yusuke1997/dblp-id-title huggingface dataset:
Predicting adverse drug reactions for combination pharmacotherapy with cross-scale associative learning via attention modules
Gauging urban development with neural networks
What's so hard about RNA-targeting drug discovery?
The digital twin revolution
Eva to the rescue
RNA-ligand interaction scoring via data perturbation and augmentation modeling
A successful few drive urban scaling
Advancements in multicellular simulations
Deep contrastive learning of molecular conformation for efficient property prediction
Accelerating economic development in Latin America through overcoming access challenges to supercomputing infrastructure
Cover runners-up of 2023
An efficient and scalable top-down method for predicting structures of microbial communities
The motive cocktail in altruistic behaviors
Towards practical applications in quantum computational biology
Modeling the building blocks of life
Computational challenges arising in algorithmic fairness and health equity with generative AI
Towards privacy-aware mental health AI models
Analyzing noise for quantum advantage
A structural optimization algorithm with stochastic forces and stresses
Discovering and forecasting extreme events via active learning in neural operators
Rotamer-free protein sequence design based on deep learning and self-consistency
The meaning emerging from combining words can be detected in space but not time
Fast inverse design of microstructures via generative invariance networks
Modeling the electrical double layer at solid-state electrochemical interfaces
Learning on topological surface and geometric structure for 3D molecular generation
Inter-individual and inter-site neural code conversion without shared stimuli
Accurate and efficient fluid flow analysis
Reducing qubit budget in quantum machine learning
Enhancing biomarkers with co-abundance
Predicting physics efficiently with hybrid hardware
Mobility data as a proxy for epidemic measures
Data-driven law firm rankings to reduce information asymmetry in legal disputes
Computational models of social learning
From noise to protein with image models
Using machine learning to go beyond potential energy surface benchmarking for chemical reactivity
Defect modeling and control in structurally and compositionally complex materials
Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning
A dynamic block activation framework for continuum models
Evaluating and mitigating bias in AI-based medical text generation
Crypto and technology for the people
Algorithms for reliable decision-making need causal reasoning
An integrative data-driven model simulating C. elegans brain, body and environment interactions
But is the code (re)usable?
From inception to current challenges in bioinformatics
Co-occurrence and similarity revisited
A software resource for large graph processing and analysis
In silico biological discovery with large perturbation models
Delineating cell types with transcriptional kinetics
Accurate assembly of multi-end RNA-seq data with Scallop2
Privacy-preserving multicenter differential protein abundance analysis with FedProt
Interpreting single-cell and spatial omics data using deep neural network training dynamics
A kinetic ensemble of the Alzheimer's Aβ peptide
Publisher Correction: Similarity-driven multi-view embeddings from high-dimensional biomedical data
Computational and ethical considerations for using large language models in psychotherapy
The Universe's expansion in the eyes of computers
Understanding cancer drivers and passengers
Annotating cell types in single-cell ATAC data via the guidance of the underlying DNA sequences
Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen
Deep learning to estimate brain age
Accurate short-term precipitation prediction
Efficient methods for facilitating topological photonics and acoustics computation
Recent advances and outstanding challenges for machine learning interatomic potentials
Interpretable predictions of cellular behavior
On the unknowable limits to prediction
The carbon footprint of computational research
Population-scale identification of differential adverse events before and during a pandemic
Biophysically interpretable inference of cell types from multimodal sequencing data
An interdisciplinary effort to integrate coding into science courses
Chiral topographic instability in shrinking spheres
Improving the efficiency of ab initio electronic-structure calculations by deep learning
Persistent spectral theory-guided protein engineering
A simulated annealing algorithm for randomizing weighted networks
Interpretable neural architecture search and transfer learning for understanding CRISPR-Cas9 off-target enzymatic reactions
Towards practical and robust DNA-based data archiving using the yin-yang codec system
Promising directions of machine learning for partial differential equations
Efficient parallelization of tensor network contraction for simulating quantum computation
Defining our future with generative AI
A machine learning route between band mapping and band structure
Diversity-aware population modeling
Detecting microstructural deviations in individuals with deep diffusion MRI tractometry
Quantum mechanics guided by simplicity
Forced and spontaneous symmetry breaking in cell polarization
Implicit neural image field for biological microscopy image compression
A shortcut to the thermodynamic limit for quantum many-body calculations of metals
Computationally probing exciton dynamics
A deep-learning method for studying magnetic superstructures
Deciphering cell types by integrating scATAC-seq data with genome sequences
Language models for quantum simulation
Resistive memory-based zero-shot liquid state machine for multimodal event data learning
Directional multiobjective optimization of metal complexes at the billion-system scale
