Use BioMod model weights naming across documentation

Update eight Markdown documents to use BioMod model weights terminology. Refresh stale source-guide examples and remove historical model-size comparisons and unverified benchmark values. Actual weight filenames, paired configuration, code, and datasets are unchanged.

BioMod/README.md

@@ -171,10 +171,9 @@ process group, with training data and paths configured for the target machine.
 
 ## Shared Assets
 
-This local checkout retains only `weight/0810model/`. The original 0629
-checkpoint/config pair is not included. For examples that reference
-`weight/0629model/`, use the matching 0810 `.pth` and `.pkl` files described below,
-and update any server-specific paths for the local environment.
+The BioMod model weights and matching configuration are stored in
+`weight/0810model/`. Use the supplied `.pth` and `.pkl` together, and update
+server-specific paths for the local environment.
 
 Tutorials expect a pretrained BioMod/PerformerModule checkpoint and shared
 gene assets. Set `BIOMODULEFM_ASSET_ROOT` to the directory containing
@@ -187,7 +186,7 @@ export BIOMODULEFM_ASSET_ROOT=/path/to/BioMod
 Current local asset files:
 
 ```text
-$BIOMODULEFM_ASSET_ROOT/weight/0810model/                # 0810 checkpoint: .pth weights and .pkl configuration
+$BIOMODULEFM_ASSET_ROOT/weight/0810model/                # BioMod model weights and matching configuration
 ├── mae_tost_70M_75mask_ec2.pkl
 └── mae_tost_70M_75mask_ec2_epoch0.pth
 
@@ -222,21 +221,13 @@ BioMod/module_data_all/                                  # Module definitions, m
 └── merged_modules.json
 ```
 
-## Smaller 0810 model
+## BioMod model weights
 
-The repository also includes a smaller 2-layer encoder checkpoint:
-
-```text
-weight/0810model/                                        # 0810 checkpoint: .pth weights and .pkl configuration
-|-- mae_tost_70M_75mask_ec2_epoch0.pth
-`-- mae_tost_70M_75mask_ec2.pkl
-```
-
-It uses a 2-layer encoder with the same 27,874-gene axis and 660 module
-definitions as the primary 32-layer model. Use its checkpoint and PKL together
-by replacing the two `weight/0629model/` paths in the commands below with the
-matching `weight/0810model/` files. The code restores `enc_depth=2` and the
-other architecture settings from the PKL automatically.
+Use `mae_tost_70M_75mask_ec2_epoch0.pth` with
+`mae_tost_70M_75mask_ec2.pkl` from the shared-assets directory above. The supplied
+configuration specifies a 2-layer encoder, a 27,874-gene axis, and 660 module
+definitions. Keep this configuration paired with the weights so that task
+runners can restore the corresponding architecture settings.
 
 ## Tutorials
 
@@ -255,9 +246,11 @@ notebook.
 
 ## Data Layout
 
-Large datasets are not committed to this code repository. Place them under
-task-specific data directories or pass absolute paths through environment
-variables.
+The Hugging Face repository includes datasets under the project-root `data/`
+directory. Individual tasks may require additional inputs or a different data
+layout. Check each task's required files, then configure its data paths for the
+local environment; the presence of `data/` does not imply that every tutorial
+has all of its inputs.
 
 ### Single-Drug Response
 
@@ -293,20 +286,20 @@ files for ROC, PR, and confusion-matrix visualization.
 
 ## Example Commands
 
-Drug response prediction with the primary 0629 model:
+Drug response prediction with the BioMod model weights:
 
 ```bash
 python drug_response/train_drug_performer_module.py \
   --gpu_id 0 \
   --data_path /path/to/single_drug_response/data \
-  --checkpoint_path $BIOMODULEFM_ASSET_ROOT/weight/0629model/mae_tost_48M_cross_attn_only_pure_memory_epoch12.pth \
-  --hyper_params_path $BIOMODULEFM_ASSET_ROOT/weight/0629model/mae_tost_48M_cross_attn_only_pure_memory.pkl \
+  --checkpoint_path $BIOMODULEFM_ASSET_ROOT/weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \
+  --hyper_params_path $BIOMODULEFM_ASSET_ROOT/weight/0810model/mae_tost_70M_75mask_ec2.pkl \
   --module_data_dir ./module_data_all \
   --gene_list_path $BIOMODULEFM_ASSET_ROOT/model_dependencies/selected_genes_27k.txt \
   --gene_mapping_path $BIOMODULEFM_ASSET_ROOT/model_dependencies/gene2ens.csv \
   --gene2vec_path $BIOMODULEFM_ASSET_ROOT/model_dependencies/selected_gene2vec_27k.npy \
   --symbol2ens_path $BIOMODULEFM_ASSET_ROOT/model_dependencies/gene2ens.csv \
-  --save_dir ./results/drug_response_0629 \
+  --save_dir ./results/drug_response \
   --epochs 300 \
   --batch_size 32 \
   --pool_method max
@@ -316,15 +309,15 @@ Drug-combination synergy prediction:
 
