Jianlin Cheng
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View article: ETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms
ETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms Open
Cryogenic Electron Tomography (cryo-ET) is an emerging experimental technique to visualize cell structures and macromolecules in their native cellular environment. Accurate segmentation of cell structures in cryo-ET tomograms, such as cell…
View article: Boosting AlphaFold protein tertiary structure prediction through MSA engineering and extensive model sampling and ranking in CASP16
Boosting AlphaFold protein tertiary structure prediction through MSA engineering and extensive model sampling and ranking in CASP16 Open
AlphaFold2 and AlphaFold3 have revolutionized protein structure prediction by enabling high-accuracy structure predictions for most single-chain proteins. However, obtaining high-quality predictions for difficult targets with shallow or no…
View article: Enhancer Rewiring Orchestrates Inflammation and Loss of Cell Identity During Muscle Stem Cell Aging
Enhancer Rewiring Orchestrates Inflammation and Loss of Cell Identity During Muscle Stem Cell Aging Open
Loss of regeneration is a key feature of aging organs, often linked to stem cell exhaustion. Skeletal muscle stem cells (MuSCs) undergo age‐related numerical and functional decline, contributing to reduced regenerative potential. Using low…
View article: Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination
Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination Open
Cryo-electron microscopy (cryo-EM) is a key technology for determining the structures of proteins, particularly large protein complexes. However, automatically building high-accuracy protein structures from cryo-EM density maps remains a c…
View article: MPBind: a multitask protein binding site predictor using protein language models and equivariant GNNs
MPBind: a multitask protein binding site predictor using protein language models and equivariant GNNs Open
Motivation Proteins interact with a variety of molecules, including other proteins, DNAs, RNAs, ligands, ions, and lipids. These interactions play a crucial role in cellular communication, metabolic regulation, gene regulation, and structu…
View article: Beckwith–Wiedemann syndrome and large offspring syndrome involve alterations in methylome, transcriptome, and chromatin configuration
Beckwith–Wiedemann syndrome and large offspring syndrome involve alterations in methylome, transcriptome, and chromatin configuration Open
Beckwith–Wiedemann syndrome (BWS) is the most common epigenetic overgrowth syndrome, caused by epigenetic alterations on chromosome 11p15. In ∼50% of patients with BWS, the imprinted region KvDMR1 (IC2) is hypomethylated. Nearly, all child…
View article: How to go with the flow: flow matching in bioinformatics and computational biology
How to go with the flow: flow matching in bioinformatics and computational biology Open
View article: CryoFSL: An Annotation-Efficient, Few-Shot Learning Framework for Robust Protein Particle Picking in Cryo-EM Micrographs
CryoFSL: An Annotation-Efficient, Few-Shot Learning Framework for Robust Protein Particle Picking in Cryo-EM Micrographs Open
A bstract Accurate identification of protein particles in cryo-electron microscopy (cryo-EM) micrographs is crucial for high-resolution structure determination, but remains challenging due to the heavy reliance on extensive annotated datas…
View article: How to go with the flow: flow matching in bioinformatics and computational biology
How to go with the flow: flow matching in bioinformatics and computational biology Open
View article: Machine learning methods for gene regulatory network inference
Machine learning methods for gene regulatory network inference Open
Gene Regulatory Networks (GRNs) are intricate biological systems that control gene expression and regulation in response to environmental and developmental cues. Advances in computational biology, coupled with high-throughput sequencing te…
View article: Assessing the potential of deep learning for protein-ligand docking.
