optimal antigen-specific CDRH3 regions in antibody design due to combinatorial sequence space.
method Combinatorial Bayesian optimization framework with trust region for developability.
result AntBO designs CDRH3 regions with diverse biophysical properties and outperforms existing methods.
Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks,…
A new method for handling imbalanced big data using ensembles and smart data.
problem Imbalanced data distribution in big data scenarios.
method Smart Data driven Decision Trees Ensemble (SD_DeTE) methodology.
result SD_DeTE outperforms Random Forest in handling imbalanced binary classification problems in big data.
Prevents sensitive data generation in diffusion models using labeled and unlabeled data.
problem Generating sensitive data in diffusion models using unlabeled data.
method Positive-Unlabeled Diffusion Models, approximating ELBO with labeled and unlabeled data.
result Prevents the generation of sensitive data without compromising image quality.
Study reveals Data Shapley's inconsistent performance in data selection tasks.
problem Inconsistency of Data Shapley's performance in data selection across different settings.
method Hypothesis testing framework and identification of utility functions.
result Data Shapley's performance is no better than random selection without specific constraints.
PRRO generates synthetic tabular data that improves SL performance and class distribution.
problem Low SL utility of synthetic data due to class imbalance and overlooked data relationships.
method Data pruning and column reordering to optimize SL utility.
result Synthetic data generated with PRRO enhances predictive performance and class distribution.
Defines data science as a natural ecosystem with challenges and missions.
problem Challenges and missions in data science due to 5D complexities and data life cycle phases.
method Systemic and data-centric view of data science as a fusion of data universe and its challenges, formalizing a general-purpose architecture.
result Essential data science as a natural ecosystem integrating specific disciplines and high-impact applications.
Synthetic data enhances analytics but requires careful volume management.
problem Accuracy of statistical methods on synthetic data vs. raw data.
method Synthetic Data Generation for Analytics framework using tabular diffusion models.
result Error rate decreases with more synthetic data but may stabilize or increase.
This paper introduces C-DSL to improve data mining outcomes by considering context.
problem Data collection ambiguities, data imbalance, hidden biases, lack of domain info, and data incompleteness.
method Developed Context-Driven Data Science Lifecycle (C-DSL) to address data quality issues.
result Tangible improvements to data mining outcomes were achieved through C-DSL.
Proposes using probabilistic models for privacy-preserving synthetic data.
problem Designing high-quality synthetic data for privacy preservation.
method Formulate the problem through probabilistic modelling, choosing a model for the data.
result Statistical discoveries can be reliably reproduced from synthetic data.
New test ensures quality of shared data in machine learning.
problem Ensuring quality of external data in machine learning tasks.
method Distribution-free two-sample testing procedures grounded in conformal outlier detection.
result Identifies valuable external data agents for model personalization.
Paper creates fair synthetic data ensuring equal predictions across sensitive attributes.
problem Ensuring fair predictions across sensitive attributes in synthetic data.
method Equalizing target probability distributions across sensitive attributes in synthetic data generation.
result Synthetic data provides strong fair predictions, equal across all thresholds.
DPA preserves data distribution in reduced dimensions.
problem Loss of data distribution in dimension reduction.
method DPA combines encoder and decoder to match data distribution.
result DPA successfully reconstructs data distribution.
Efficient synthetic data generation improves model performance on tabular data.
problem Improving model robustness and performance with scarce or low-quality data.
method Hardness characterization to identify high-value training points, generating synthetic data only from these points.
result Synthetic data generated from hardest points outperforms non-targeted methods on tabular datasets.
For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly ass…
DAERNN models censored data using neural networks with data augmentation.
problem Handling censored data in expectile regression.
method Data augmentation based Expectile Regression Neural Networks (ERNNs).
result DAERNN outperforms existing censored ERNNs methods and achieves comparable predictive performance to fully observed data.
Data preprocessing techniques are devoted to correct or alleviate errors in data. Discretization and feature selection are two of the most extended data preprocessing techniques. Although we can find many proposals for static Big Data preprocessing, there is little research devoted to the continuous Big Data problem. A…
Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream. However, multiple data streams that involve several correlated data streams are common in many practical scenarios, which can be viewed as multi-task data streams. Instead of handli…
Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough …
This paper quantifies uncertainty in Data Shapley using statistical inference.
problem Uncertainty in data valuation due to dynamic data distribution.
method Established relationship with U-statistics and quantified uncertainty using statistical inference.
result Confidence intervals for Data Shapley estimations are provided.