 ```bash
 python drug_combination/train_drug_combination_performer_module.py \
-  --checkpoint_path $BIOMODULEFM_ASSET_ROOT/weight/0629model/mae_tost_48M_cross_attn_only_pure_memory_epoch12.pth \
-  --hyper_params_path $BIOMODULEFM_ASSET_ROOT/weight/0629model/mae_tost_48M_cross_attn_only_pure_memory.pkl \
+  --checkpoint_path $BIOMODULEFM_ASSET_ROOT/weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \
+  --hyper_params_path $BIOMODULEFM_ASSET_ROOT/weight/0810model/mae_tost_70M_75mask_ec2.pkl \
   --module_data_dir ./module_data_all \
   --gene_list_path $BIOMODULEFM_ASSET_ROOT/model_dependencies/selected_genes_27k.txt \
   --gene_mapping_path $BIOMODULEFM_ASSET_ROOT/model_dependencies/gene2ens.csv \
   --gene2vec_path $BIOMODULEFM_ASSET_ROOT/model_dependencies/selected_gene2vec_27k.npy \
   --symbol2ens_path $BIOMODULEFM_ASSET_ROOT/model_dependencies/gene2ens.csv \
   --data_dir ./drug_combination/data_summary \
-  --output_dir ./results/drug_combination_0629 \
+  --output_dir ./results/drug_combination \
   --epochs 500 \
   --batch_size 256 \
   --lr 1e-4 \
@@ -348,17 +341,12 @@ include:
 For GitHub display, use PNG files. For manuscripts, use the PDF/SVG exports.
 The plotting cells use embedded, editable PDF fonts where supported.
 
-## Reference Results
-
-These values are retained as run-level reference points and should be refreshed
-from final locked runs before manuscript submission.
+## Evaluation Results
 
-| Task | Dataset / setting | Reported signal |
-| --- | --- | --- |
-| Drug response | GDSC/CCLE, primary 0629 model | Pearson correlation around `0.93` in the existing full run |
-| Drug combination | Independent-set domain generalization, primary 0629 model | AUROC reference around `0.6824` for the previous run |
-| Zero-shot integration | HP, BMMC, Kim Lung, Kim Lung no-malignant | Notebook computes avgBatch and avgBio across repeated runs |
-| Cell annotation | Seven benchmark datasets | Notebook reports accuracy, macro-F1, and confusion matrices |
+This README does not provide verified benchmark scores for the bundled BioMod
+model weights. Run the relevant task with a recorded data split and configuration
+before reporting performance. Notebook plotting cells and historical outputs
+alone do not establish performance for the supplied weights.
 
 ## Reproducibility Conventions
 
@@ -379,9 +367,9 @@ files and shows which entries are missing.
 
 ### Missing model assets
 
-Check `BIOMODULEFM_ASSET_ROOT` and verify that the 0407 checkpoint, pkl,
-gene2vec, gene list, gene mapping, and `performer_pytorch_cont/` directory
-exist.
+Check `BIOMODULEFM_ASSET_ROOT` and verify that the BioMod model weights, matching
+`.pkl` configuration, gene2vec file, gene list, gene mapping, and
+`performer_pytorch_cont/` directory exist at the paths listed under Shared Assets.
 
 ### No space left on device
 

BioMod/drug_combination/README.md

@@ -1,7 +1,8 @@
-# Drug Combination Synergy Prediction (PerformerModule)
+# Drug Combination Synergy Prediction with BioMod
 
-This task predicts drug-combination synergy using pretrained PerformerModule cell
-embeddings and GCN-based drug molecular graph encoders.
+This task predicts drug-combination synergy using BioMod cell embeddings from
+the PerformerModule encoder and GCN-based drug molecular graph encoders. Use the
+[BioMod model weights and matching configuration](../../README.md#biomod-model-weights).
 
