Meet the authors: Hanchuan Peng, Peng Xie, and Feng Xiong [0.03%]
作者简介：Hanchuan Peng、Peng Xie 和 Feng Xiong
Hanchuan Peng,Peng Xie,Feng Xiong
Hanchuan Peng
In a recent paper at Patterns, Hanchuan Peng, Peng Xie, and Feng Xiong from Southeast University describe a deep learning method to characterize complete single-neuron morphologies, which can discover neuron projection patterns of diverse c...
FHBF: Federated hybrid boosted forests with dropout rates for supervised learning tasks across highly imbalanced clinical datasets [0.03%]
FHBF：具有dropout率的联邦混合提升森林，用于高度不平衡的临床数据集上的监督学习任务
Vasileios C Pezoulas,Fanis Kalatzis,Themis P Exarchos et al.
Vasileios C Pezoulas et al.
Although several studies have deployed gradient boosting trees (GBT) as a robust classifier for federated learning tasks (federated GBT [FGBT]), even with dropout rates (federated gradient boosting trees with dropout rate [FDART]), none of ...
DSM: Deep sequential model for complete neuronal morphology representation and feature extraction [0.03%]
DSM：完整神经形态表示和特征提取的深度顺序模型
Feng Xiong,Peng Xie,Zuohan Zhao et al.
Feng Xiong et al.
The full morphology of single neurons is indispensable for understanding cell types, the basic building blocks in brains. Projecting trajectories are critical to extracting biologically relevant information from neuron morphologies, as they...
Looking forward to the new year [0.03%]
展望新年
Andrew L Hufton
Andrew L Hufton
AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data [0.03%]
AlphaML：一个清晰、易读、可解释、透明和明确的表格数据二元分类平台
Ahmad Nasimian,Saleena Younus,Özge Tatli et al.
Ahmad Nasimian et al.
Leveraging the potential of machine learning and recognizing the broad applications of binary classification, it becomes essential to develop platforms that are not only powerful but also transparent, interpretable, and user friendly. We in...
Fengda Zhang,Zitao Shuai,Kun Kuang et al.
Fengda Zhang et al.
Federated learning (FL) is a promising approach for healthcare institutions to train high-quality medical models collaboratively while protecting sensitive data privacy. However, FL models encounter fairness issues at diverse levels, leadin...
Functional microRNA-targeting drug discovery by graph-based deep learning [0.03%]
基于图的深度学习在功能microRNA靶标药物发现中的应用
Arash Keshavarzi Arshadi,Milad Salem,Heather Karner et al.
Arash Keshavarzi Arshadi et al.
MicroRNAs are recognized as key drivers in many cancers but targeting them with small molecules remains a challenge. We present RiboStrike, a deep-learning framework that identifies small molecules against specific microRNAs. To demonstrate...
Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT [0.03%]
使用大型语言模型改进临床记录中的表型识别：PhenoBCBERT 和 PhenoGPT
Jingye Yang,Cong Liu,Wendy Deng et al.
Jingye Yang et al.
To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models-PhenoBCBERT and PhenoGPT-for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for ...
Shifting your research from X to Mastodon? Here's what you need to know [0.03%]
将研究从X转移到Mastodon？你需要知道的内容
Roel Roscam Abbing,Robert W Gehl
Roel Roscam Abbing
Since Elon Musk's purchase of Twitter/X and subsequent changes to that platform, computational social science researchers may be considering shifting their research programs to Mastodon and the fediverse. This article sounds several notes o...
LATTE: Label-efficient incident phenotyping from longitudinal electronic health records [0.03%]
LATTE：从纵向电子健康记录中进行标签高效的事件表型分析
Jun Wen,Jue Hou,Clara-Lea Bonzel et al.
Jun Wen et al.
Electronic health record (EHR) data are increasingly used to support real-world evidence studies but are limited by the lack of precise timings of clinical events. Here, we propose a label-efficient incident phenotyping (LATTE) algorithm to...