Hongyang Li

[The Innovation | Informatics @Innov_Inf]

Machine learning for artemisinin resistance in malaria treatment across in vivo-in vitro platforms: iScience

#news #Trending #AI #business #programming #Technology #DataScience #research @The_InnovationJ Tweets by @YangLiu_CTH
https://t.co/yas2xifyO4

[The Innovation | Informatics @Innov_Inf]

Auto-annotating sleep stages based on polysomnographic data: Patterns

#news #Trending #AI #business #programming #Technology #DataScience #research @The_InnovationJ Tweets by @YangLiu_CTH
https://t.co/z0lF8ioVFe

[GigaScience @GigaScience]

Presenting the winning DREAM Olfaction Prediction Challenge algorithm: Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features https://t.co/zXBcvm5R9r

[TF binding papers 📚 NucPosDB database @TF_binding_bot]

Anchor: Trans-cell type prediction of transcription factor binding sites. https://t.co/0aQyPAY37c

[Gene Regulation @generegulation]

Evidence of widespread, independent sequence signature for transcription factor cobinding https://t.co/mSsZUOOtzr
Motifs associated with TF-TF co-binding:
(1) TF-pair specific (see table)
(2) TF-specific (see table)
(3) TF-generic (e.g. GABPA, JUND, TAF1, and HNF4A)
#TFbinding

[BioDecoded @BioDecoded]

Asymmetric predictive relationships across histone modifications | Nature Machine Intelligence
https://t.co/9rwIskP5vT

#MachineLearning

[Communications Biology @CommsBio]

@HongyangLi4 and @gyuanfan present an award winning algorithm for automatically segmenting sleep arousal regions based on polysomnographic recordings, enabling fast and accurate delineation of sleep arousal events.https://t.co/G65wixWVrr

[TF binding papers 📚 NucPosDB database @TF_binding_bot]

Fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution https://t.co/BqeMohNs9S

[Radiology: Artificial Intelligence @Radiology_AI]

#DeepLearning automatic segmentation of brain gliomas is efficient and accurate in multimodal MRI scans https://t.co/R4vl9RyqCe @markutz556 @HongyangLi4 @danqAI @gyuanfan

[BMC Biology @BMCBiology]

NEW #research from Hongyang Li and colleagues at @UMich: Joint learning improves protein abundance prediction in #cancers

Read more here: https://t.co/YdxTNY7YvM

[Hongyang Li @HongyangLi4]

Leopard: fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution https://t.co/tQtvhpdea0

[Nature Machine Intelligence @NatMachIntell]

"Waking up to data challenges" Researcher Yuanfang Guan from @UMich explains how her group benefits from taking part in of data challenges, which give young scientists a chance to dive into interesting new problems and demonstrate their skills https://t.co/1pij4ML1oo