 It is adapted from [scLong/drug_combination](../../../drug_combination)
 (DeepDDs), replacing the scfm/geneformer cell-line representation with
@@ -105,7 +106,7 @@ python train_drug_combination_performer_module.py \
 
 | Argument | Default | Description |
 | --- | --- | --- |
-| `--checkpoint_path` | Required | Path to the pretrained PerformerModule checkpoint |
+| `--checkpoint_path` | Required | Path to the BioMod model weights |
 | `--hyper_params_path` | Required | Path to the PerformerModule hyper-parameter pkl file |
 | `--module_data_dir` | None | Directory containing module data files |
 | `--data_dir` | `./data_summary` | Data directory |

BioMod/gene_perturbation/README.md

@@ -1,7 +1,9 @@
-# Adapting PerformerEncoderOnly_Module to Gene Perturbation
+# Gene Perturbation Prediction with BioMod
 
-This document explains how to adapt the PerformerEncoderOnly_Module model to the
-gene perturbation task (GEARS).
+This document describes the BioMod gene perturbation workflow (GEARS), using
+the `PerformerEncoderOnly_Module` encoder. The
+[BioMod model weights and matching configuration](../../README.md#biomod-model-weights)
+are shared with the other downstream tasks.
 
 ## Overview
 
@@ -19,7 +21,7 @@ are:
 
 `SCFM_Module` wraps `PerformerEncoderOnly_Module` and provides these functions:
 
-- **Input handling**: reindex input gene expression values to the scfm gene order.
+- **Input handling**: reindex input gene expression values to the BioMod gene order.
 - **Module encoding**: obtain module-level encodings from `PerformerEncoderOnly_Module`.
 - **Gene embedding recovery**: recover gene-level embeddings from module encodings.
   - For each gene, find all modules that contain it.
@@ -65,7 +67,7 @@ The training script is based on `GEARS/train_dual_ddp_reduce_dec.py`. Main chang
 Make sure the following files exist:
 
 - Perturbation data, such as Norman, Adamson, or Dixit.
-- scfm model checkpoint and hyper-parameter file.
+- BioMod model weights and their matching configuration file.
 - Module data in JSON format.
 - Gene mapping file, such as `gene2ens.csv`.
 
@@ -94,14 +96,14 @@ python -m torch.distributed.launch --nproc_per_node=4 \
 
 **Required arguments**:
 
-- `--scfm_hyper_params_path`: path to the scfm model hyper-parameter file.
-- `--scfm_ckpt_path`: path to the scfm model checkpoint.
+- `--scfm_hyper_params_path`: path to the BioMod model configuration file.
+- `--scfm_ckpt_path`: path to the BioMod model weights.
 - `--module_data_path` or `--module_data_dir`: module data path; provide one of them.
 
 **Recommended arguments**:
 
 - `--gene_mapping_path`: gene mapping file, especially when module data use gene symbols.
-- `--scfm_genes_list_path`: scfm gene list file.
+- `--scfm_genes_list_path`: BioMod gene list file.
 - `--scfm_gene2vec_file`: path to the gene2vec file.
 
 **Optional arguments**:
@@ -200,7 +202,7 @@ BioMod/gene_perturbation/                                # Perturbation response
 
 ## Notes
 
-1. **Module data format**: make sure the gene indices or names in the module data match the scfm gene list.
+1. **Module data format**: make sure the gene indices or names in the module data match the BioMod gene list.
 2. **Gene mapping**: provide `gene_mapping_path` if the module data use gene symbols.
 3. **Memory usage**: the module version may require more memory because it maintains the module-to-gene mapping.
 4. **Training stability**: use the same learning rate and training strategy as original sclong when possible.

BioMod/interpretability/README.md

@@ -1,10 +1,11 @@
-# Module Interpretability Experiments for scLong (performer_module)
+# Module Interpretability Experiments for BioMod
 
-This module contains interpretability experiments designed specifically for the
-**module level** of scLong. The goal is to demonstrate, using structural,
-semantic, and functional evidence, that module design has a paradigm-level
-advantage over gene-level foundation models such as GenePT, scGPT, and
-scFoundation.
+This directory contains analyses of BioMod module representations, covering
+latent geometry, pathway-text alignment, module importance, and attention.
+These workflows examine structural, semantic, and functional properties of the
+learned modules. Use the
+[BioMod model weights and matching configuration](../../README.md#biomod-model-weights)
+when loading the pretrained backbone.
 
 ## Experiment Roadmap, Ordered by Priority
 
@@ -13,7 +14,7 @@ scFoundation.
 | 1 | `module_geometry.py` | Does the module latent space self-organize by pathway family,  | Completed |
 | 2 | `module_text_alignment/module_text_alignment.py` | Do module embeddings align with natural-language pathway descriptions,  | Implemented |
 | 3 | `module_importance/module_importance.py` | Does task-driven module importance match known pathway-cell-type or pathway-disease associations,  | Implemented |
-| 4 | `module_vs_gene.py` | Is scLong better than GenePT/scGPT for pathway-level interpretability,  | TODO |
+| 4 | `module_vs_gene.py` | Is BioMod better than GenePT/scGPT for pathway-level interpretability,  | TODO |
 | 6 | `attention/module_crosstalk.py` | Do module interactions match Reactome/KEGG pathway relationships,  | Implemented |
 
 Experiments 1 and 2 are the core differentiators because gene-level baselines
@@ -25,7 +26,7 @@ be pursued after the core experiments are complete.
 
