Bioinformatics and Genomic Networks

Últimos artículos

• Bison: Cross-Dataset Learning for Unseen-Compound Perturbation Prediction

  Yunfan Liu, Kasra Ghorbani, Yufei Huang, Zicheng Liu, Jiangbin Zheng, Jingbo Zhou, Shaorong Chen, Chang Yu, Stan Z. Li · 29 de septiembre de 2026

  Predicting transcriptional responses to unseen compounds is limited by fragmented chemical coverage and heterogeneous experimental platforms and gene panels. To assess molecular generalization across these settings, we build on Chem-PerturBridge to benchmark eight datasets with 16,771 compounds, wit…

• MIRCID: Inferred Hub-miRNAs Drive Cross-Task Improvements in Drug Mechanistic Modeling

  Xin Cao, Yigang Chen, Jiatong Xu, Ziyue Zhang, Xiang Cheng, Shenyu Wang, Yangyi Zhang, Xiaoxuan Cai, Shidong Cui, Zihao Zhu, Xiang Ji, Hsi-Yuan Huang, Yang-Chi-Dung Lin, Hsien-Da Huang · 21 de septiembre de 2026

  Drug mechanism-of-action (MoA) modeling commonly relies on perturbational transcriptomes, but matched microRNA (miRNA) measurements are often unavailable. Inferred regulatory features offer a scalable way to reuse these data. Here, we present MIRCID, a framework comparing gene expression with inferr…

• Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs

  Pietro Miotto, Lucia Mellini, Tommaso Marzi, Cesare Alippi, Elena Casiraghi, Alberto Paccanaro, Giorgio Valentini, Mauricio Soto-Gomez · 17 de septiembre de 2026

  Biomedical knowledge graphs combine ontology-derived hierarchies with transversal associations among heterogeneous entities such as phenotypes, diseases, genes, proteins, and patients. This hybrid structure raises the question of whether hyperbolic embeddings, which naturally capture tree-like organ…

• Are You Learning Biological Signal or Shortcuts? Auditing and Mitigating Bias in Protein-Protein Interaction Datasets

  Judith Bernett, Anton Spannagl, Joel {\AA}s, Markus List, David B. Blumenthal · 10 de septiembre de 2026

  Protein-protein interaction (PPI) databases do not faithfully reflect biological realities. Instead, they are influenced by study and technical biases that distort certain protein and interaction attributes. Machine learning models can exploit these as learning shortcuts if the negative dataset is n…

• A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction

  Chunxu Zhang, Bo Li, Wenliang Wang, Yang Liu, Di Jiang, Yuan Huang, Yo-ichi Nabeshima, Akinori Yamamura, Bo Yang, Qiang Yang · 10 de septiembre de 2026

  Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks, and are they zeroth-order or higher-order? Addressing these questions requires learning from large molecular datasets distributed across medical c…

• Language-encoded network topology enables large language models to reason about complex networks

  Ucchwas Talukder Utsha, Sakib Mostafa, James Zou, Md Tauhidul Islam · 4 de septiembre de 2026

  Networks describe systems in biology and beyond, from protein interactions and social relationships to power grids and citation records. Reasoning about such systems requires understanding their structure: which elements are central, which connections bridge separate communities, and how it changes …

• Expert Knowledge & Machine Understanding: Bridging Reactome's Ontology with LLM Semantic Embeddings

  Susanna Bravi, Riccardo De Luca, Rosa Sicilia, Christine Nardini, Mario Santoro · 31 de agosto de 2026

  Biological knowledgebases like Reactome provide high-quality pathways that include biological elements' relationships and textual descriptions (metadata). The quality of such pathways is granted by manual curation, that presents, however, significant scalability challenges. Lately, numerous NLP tool…

• Beyond Tokens: Probing Higher-Order Epistasis in Learned Protein Representations

  Maryam Rahimimovassagh, Ivan Garibay, Niloofar Yousefi · 27 de agosto de 2026

  Protein fitness landscapes contain nonlinear interactions in which mutation effects depend on other residues. We introduce ORBIT, an Order-Resolved Benchmarking of Interaction Transformations framework that separates interaction presence, representation accessibility, and functional recovery. ORBIT …

• GenEx: A Graph-Based Representational Paradigm for SARS-CoV-2 Variant Detection via Codon Co-occurrence Networks

  Arefin Amin, Labiba Faiza Karim, M. Monir Uddin · 20 de agosto de 2026

  Genomic analysis on viruses such as SARS-CoV-2 variants: Beta, Gamma, Delta, and Omicron is heavily dominated by classical bioinformatics methods, including Sequence Alignment, Phylogenetic Analysis, and Mutation Frequency Statistics. These approaches use pairwise codon or nucleotide distance matric…

• Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data

  Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli · 14 de agosto de 2026

  As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical. Challenges such as imbalances, biases, and ethical or legal constraints often limit access to high-quality data. Synthetic data generation can help overcome these limitations. Here, …

• EGRL: Edge generation-guided relation-aware learning for RNA-protein interaction prediction

  Danyu Li, Ling Zhou, Rubing Huang, Xian Zhong, Bin Zou, Kui Jiang · 14 de agosto de 2026

  RNA-Protein Interactions (RPIs) are critical for regulating cellular functions. While traditional wet-lab experiments for RPI detection are costly and time-consuming, Deep Learning (DL) methods provide an efficient computational alternative for RPI Prediction (RPIP). In particular, Graph Neural Netw…

• DoGMA: A Central-Dogma-Guided Foundation Model for Multi-Omics Alignment and Multi-Task Learning in Oncology

