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Found 21 papers, 10 papers with code
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning
In this paper, we define a novel causal graph partition that allows for divide-and-conquer causal discovery with theoretical guarantees.
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries.
WordScape: a Pipeline to extract multilingual, visually rich Documents with Layout Annotations from Web Crawl Data
Together with the pipeline, we will additionally release 9. 5M urls to word documents which can be processed using WordScape to create a dataset of over 40M pages.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery
Causal discovery of genome-scale networks is important for identifying pathways from genes to observable traits - e. g. differences in cell function, disease, drug resistance and others.
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery
Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design.
Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening
Our analysis of the speedup explains that to screen more molecules under a docking paradigm, another order of magnitude speedup must come from model accuracy rather than computing speed (which, if increased, will not anymore alter our throughput to screen molecules).
Neko: a Library for Exploring Neuromorphic Learning Rules
To address this gap, we present Neko, a modular, extensible library with a focus on aiding the design of new learning algorithms.
A cross-study analysis of drug response prediction in cancer cell lines
To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI.
Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds
Molecules have seemed like a natural fit to deep learning's tendency to handle a complex structure through representation learning, given enough data.
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow.
Learning Curves for Drug Response Prediction in Cancer Cell Lines
In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training sizes for two of the datasets, whereas the mNN performs better at the higher range of training sizes.
Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models
We present a new method for understanding the performance of a model in virtual drug screening tasks.
Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release
Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response
Previous transfer learning studies for drug response prediction focused on building models that predict the response of tumor cells to a specific drug treatment.
Deep Medical Image Analysis with Representation Learning and Neuromorphic Computing
We explore three representative lines of research and demonstrate the utility of our methods on a classification benchmark of brain cancer MRI data.
A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning
By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance drug response predictions.
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research
Cancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power.
Precision Medicine as an Accelerator for Next Generation Cognitive Supercomputing
In the past several years, we have taken advantage of a number of opportunities to advance the intersection of next generation high-performance computing AI and big data technologies through partnerships in precision medicine.
Machine Learning for Antimicrobial Resistance
Biological datasets amenable to applied machine learning are more available today than ever before, yet they lack adequate representation in the Data-for-Good community.
