THBKG：用于决策对齐的临床进展预测的时间生物医学知识图谱
THBKG: A Temporal Biomedical Knowledge Graph for Decision-Aligned Clinical Advancement Prediction
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中文总结 AI 辅助
该研究提出THBKG时间生物医学知识图谱，通过图传播方法预测靶点-疾病对的临床进展，在无直接证据的对中表现优异，还提供可解释预测的路径解释器。
中文摘要 AI 辅助
40%-50%的II期疗效失败源于靶点-疾病关联不足，因此预测哪些项目将推进可让资助方支持最有可能惠及患者的假设。项目的判断依据是其进入临床时支持关联的证据。目前尚无生物医学知识图谱能按过去日期组装该证据图谱。我们提出时间异质性生物医学知识图谱（THBKG），用于随时间描述和预测治疗靶点-疾病关联：包含110396个实体、1110万条边，涉及19种关系类型，每条边携带证据变更年份，因此可恢复某对关联在决策到期时的状态。基于该图谱，我们定义了决策对齐基准，用于预测进入II期的靶点-疾病对是否基于决策前可追溯的证据推进至III期。在THBKG上的图传播方法在相同决策对齐协议下优于所有直接证据参考，在每个治疗领域排名前10的对中达到4.3-4.5的相对成功率。该增益集中在决策点无直接靶点-疾病证据的72.8%的对上，这类对的直接边模型无数据可读：编码器仍能以5-6倍于随机的排名，通过中间生物学传播恢复信号。将基于路径的解释器适配到决策时间子图，可将每个预测分解为假设背后的证据图谱，实现可解释预测。我们发布THBKG作为持续更新的基础，用于通过回顾性验证研究治疗靶点假设。
英文摘要
Inadequate target--disease linkage accounts for 40--50\% of Phase~II efficacy failures, so anticipating which programmes will advance would let sponsors back the hypotheses most likely to reach patients. What a programme can be judged on is the evidence that supported its linkage \emph{when it entered the clinic}. No existing biomedical knowledge graph allows that evidence profile to be assembled as of a past date. We present the Temporal Heterogeneous Biomedical Knowledge Graph (THBKG), which describes and predicts therapeutic target--disease links through time: 110,396 entities and 11.1M edges across nineteen relation types, each edge carrying the year its evidence changed, so a pair's profile can be recovered as it stood when its own decision fell due. On this graph we define a decision-aligned benchmark that predicts, for a target--disease pair entering Phase~II, whether it advances to Phase~III on evidence datable before that decision. Graph propagation over the THBKG outranks every direct-evidence reference scored under the same decision-aligned protocol, reaching a relative success of 4.3--4.5 at the top ten pairs per therapeutic area. The gain concentrates on the 72.8\% of pairs with no direct target--disease evidence at their decision point, where a direct-edge model has nothing to read: the encoders still rank five- to sixfold above chance, recovering the signal by propagating over the intervening biology. Adapting a path-based explainer to the decision-time subgraph decomposes each prediction into the evidence landscape behind the hypothesis for explainable prediction. We release the THBKG as a continually updated substrate for studying therapeutic target hypotheses by retrospective validation.
发表机构
- Queen Mary University of London（伦敦玛丽女王大学）
- Recursion Pharmaceuticals Inc.（Recursion制药公司）
机构由 AI 辅助整理，请以论文原文为准。