  Junfei Ling (Institute of Medical Robotics, Shanghai Jiao Tong University), Bangzheng Pu (Institute of Medical Robotics, Shanghai Jiao Tong University), Bingsen Xue (Institute of Medical Robotics, Shanghai Jiao Tong University), Tianle Li (Institute of Data Science, The University of Hong Kong), Ruying Hu (Oriental Pan-Vascular Devices Innovation College, University of Shanghai for Science and Technology), Cheng Jin (Institute of Medical Robotics, Shanghai Jiao Tong University) · 11 de agosto de 2026

  Attention mechanisms have been widely utilized in modern deep learning, and many existing multi-omics models inherit their conventional use to allow unrestricted bidirectional interactions. However, the fundamental logic of life is directional. Existing designs often overlook the directionality sugg…

• bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning

  Koushik Howlader, Tirtho Roy, Md Tauhidul Islam, Wei Le · 10 de agosto de 2026

  Transformer models for high-dimensional omics analysis process thousands of genes or pathways, although only a subset requires deep computation. Mixture-of-Recursions (MoR) improves efficiency through adaptive token-choice or expert-choice routing. We propose bioMoR, which, to the best of our knowle…

• THBKG: A Temporal Biomedical Knowledge Graph for Decision-Aligned Clinical Advancement Prediction

  Pui Chung Siu, Claudia Cabrera, Mani Mudaliar, Arkaitz Zubiaga · 7 de agosto de 2026

  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 enter…

• CASCADE: An Agentic Regulatory Network Framework for Patient-Data-Validated Downstream Perturbation Prediction

  Jose A. Bird · 7 de agosto de 2026

  CASCADE is an agentic framework that predicts downstream transcriptional effects of gene perturbation from precomputed ARACNe regulatory networks, exposed via MCP. Prior work validates such tools by checking whether predicted genes are known cancer genes (membership); we instead test whether the…

• Evolution-Aware MSA Reasoning for Subsampling via Factor Graphs

  Zhangzhi Xiong, Minzhang Li, Haotian Yu, Sixian Shen, Kexin Zhang, Mingrui Li, Jie Zheng, Kewei Tu, Jingyi Yu · 27 de julio de 2026

  Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoidable under limited token budgets. Existing strategies, including random selection, identity-based filtering, and diversity-driven sampling, are effec…

• Discrete Ricci Curvature on Protein Contact Graphs for Lightweight Fold Classification

  Jianru Shen · 21 de julio de 2026

  Protein fold classification can be approached via sequence-based representations or structural descriptors, but direct comparisons between lightweight handcrafted descriptors and pretrained protein language model embeddings remain limited. We investigate discrete Ricci curvature on Calpha contact gr…

• Benchmarking Machine Learning Models for Multi-Omics-Based Breast Cancer Prediction

  Priyanka Paudel, Madan Baduwal · 21 de julio de 2026

  Estrogen Receptor (ER) status is a critical biomarker in breast cancer diagnosis, prognosis, and treatment selection. Recent advances in high-throughput sequencing technologies have enabled the generation of multi-omics datasets that provide complementary molecular information for computational pred…

• Batch effects can impair federated learning in multi-center omics studies

  Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann, Anne Hartebrodt, Ahmad Taheri, Niklas Probul, Jan Baumbach, Olga Zolotareva · 7 de julio de 2026

  Federated learning (FL) enables collaborative analysis of biomedical data without exchanging sensitive patient-level information, but its performance in multi-center studies may be compromised by batch effects which can obscure biological signals. Here, we systematically assess the impact of uncorre…

• Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer

  Pedro Henrique da Costa Avelar, Le Ou-Yang, Min Wu, Sophia Tsoka · 7 de julio de 2026

  Integrating complex, multi-omics data presents significant challenges. Existing approaches often face a trade-off between model interpretability and representational capacity, with most either relying on post-hoc interpretation or use linear models that may overlook complex interactions. We report P…

• Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations

  Dongmin Bang, Sugyun An, Inyoung Sung, Ilho Yun, Sun Kim, Sangseon Lee · 7 de julio de 2026

  Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles. Preclinical transfer-learning models can simulate drug-induced expression changes but are often hard t…

• MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction

  Wenbo Zhang · 3 de julio de 2026

  Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development. A difficult setting arises when candidate interactions include proteins that have no observed PPI edges during training, where models relying on network topolog…

• Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions

  Martino Ciaperoni, Margherita Lalli, Simone Piaggesi, Martina Varisco, Francesco Carli, Riccardo Guidotti, Dino Pedreschi, Francesco Raimondi, Fosca Giannotti · 2 de julio de 2026

  Predicting cancer drug response from transcriptomic profiles is a cornerstone of precision oncology, yet the scientific value of machine learning models hinges not solely on predictive accuracy, but also on their capacity to generate reliable biological insights. Current explainability approaches in…

• Retrieval-Augmented Multimodal Learning for Enzyme-Substrate Interaction Prediction Under Low-Homology Shift

  Chen Liu, Bingxin Zhou, Xinyuan Wang, Ming Li, Guisheng Fan, Liang Hong · 23 de junio de 2026

  Enzyme substrate interaction (ESI) prediction is a fundamental computational task for biocatalyst discovery and reaction screening in large biochemical spaces. In practical settings, ESI prediction is challenged by sparse positive supervision and low-homology distribution shift, where test enzymes s…

• Protein contacts are already in the attention: a single-forward-pass alternative to the Categorical Jacobian

  Rome Thorstenson · 23 de junio de 2026

  The Categorical Jacobian (CJ) of Zhang et al. (2024) reads protein contacts from a language model by perturbing every residue with every alternative amino acid, about 19L forward passes. We show the signal it reconstructs is already concentrated in a small subset of attention heads: averaging the to…