Modeling historical impacts of fisheries on biomass
Mathematical modeling of cancer immunotherapy for personalized clinical translation
Fast and effective protein model refinement using deep graph neural networks
The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci
Shining a light on mutational signatures
A key toolbox for cellular barcoding analysis
An imaging-based approach to measure atmospheric turbulence
A spatiotemporal style transfer algorithm for dynamic visual stimulus generation
Network model with internal complexity bridges artificial intelligence and neuroscience
Estimating cell growth rate
GREENER principles for environmentally sustainable computational science
Neural network-based order parameter for phase transitions and its applications in high-entropy alloys
Rapid traversal of vast chemical space using machine learning-guided docking screens
Active learning for alloy design
Linguistics-based formalization of the antibody language as a basis for antibody language models
Reliable deep learning in anomalous diffusion against out-of-distribution dynamics
When and where the climate threshold will be reached
How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex-dentate gyrus-CA3 network
Publisher Correction: Increasing the presence of BIPOC researchers in computational science
MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery
Moving toward safer driverless vehicles
Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain
Computationally designing synthesizable molecules
Computing the committor with the committor to study the transition state ensemble
Stochastic modeling of antibody binding predicts programmable migration on antigen patterns
Discovering kirigami patterns
Why bigger quantum neural networks do better
Computational chemistry for all
New views of black holes from computational imaging
Fast kinetic simulator for relativistic matter
Publisher Correction: SimuCell3D: three-dimensional simulation of tissue mechanics with cell polarization
Pioneering real-time genomic analysis by in-memory computing
Biologically inspired graphs to explore massive genetic datasets
Binding affinity estimation from restrained umbrella sampling simulations
Accelerating the prediction of stable materials with machine learning
Data curation during a pandemic and lessons learned from COVID-19
Balancing efficient analysis and storage of quantitative genomics data with the D4 format and d4tools
Predicting protein structure from cryo-EM data
Protein sequence design by deep learning
Publisher Correction: Single-cell manifold-preserving feature selection for detecting rare cell populations
Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network
Democratizing interactive, immersive experiences for science education with WebXR
Efficient scaling of large language models with mixture of experts and 3D analog in-memory computing
Learning committor-consistent collective variables
Detection of quantitative trait loci from RNA-seq data with or without genotypes using BaseQTL
Dimensionality reduction under scrutiny
Controlling patterns of synchrony
Threats to scientific software from over-reliance on AI code assistants
Confidential computing for population-scale genome-wide association studies with SECRET-GWAS
SurFF: a foundation model for surface exposure and morphology across intermetallic crystals
Machine learning reveals how complex molecules bind to catalyst surfaces
A digital twin that interprets and refines chemical mechanisms
Author Correction: Probing the limitations of multimodal language models for chemistry and materials research
The other AI revolution: how the Global South is building and repurposing language models that speak to billions
Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort
Computational models to confront the complex pollution footprint of plastic in the environment
Pushing the frontiers in computational chemistry
Simulating synthesis of 3D network polymers
Theoretical progress toward sustainable batteries
Undisclosed, unmet and neglected challenges in multi-omics studies
Inspecting treatment benefit in clinical drug trials
The lost data: how AI systems censor LGBTQ+ content in the name of safety
AI tool makes phase identification crystal clear
Indexing and real-time user-friendly queries in terabyte-sized complex genomic datasets with kmindex and ORA
Modeling how antibody responses may determine the efficacy of COVID-19 vaccines
Leveraging epidemic network models towards wildfire resilience
Materials informatics heralds a collaborative future
Bridging the gap between density functional theory and quantum materials
Designing a biomedical coding program focused on inclusivity and agency
Digital Twin for Chemical Science: a case study on water interactions on the Ag(111) surface
The waiting game
Large-scale microbiome data integration enables robust biomarker identification
Pushing the limits of OFDFT with neural networks
Physics-educated DL refines phase mapping
How LLMs generate judgments
Reference-guided search for open reading frames
Author Correction: Computational design of quantum defects in two-dimensional materials
Fighting COVID-19 with HPC
Discovering metal complexes in vast chemical spaces
Extracting reliable quantum outputs for noisy devices
A single-cell-resolution mathematical model of the CA1 human hippocampus
Scaling digital twins from the artisanal to the industrial
Towards equitable education
A perspective on brain-age estimation and its clinical promise
Computational electrochemistry of oxygen 150 years after Priestley
The impact of language models on the humanities and vice versa
Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS
Enhancing human mobility research with open and standardized datasets
Using proprietary language models in academic research requires explicit justification
Experimental validation, anyone?
