Why BGPT?
logo

Assess an author's data and outputs

See the raw experimental evidence behind an author's publications and reproducibility signals.







Press Enter ↵ to solve



    Fuel Your Discoveries




     Quick Explanation


    Yong Yu — scientific strength snapshot
    • Strong cross-domain experimental + computational output (tumor immunology/spatial omics, RCC metabolism, fungal nutrient sensing, molecular-glue toxicity, regulatory-embedding ML, etc.).
    • Most convincing work in mechanistic biology: e.g., epithelial–myeloid immunosuppression via ANXA1–FPR signaling with spatial omics and combination intervention in mice
    • Some papers are primarily in vitro / proxy-based (e.g., PPI from sequence embeddings; cell-growth chemical analogs), where generalizability and direct causal validation can be weaker



     Long Explanation


    Author Review: Yong Yu
    Evidence base: the dataset you provided lists multiple publications attributed to “Yong Yu” with DOIs, experimental summaries, limitations, and (sometimes) data availability details. This review therefore evaluates scientific strength only from that provided evidence, without claiming additional author-wide metrics (OpenAlex timed out).
    1) Visual evidence dashboard (from your provided paper excerpts)
    2) What the evidence suggests about Yong Yu’s scientific strengths
    A. Mechanistic, multi-level validation (human + in vivo + molecular hooks)
    The clearest strength in the provided material is mechanistic layering: (i) human or multi-omic mapping, (ii) spatial or systems-level correlation, and (iii) targeted perturbation with rescue/functional endpoints.
    • OSCC spatial heterogeneity: links stage-dependent MDSC spatial redistribution to ANXA1–FPR subtype signaling and tests FPR2 blockade plus anti–PD-1 in mouse models . Main caution: the provided excerpt notes cross-sectional sampling (limited longitudinal causal inference) and computational inference dependence for ligand–receptor interaction .
    • Sepsis immunosuppression: integrates observational human cohorts (MIMIC-IV, prospective flow, CD8 RNA-seq) with murine sepsis and adoptive transfer; proposes mechanistic control via HDAC1–NFAT1 at the PDCD1 promoter and supports functional reversal by HDAC1 inhibition . Main caution: preclinical therapeutic targeting is constrained by clinical causality (observational human data) and model generalization .
    • Molecular-glue safety/on-target toxicity: uses engineered humanized CRBN mouse models, then demonstrates that protection by a non-degradable GSPT1 allele rescues toxicity—this is a strong on-target validation pattern .
    B. Breadth across biological scales (cell, organism, and in silico)
    The provided set spans: (i) immune microenvironments in cancer (OSCC), (ii) metabolic transcriptional control in ccRCC (YBX1–LDHA–NF-κB), (iii) fungal nutrient sensing and virulence (Itr4), (iv) structural/biochemical protein complexes (26S proteasome recognition), (v) immunology in sepsis, and (vi) ML tooling for regulatory embeddings (ChromBERT-tools).
    • Fungal virulence via inositol sensing: frames Itr4 as a “transceptor,” combines genetics, binding/uptake assays, and infection phenotypes .
    • Regulatory embedding tooling: ChromBERT-tools provides a pipeline for context-specific embeddings and demonstrates performance on BRD4 cistrome imputation with reported AUPRC improvements vs baselines . Main caution: embedding similarity is a proxy and validation is limited to selected cell types in the provided excerpt .
    C. Reproducibility signals vary by study type
    From your provided metadata, reproducibility-like scores appear higher for some “mechanistic and data-rich” experimental works than for proxy-only computational studies. For example, the GSPT1 toxicity paper is listed with repro score 9 , whereas some computational tool performance scores are more moderate (e.g., repro score 7 for ChromBERT-tools) .
    3) Risks, blind spots, and where the evidence is weaker
    • Cross-sectional inferential gaps: OSCC spatial work uses early/late-stage snapshots; redistribution over “time” is inferred, not tracked longitudinally in the provided excerpt .
    • Proxy interaction inference: ligand–receptor maps via computational frameworks (CellPhoneDB-like approaches) can over-interpret colocalization and expression without direct binding or functional perturbation of the interaction itself .
    • Model generalization: sepsis work uses a specific pneumonia-driven model and scRNA-seq with n=8 mice; thus HDAC1 dynamics/exhaustion mechanisms may differ across sepsis etiologies .
    • Public data gaps: not all provided items mention open accession numbers (e.g., some in vitro chemical analog studies); in such cases, reproducibility assurance is weaker .
    • Computational performance ≠ causality: regulator embedding/imputation and PPI-from-sequence approaches can produce high benchmark metrics but remain proxy-based; external validation breadth matters .
    4) “Known vs inferred” mapping (from provided excerpts)
    This graph is not a new measurement: it translates the provided “limitations/biases” notes into a qualitative emphasis on direct vs inferential evidence. For instance, OSCC’s ligand–receptor inference is explicitly described as assumption-dependent .
    5) What would most effectively disprove the strongest claims (falsification targets)
    For OSCC ANXA1–FPR2 axis
    • Show that blocking FPR2 does not reduce MDSC accumulation or CD8 activity, and that improved anti–PD-1 efficacy does not occur in independent OSCC models .
    For HDAC1/NFAT1-mediated CD8 exhaustion in sepsis
    • Show that HDAC1 does not upregulate with exhaustion markers across cohorts/models, and that inhibiting HDAC1 does not restore CD8 function or survival .
    For GSPT1 on-target toxicity of CRBN molecular glues
    • Demonstrate that toxicity persists even when endogenous GSPT1 cannot be degraded, undermining the on-target causal link .
    6) Confidence statement about this review
    Confidence is moderate because your OpenAlex query timed out (so author-wide citation metrics weren’t pulled), and because the evidence is drawn from the provided per-paper summaries rather than full-text direct verification. However, several items include explicit limitations and data deposition statements, letting us judge rigor-to-inference balance directly from the excerpts.


    Feedback:    

    Updated: April 08, 2026

     Top Data Sources ExportMCP


     Analysis Wizard



    It will assemble a cross-paper dataframe of provided scores, sample sizes, and validation types, then generate Plotly dashboards comparing direct perturbation evidence versus proxy inference across Yong Yu publications.



     Hypothesis Graveyard


    A simple “only PD-1 explains exhaustion” model is unlikely: the sepsis excerpt emphasizes AP-1/NFAT balance and promoter occupancy beyond PD-1 alone .


    A “ligand–receptor inference alone” model for OSCC spatial immunosuppression is less favored because the excerpt includes FPR2 blockade combined with anti–PD-1 and changes in immune cell infiltration—suggesting a functional role beyond computational association .

     Science Art


    Author Review: Yong Yu Science Art

     Science Movie


    Make a narrated HD Science movie for this answer ($32 per minute)




     Discussion








    Get Ahead With Science Insights

    Custom summaries of the latest cutting edge Science research. Every Friday. No Ads.


    My BGPT






     Trending