MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2018). CAS PubMed Google Scholar Kang, B., Seok, C. & Lee, J. Prediction of molecular electronic transitions using random forests. J. Chem. Inf. Model. 60, 5984–5994 (2020). CAS PubMed Google ...
The remainder of this article is structured as follows: First, we give a brief overview over machine learning methods for protein-RNA interaction prediction with a focus on input modalities and deep learning model architectures. Next, we cover benchmarking datasets and their preprocessing, before int...
In this work, we introduce MOlecular SEtS (MOSES), a benchmarking platform to support research on machine learning for drug discovery. MOSES implements several popular molecular generation models and includes a set of metrics that evaluate the diversity and quality of generated molecules. MOSES is ...
BenchmarkBig dataBiological assaysDatabaseDrug discoveryMachine learningPubChemDeep learning's automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from ...
联邦学习——论文研究(FedML: A Research Library and Benchmark for Federated Machine Learning),主要内容:该篇论文提出了一个联邦学习框架——FedML,该框架支持三种计算范式:on-devicetrainingforedgedevicesdistributedcomputingsingle-machinesimulation强调联邦
🤖🌄 KG-driven Multi-modal Learning (KG4MM)Understanding & Reasoning Tasks👈 🔎 Pipeline Visual Question Answering👈 🔎 Benchmarks [arXiv 2023] Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering. [arXiv 2023] Open-Set Knowledge-Based Visual ...
A benchmark dataset for Machine Learning emulation of atmospheric radiative transfer in weather and climate models (NeurIPS 2021 Datasets and Benchmarks Track) Topics machine-learning emulation pytorch radiative-transfer dataset neural-networks atmospheric-science climate-change distributional-shift climart ...
and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect ...
1. Machine learning in bioinformatics Machine learning techniques have found widespread application in bioinformatics [1]. The diverse range of rapidly expanding data produced by modern molecular biology has fuelled a need for accurate classification and prediction algorithms. The accuracy of classifi...
[11] Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande, MoleculeNet: A Benchmark for Molecular Machine Learning, arXiv preprint, arXiv: 1703.00564, 2017. [12] J. J. Irwin, T. Sterling, M. M. Mysinger, E. ...