Assessing the potential of deep learning for protein-ligand docking. Open
The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning …
View article: How to go with the flow: flow matching in bioinformatics and computational biology
How to go with the flow: flow matching in bioinformatics and computational biology Open
View article: Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination
Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination Open
Cryo-electron microscopy (cryo-EM) is a key technology for determining the structures of proteins, particularly large protein complexes. However, automatically building high-accuracy protein structures from cryo-EM density maps remains a c…
View article: Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination
Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination Open
Cryo-electron microscopy (cryo-EM) is a key technology for determining the structures of proteins, particularly large protein complexes. However, automatically building high-accuracy protein structures from cryo-EM density maps remains a c…
View article: <scp>FlowDock</scp>: Geometric flow matching for generative protein–ligand docking and affinity prediction
<span>FlowDock</span>: Geometric flow matching for generative protein–ligand docking and affinity prediction Open
Motivation Powerful generative AI models of protein–ligand structure have recently been proposed, but few of these methods support both flexible protein–ligand docking and affinity estimation. Of those that do, none can directly model mult…
View article: MegaFold: System-Level Optimizations for Accelerating Protein Structure Prediction Models
MegaFold: System-Level Optimizations for Accelerating Protein Structure Prediction Models Open
Protein structure prediction models such as AlphaFold3 (AF3) push the frontier of biomolecular modeling by incorporating science-informed architectural changes to the transformer architecture. However, these advances come at a steep system…
View article: Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking inCASP16
Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking inCASP16 Open
View article: Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking in CASP16
Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking in CASP16 Open
AlphaFold2 and AlphaFold3 have revolutionized protein structure prediction by enabling high-accuracy tertiary structure predictions for most single-chain proteins. However, obtaining high-quality predictions for some hard protein targets w…
View article: Improving <scp>AlphaFold2</scp> ‐ and <scp>AlphaFold3</scp> ‐Based Protein Complex Structure Prediction With <scp>MULTICOM4</scp> in <scp>CASP16</scp>
Improving <span>AlphaFold2</span> ‐ and <span>AlphaFold3</span> ‐Based Protein Complex Structure Prediction With <span>MULTICOM4</span> in <span>CASP16</span> Open
With AlphaFold achieving high‐accuracy tertiary structure prediction for most single‐chain proteins (monomers), the next major challenge in protein structure prediction is to accurately model multichain protein complexes (multimers). We de…
View article: A unified multimodal model for generalizable zero-shot and supervised protein function prediction
A unified multimodal model for generalizable zero-shot and supervised protein function prediction Open
Predicting protein function is a fundamental yet challenging task that requires integrating diverse biological data modalities to capture complex functional relationships. Traditional machine learning methods often rely on single modalitie…
View article: Global modulation of gene expression and transcriptome size in aneuploid combinations of maize
Global modulation of gene expression and transcriptome size in aneuploid combinations of maize Open
Genomic imbalance refers to the more severe phenotypic consequences of changing a single chromosome compared to changing the whole genomic set. Previous genomic imbalance studies in maize have identified gene expression modulation in aneup…
View article: MPBind: Multitask Protein Binding Site Prediction by Protein Language Models and Equivariant Graph Neural Networks
MPBind: Multitask Protein Binding Site Prediction by Protein Language Models and Equivariant Graph Neural Networks Open
Proteins interact with a variety of molecules, including other proteins, DNAs, RNAs, ligands, ions, and lipids. These interactions play a crucial role in cellular communication, metabolic regulation, immune response, and structural integri…
View article: Protein‐Ligand Structure and Affinity Prediction in <scp>CASP16</scp> Using a Geometric Deep Learning Ensemble and Flow Matching
Protein‐Ligand Structure and Affinity Prediction in <span>CASP16</span> Using a Geometric Deep Learning Ensemble and Flow Matching Open
Predicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein‐ligand structure (poses) produced by the latest…
View article: Starfish-inspired wearable bioelectronic systems for physiological signal monitoring during motion and real-time heart disease diagnosis
Starfish-inspired wearable bioelectronic systems for physiological signal monitoring during motion and real-time heart disease diagnosis Open
Soft bioelectronics enable noninvasive, continuous monitoring of physiological signals, essential for precision health care. However, capturing biosignals during physical activity, particularly biomechanical signals like cardiac mechanics,…
View article: FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction.
FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction. Open
In this work, we propose FlowDock, the first deep geometric generative model based on conditional flow matching that learns to directly map unbound (apo) structures to their bound (holo) counterparts for an arbitrary number of binding liga…
View article: Rapamycin enhances neurovascular, peripheral metabolic, and immune function in cognitively normal, middle-aged APOE4 Carriers: genotype-dependent effects compared to non-carriers
Rapamycin enhances neurovascular, peripheral metabolic, and immune function in cognitively normal, middle-aged APOE4 Carriers: genotype-dependent effects compared to non-carriers Open
View article: A Labeled Dataset for AI-based Cryo-EM Map Enhancement
A Labeled Dataset for AI-based Cryo-EM Map Enhancement Open
Cryo-electron microscopy (cryo-EM) has transformed structural biology by enabling near-atomic resolution imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from structural sources, shot n…
View article: Atomic Protein Structure Modeling from Cryo-EM Using Multi-Modal Deep Learning and AlphaFold3
Atomic Protein Structure Modeling from Cryo-EM Using Multi-Modal Deep Learning and AlphaFold3 Open
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling near-atomic resolution visualization of protein structures. However, accurately modeling 3D atomic structures from cryo-EM density maps remains challengin…
View article: Improving AlphaFold2 and 3-based protein complex structure prediction with MULTICOM4 in CASP16
Improving AlphaFold2 and 3-based protein complex structure prediction with MULTICOM4 in CASP16 Open
With AlphaFold achieving high-accuracy tertiary structure prediction for most single-chain proteins (monomers), the next major challenge in protein structure prediction is accurately modeling multi-chain protein complexes (multimers). We d…
View article: Author response: Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos
Author response: Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos Open