Uncovering magnetic interactions in moiré magnets
Thermal physics meets quantum computing
Leveraging pharmacovigilance data to predict population-scale toxicity profiles of checkpoint inhibitor immunotherapy
Advancing real-time infectious disease forecasting using large language models
Open source ecosystems need equitable credit across contributions
Revisiting signal analysis in the big data era
Urban planning in the era of large language models
Based on the science, diversity matters
Fewer false positives in electrocatalytic nitrogen reduction by synergizing theory and experiment
Data generation for training biomedical models
Random sketch learning for deep neural networks in edge computing
Expanding the capability of autonomous microscopy
The roadmap of climate models
Deconstructing the compounds of altruism
Unbalanced gene-level batch effects in single-cell data
Accelerating quantum molecular simulations
ECloudGen: leveraging electron clouds as a latent variable to scale up structure-based molecular design
Quantifying the information in noisy epidemic curves
Virtual node graph neural network for full phonon prediction
Computational challenges in additive manufacturing for metamaterials design
A quest toward comprehensive benchmarking of quantum computing software
Mixing patterns and the spread of pandemics
A software framework for end-to-end genomic sequence analysis with deep learning
Data as the next challenge in atomistic machine learning
Particle picking in tomograms
Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles
Effective quantum error correction by AI
Cumulative reciprocity can sustain cooperation in repeated social interactions
Neuromorphic principles in self-attention hardware for efficient transformers
Active twisting for adaptive droplet collection
Deep generative design of RNA aptamers using structural predictions
Interpretable multi-modal data integration
The value of quantum effects in reaction rates
Using computational tools to monitor and improve access to quality food and water
Correlates of protection via modeling
Quantum Monte Carlo guided by quantum computing
Detect-ing brain anomalies with autoencoders
Overcoming data scarcity in biomedical imaging with a foundational multi-task model
Predicting the shape of crystals with 'unknowable' surface energies
Generative molecular design and discovery on the rise
Alerting for imminent earthquakes
The role of ADM in brain function
Voice-controlled quantum chemistry
High-performance modeling toolkit
Strategy evolution on dynamic networks
Reducing disaster risk for the poor in tomorrow's cities with computational science
Deep active optimization for complex systems
Cost-effective instruction learning for pathology vision and language analysis
A new tool for shape and structure optimization of soft materials
Accurately predicting molecular spectra with deep learning
Tackling environmental challenges, one molecule at a time
Conversations that explain predictions
Autonomous inference of complex network dynamics from incomplete and noisy data
Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies
Universal descriptors to predict interactions of inorganic nanoparticles with proteins
Revealing trajectories of the mind via non-linear manifolds of brain activity
Why can't we predict earthquakes?
Fostering discussions on topical issues
A committor-based method to uniformly sample rare reactive events
Machine learning-guided engineering of genetically encoded fluorescent calcium indicators
Scaling up system size in materials simulation
Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data
Ada Lovelace, a role model for the ages
Everything everywhere all at once: a probability-based enhanced sampling approach to rare events
Modeling gene regulatory networks using neural network architectures
Quantifying causality in data science with quasi-experiments
Author Correction: Spatial modeling algorithms for reactions and transport in biological cells
Quantifying the uncertainty of CovidSim
Enabling efficient analysis of biobank-scale data with genotype representation graphs
Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics
Quantum computing opportunities in renewable energy
A deep learning model for predicting selected organic molecular spectra
Enhancing synthesis prediction via machine learning
Programmability empowering quantum boson sampling
Interpretable niche-based cell‒cell communication inference using multi-view graph neural networks
Harnessing large language models for data-scarce learning of polymer properties
Bi-level identification of governing equations for nonlinear physical systems
Exploring robust pattern formation
Towards accurate predictions of NMR chemical shifts in liquid solutions
Down to one network for computing crystalline materials
Disentangling truth from bias in naturally occurring data
Enhancing the ethics of user-sourced online data collection and sharing
The importance of local context in COVID-19 models
Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model
Integrating spatial transcriptomics data across different conditions, technologies and developmental stages
Progress in using deep learning to treat cancer
Outsourcing eureka moments to artificial intelligence
Challenges and opportunities in quantum machine learning
A handy meta-analysis tool for IBD research
Integrating computational and experimental worlds
Homeostatic coordination and up-regulation of neural activity by activity-dependent myelination
Accelerating discovery in organic redox flow batteries
AI improves the design of urban communities
Chemical reaction networks and opportunities for machine learning
Computing optimal carbon dioxide removal portfolios
Efficient modeling of ionic and electronic interactions by a resistive memory-based reservoir graph neural network
Crafting molecular architectures with guided diffusion
