에이전트 기반 모델링에서 생물학 세부 분야 간 제한된 지식 교환: LLM 기반 서지학적 분석

폐루프 자극을 통한 LLM의 마음 통제: 분포 외 견고성

Cross-cancer knowledge transfer for lymph node metastasis prediction: Applicability of an axillary lymph node-based deep learning model

Evaluation of lymph node (LN) metastasis is critical for cancer staging and treatment planning. Since radiologic hallmarks of metastatic LNs are shared across primary cancers, a computed tomography (CT)-based deep learning (DL) classifier developed from one anatomy may transfer to others. This study built a DenseNet-121 ensemble model using clinical-implication-applied preprocessing and evaluated its cross-anatomical generalizability to LNs from diverse primary cancers. A DenseNet-121 ensemble model combining fixed-size cropping (FSC) and adjustable square cropping (ASC) was trained from ImageNet initialization on an axillary lymph node (ALN) dataset (n = 1,128 from 523 breast cancer patients). The model utilized clinical-implication-applied perinodal cropping with bicubic filtering and standardized resizing. To test cross-anatomical generalizability, an external dataset of 256 CT images across four anatomical regions was evaluated: cervical, mediastinal, upper-abdominal, and lower-abdominal LNs. Perinodal free margins were systematically varied from 10% to 40% to optimize the balance between context preservation and surround interference. Bicubic filtering and standardized resizin

A protein language model for domain, functional site, and variant effect prediction

Protein sequence databases are growing far faster than experimentally curated annotations, limiting functional interpretation of newly identified proteins and coding variants. We developed PALM, a protein annotation language model that jointly predicts domain and functional-site occurrence, while preserving overlapping and nested annotations through a soft-target objective. PALM integrates ESM2 with low-rank adaptation (LoRA), learned layer aggregation, and a bidirectional long short-term memory (BiLSTM) encoder. PALM was trained on ~15 million InterPro-annotated UniRef50 sequences spanning 17,951 domain and 1,383 site classes. Across 2.3 million held-out proteins, PALM achieved domain- and site-occurrence F1 scores of 0.941 and 0.877, respectively, and retained substantial performance in proteins with <20% sequence identity to the training and validation data. PALM also substantially outperformed ESM3 annotation baseline. Without training on variant-pathogenicity labels, PALM embedding disruption distinguished 151,823 ClinVar missense variants more accurately than ESM2 embedding disruption (AUROC 0.835 versus 0.710), and combining PALM embedding disruption with the ESM2 likelihood

Harnessing catalytic knowledge to guide enzyme discovery and evolution with AlphaEnzyme

Retrieving an enzyme for a target reaction provides a starting point for evolution, but predicting how to improve it remains difficult. This requires connecting evolutionary information with the structural and chemical context of catalysis. Here we present AlphaEnzyme, an enzyme foundation model built on a structure-grounded representation of an enzyme paired with its reaction, from which catalytic compatibility, kinetics and mutational response are read out together. On a time-separated benchmark, AlphaEnzyme achieves a top-1 catalyst retrieval success rate of 30.1%, against 17.2% for the strongest baseline. Michaelis constant predictions achieve the highest rank correlations among the compared methods, reaching 0.64 for enzymes sharing at most 40% identity with the kinetic training set. The model also enriches high-fitness TrpB variants at a budget of 16 candidates without target-specific fitness training. In prospective transaminase engineering, several prioritized variants exceed the parent, with the best design yielding a 30.4-fold increase in product signal in a lysate-based assay while retaining (R)-selectivity. Together, AlphaEnzyme provides a common molecular basis for sel

CARTnet: Template-guided B-cell Epitope Prediction with Contact-aware Attention

B-cell epitope prediction remains challenging due to the limited availability of experimentally characterized data and the incomplete understanding of antibody-antigen recognition. Here, we present CARTnet, a novel contact-aware deep learning framework that integrates multimodal features and template-derived epitope information. CARTnet demonstrated superior performance when compared to the existing state-of-the-art methods across most evaluation metrics on both the internal and external test datasets. In particular, CARTnet achieved an AUPRC of 0.243 on the internal test set, compared with 0.220 for DiscoTope-3, 0.140 for BepiPred-2, and 0.198 for BepiPred-3. This improvement was also observed on the external test set, where CARTnet achieved an AUPRC of 0.267, compared with 0.192, 0.111, and 0.215 for DiscoTope-3, BepiPred-2, and BepiPred-3, respectively, supporting its generalizability. CARTnet also supports template-guided prediction, in which limited prior epitope information can improve the identification of additional epitope residues; a single template residue increased the AUPRC of the template-based variant from 0.225 to 0.403 on the internal test set. Furthermore, we demo

FeatureMAP, 비표적 대사체학의 네트워크 기반 해석 가능

비표적 액체 크로마토그래피-질량 분석(LC-MS) 대사체학은 수천 개의 피크(features)를 생성하지만, 불완전한 대사체 주석(annotation)과 사전 정의된 경로에 대한 의존성으로 인해 생물학적 해석이 제한된다. 본 연구에서는 다중 증거 기반 대사체 주석, 네트워크 기반 정제, 대사 모듈 발견, 그리고 구조화된 대규모 언어 모델 기반 기능적 해석을 통합하는 LC-MS 피크 중심의 계산 프레임워크인 FeatureMAP을 제시한다. 10개의 공개 데이터셋에서 FeatureMAP은 MS1 기반 대사체 후보 우선순위를 개선하고 주석 중복성을 줄였다. 반합성 벤치마크에서 FeatureMAP은 모의 교란의 92%를 복원하는 등 조율된 대사 교란을 복원했으며, 동시적인 대사 과정을 구별해냈다. 통제된 벤치마크에서는 경쟁적인 대사체 수준 신호 하에서 기능적 해석을 추가로 평가했다. FeatureMAP을 기반으로, 326가지 표현형에 걸친 501건의 인간 대사체학 연구에서 유래한 12,929개의 대사 조절 이상 모듈을 포함하는 표현형 인식 지식 그래프인 PhenoMAP을 추가로 개발했다. 모듈 수준 비교를 통해 독립적인 연구들 간에 재현 가능한 대사 유사성이 확인되었다.

PINR-ESI: EEG 확장 소스 영상화를 위한 물리 정보 기반 자기지도 학습 암시적 신경 표현 프레임워크

이 논문은 EEG 확장 소스 영상화를 위해 물리 정보 기반 자기지도 학습 암시적 신경 표현 프레임워크인 PINR-ESI를 제안한다.

시분할 다중화: 디지털 삶의 운영 리듬과 시간적 조직화 계층

유전자 발현 노이즈 모델링: 전사 동역학에서 세포 성장 및 분열까지

유전자 발현 노이즈 모델링: 전사 동역학에서 세포 성장 및 분열까지