upstream/inoue0426-awesome-computational-biology/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Awesome Computational Biology [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/README.md
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/c6f07d90/README.md
+upstream_sha: c6f07d90
+imported_at: 2026-08-31
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -75,6 +75,7 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
       - [Drug Target Interaction](#drug-target-interaction)
       - [Compound-Protein Interaction](#compound-protein-interaction)
       - [Molecular Generation](#molecular-generation)
+    - [Protein Property Prediction](#protein-property-prediction)
     - [LLM for Biology](#llm-for-biology)
     - [Foundation Models](#foundation-models)
       - [Single-cell Foundation Models](#single-cell-foundation-models)
@@ -159,6 +160,7 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [GenBank](https://www.ncbi.nlm.nih.gov/genbank/) — NCBI's database of genetic sequences.
 - [UCSC Genome Browser](https://genome.ucsc.edu/) — UCSC's genome browser.
 - [cBioPortal](https://www.cbioportal.org/) — Cancer genomics database; aggregating many patient datasets.
+- [OncoKB](https://www.oncokb.org/) — Precision oncology knowledge base of cancer genes, variants, and therapeutic implications.
 - [10x Genomics Dataset](https://www.10xgenomics.com/resources/datasets) — Collection of single-cell datasets.
 - [The Genotype-Tissue Expression (GTEx)](https://gtexportal.org/home/) — Human gene expression and regulation resource.
 - [Dependency Map (DepMap)](https://depmap.org/portal/) — CRISPR-Cas9 screens in cancer cell lines.
@@ -316,7 +318,7 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [CellCharter](https://github.com/CSOgroup/cellcharter) — Identification and characterization of spatial cell niches from spatial transcriptomics using VAEs and Gaussian mixture models.
 - [STAGATE](https://github.com/RucDongLab/STAGATE) — Adaptive graph attention auto-encoder for spatial domain identification in spatial transcriptomics.
 - [NCEM](https://github.com/theislab/ncem) — GNN-based model for learning intercellular communication from spatial graphs of cells.
-- [DeepTalk](https://github.com/JiangBioLab/DeepTalk) — Graph attention network for deciphering cell-cell communication from spatial transcriptomics data.
+- [DeepTalk](https://github.com/JiangBioLab/DeepTalk) — Graph attention network for deciphering cell-cell communication from spatial transcriptomics.
 - [COMMOT](https://github.com/zcang/COMMOT) — Optimal transport-based framework for screening cell-cell communication in spatial transcriptomics.
 - [TIGON](https://github.com/yutongo/TIGON) — Neural optimal transport method for reconstructing growth and dynamic trajectories from single-cell transcriptomics.
 - [LINGER](https://github.com/Durenlab/LINGER) — Neural network for gene regulatory network inference from single-cell multiome (RNA+ATAC-seq) data with bulk data pretraining.
@@ -383,6 +385,10 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [ReLeaSE](https://github.com/isayev/ReLeaSE) — Deep reinforcement learning framework for de novo drug design combining a generative and predictive model.
 - [PaccMannRL](https://github.com/PaccMann/paccmann_generator) — Reinforcement learning-based generative model for de novo hit-like anticancer molecule design from transcriptomic data.
 
+### Protein Property Prediction
+
+- [NbBayesLM](https://github.com/FairuzShadmaniShishir/NbBayesLM) — Bayesian neural network integrating protein language model embeddings and physicochemical features to predict nanobody thermostability with uncertainty estimates. [Paper](https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1832968/full)
+
 ### LLM for Biology
 
 - [AI4Chem/ChemLLM-7B-Chat](https://huggingface.co/AI4Chem/ChemLLM-7B-Chat) — LLM for chemical & molecular science.
@@ -439,7 +445,7 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [GeneCompass](https://github.com/xCompass-AI/GeneCompass) — Large-scale foundation model integrating DNA regulatory sequences and single-cell transcriptomics from 120M+ cells across multiple species for gene regulation prediction.
 - [UnitedNet](https://github.com/LiuLab-Bioelectronics-Harvard/UnitedNet) — Interpretable multi-task deep neural network for single-cell multi-omics integration spanning transcriptomics, chromatin accessibility, and proteomics.
 - [SpatialGlue](https://github.com/zhanglabtools/SpatialGlue) — Graph attention network for spatial multi-omics integration jointly embedding spatial transcriptomics with chromatin accessibility or proteomics.
-- [MIDAS](https://github.com/labomics/midas) — Mosaic integration and differential accessibility model for single-cell multi-omics data that handles arbitrary missing-modality combinations across transcriptomics, chromatin accessibility, and proteomics.
+- [MIDAS](https://github.com/labomics/midas) — Mosaic integration and differential accessibility model for single-cell multi-omics that handles arbitrary missing-modality combinations across transcriptomics, chromatin accessibility, and proteomics.
 - [Concerto](https://github.com/melobio/Concerto-reproducibility) — Contrastive self-supervised learning framework for single-cell multimodal data integration, batch correction, and reference-query mapping.
 - [scButterfly](https://github.com/BioX-NKU/scButterfly) — Dual-aligned variational autoencoder for single-cell cross-modality translation between paired and unpaired multiomics data.
 - [JAMIE](https://github.com/Oafish1/JAMIE) — Joint variational autoencoder for multimodal single-cell data imputation and embedding.
@@ -521,6 +527,7 @@ If you use this list in papers, slides, or documentation, please cite this repos
 To keep quality high, additions should meet all of the following:
 
 - The resource is trustworthy and relevant to computational biology.
+- The resource has clear value to the scope and audience of this collection; highly specialized resources with limited relevance beyond a narrow application context may be declined even when technically sound.
 - The primary link points to an official source (official docs, organization site, maintained repository, or official dataset page).
 - The resource has evidence of technical substance: ideally a peer-reviewed paper; at minimum a preprint or official technical documentation.
 - The description is factual and concise (no marketing copy).