Spatial redundancy transformer for self-supervised fluorescence image denoising
Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond
Demand-driven design of bicycle infrastructure networks for improved urban bikeability
Harnessing low dimensionality to visualize the antibody-virus landscape for influenza
Harnessing quantum information to advance computing
Stabilizing quantum GANs
Redefining false discoveries in cancer data analyses
Towards a purely physics-based computational binding affinity estimation
Connecting Black women in computational biology
One algorithm to play them all
Evolving wave networks for materials design
Time- and memory-efficient genome assembly with Raven
Computational design of art-inspired metamaterials
Benchmarking the performance of quantum computing software for quantum circuit creation, manipulation and compilation
Bringing the pulsatile release system to light
High performance computing framework for tera-scale database search of mass spectrometry data
Periodicity-aware deep learning for polymers
Model supports storied social network theory
Boosting AI with neuromorphic computing
A multi-task learning strategy to pretrain models for medical image analysis
Boosting graph neural networks with virtual nodes to predict phonon properties
On the design of solid-state Li-ion batteries
Unlocking T-cell receptor-epitope insights with structural analysis
Machine learning for deciphering cell heterogeneity and gene regulation
Modeling higher-order lattice anharmonicity
Scalable design of orthogonal DNA barcode libraries
Similarity-driven multi-view embeddings from high-dimensional biomedical data
On the path toward brain-scale simulations
Iterative variational learning of committor-consistent transition pathways using artificial neural networks
An efficient and effective method to identify significantly perturbed subnetworks in cancer
Learning properties of ordered and disordered materials from multi-fidelity data
Spatial modeling algorithms for reactions and transport in biological cells
Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data
A multidimensional dataset for structure-based machine learning
Computing the relative binding affinity of ligands based on a pairwise binding comparison network
Supervised learning of a chemistry functional with damped dispersion
The landscape of computational approaches for artificial photosynthesis
Generative aptamer discovery using RaptGen
Learning properties of metal complexes
A versatile model for single-cell data analysis
Climatic signatures in the different COVID-19 pandemic waves across both hemispheres
In search of the most cooperative network
Formats for reporting primary research
Cultural diversity through the lenses of ecology
Fast intratumor heterogeneity inference from single-cell sequencing data
Energy materials screening with defect graph neural networks
Smart lockdowns to control COVID-19
Moving towards reproducible machine learning
Pushing the bounds of multiconfigurational methods
The next seven years
A machine learning-based model for the quantification of mental conflict
Author Correction: Balancing efficient analysis and storage of quantitative genomics data with the D4 format and d4tools
Simulating short-range order in compositionally complex materials
A resampling-based approach to share reference panels
For the greatest benefit to humankind
DNA data storage for biomedical images using HELIX
A probabilistic graphical model foundation for enabling predictive digital twins at scale
Publisher Correction: Rotamer-free protein sequence design based on deep learning and self-consistency
Author Correction: Dynamic forecasting of severe acute graft-versus- host disease after transplantation
Leveraging remeasured samples in biomedical studies
Boosting power for time-to-event GWAS analysis affected by case ascertainment
Dimensionality reduction of calcium-imaged neuronal population activity
Heat wave attribution assessment using deep learning
A formally exact method for high-throughput absolute binding-free-energy calculations
A look back at 2021
Putting a spotlight on diversity, equity, and inclusion
Optimal national prioritization policies for hospital care during the SARS-CoV-2 pandemic
Accelerating the calculation of electron-phonon coupling strength with machine learning
On writing accessible computational science papers
Using fast and slow data to unfold city dynamics
Harnessing the power of DNA for computing
Maximally localized dynamical quantum embedding for solving many-body correlated systems
Are quantum computers really energy efficient?
Drug discovery with limited resources
Investigating the volume and diversity of data needed for generalizable antibody-antigen ΔΔG prediction
Enabling comparative gene regulatory network analysis on single-cell data with SCORPION
Collaborations drive energy storage research
Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction
Can machines learn with hard constraints?
Cell clustering for spatial transcriptomics data with graph neural networks
Generalizing deep learning electronic structure calculation to the plane-wave basis
To V or not to V
Publisher Correction: A rigid body framework for multicellular modeling
Effectively detecting anomalous diffusion via deep learning
Author Correction: Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning
Accelerating computational modeling and design of high-entropy alloys
Computational morphology and morphogenesis for empowering soft-matter engineering
Graphing cell relations in spatial transcriptomics
Towards quantum computing of turbulence
Revelation of hidden 2D atmospheric turbulence strength fields from turbulence effects in infrared imaging
Virtual brain twins for stimulation in epilepsy
The zettabyte era is in our DNA
The future of machine learning for small-molecule drug discovery will be driven by data
An error correction strategy for image reconstruction by DNA sequencing microscopy