 ## Experiment 1: Intrinsic Geometry of Module Embeddings (`module_geometry.py`)
 
-**Hypothesis:** the scLong encoder is trained only with a masked-module
+**Hypothesis:** the BioMod encoder is trained with a masked-module
 reconstruction task, but it should still encode semantically related modules,
 such as modules from the same pathway family or MSigDB hallmark biological
 process, into nearby regions of latent space, even though family labels are
@@ -46,7 +47,7 @@ never seen during training.
 **Differentiation from gene-level baselines:** gene-level foundation models embed
 single genes rather than pathway groups. To reproduce this experiment, they would
 first have to manually aggregate gene embeddings by pathway, which compresses
-gene-level signals and introduces a hand-designed step. scLong learns this
+gene-level signals and introduces a hand-designed step. BioMod learns this
 aggregation end to end during pretraining.
 
 ## File Structure
@@ -345,7 +346,7 @@ necessary.
 
 ## Experiment 2: Module Embedding to Pathway Text Alignment (`module_text_alignment/`)
 
-**Hypothesis:** the structure of the scLong module embedding space significantly
+**Hypothesis:** the structure of the BioMod module embedding space significantly
 aligns with natural-language pathway semantics. Semantically similar pathways
 are also encoded nearby by the model. Gene-level models cannot reproduce this
 experiment because individual gene names do not carry phrase-level semantics.

BioMod/interpretability/attention/README.md

@@ -1,6 +1,6 @@
 # Experiment 6: Module Crosstalk Graph vs Pathway Interaction Database
 
-This experiment tests whether module-module interactions learned by the scLong
+This experiment tests whether module-module interactions learned by the BioMod
 encoder are consistent with known pathway-pathway relationships.
 
 ## Methods

BioMod/interpretability/module_text_alignment/DESIGN.md

@@ -2,7 +2,7 @@
 
 ## Overview
 
-This experiment tests whether the structure of the scLong module embedding space
+This experiment tests whether the structure of the BioMod module embedding space
 significantly aligns with the semantic space of natural-language pathway
 descriptions. Gene-level models cannot reproduce this experiment because single
 gene names do not carry phrase-level semantics.

BioMod/zero_shot_batch/README.md

@@ -1,11 +1,12 @@
-# sclong_module Zero-Shot Batch Correction
+# BioMod Zero-Shot Embeddings and Batch-Effect Evaluation
 
-This directory contains the adaptation code for running the sclong_module model
-on zero-shot batch correction.
+This directory contains code for extracting BioMod cell embeddings and
+evaluating batch effects without fine-tuning. Use the
+[BioMod model weights and matching configuration](../../README.md#biomod-model-weights).
 
 ## Features
 
-- **Task objective**: evaluate the performance of the pretrained sclong_module model on zero-shot batch correction.
+- **Task objective**: evaluate batch effects in cell embeddings extracted with the BioMod model weights.
 - **Evaluation metrics**: compute ASW_label/batch metrics with the scib package.
 - **Data format**: supports single-cell data in h5ad format.
 
@@ -14,7 +15,7 @@ on zero-shot batch correction.
 ### Basic Usage
 
 ```bash
-python sclong_module_zero_shot.py \
+python performer_module_zero_shot.py \
     --scfm_hyper_params_path <path_to_model_hyper_params.pkl> \
     --scfm_ckpt_path <path_to_model_checkpoint.pth> \
     --module_data_path <path_to_module_json_file.json> \
@@ -63,13 +64,13 @@ python performer_module_zero_shot.py \
 
 | Argument | Description | Default |
 | --- | --- | --- |
-| `--scfm_hyper_params_path` | Path to the model hyper-parameter pkl file; required | - |
-| `--scfm_ckpt_path` | Path to the model checkpoint; required | - |
+| `--scfm_hyper_params_path` | Path to the matching BioMod model configuration; required | - |
+| `--scfm_ckpt_path` | Path to the BioMod model weights; required | - |
 | `--module_data_path` | Path to one module JSON file | None |
 | `--module_data_dir` | Directory containing multiple module JSON files | None |
 | `--gene2vec_path` | Path to the Gene2Vec file | ../selected_gene2vec_27k.npy |
 | `--gene_mapping_path` | CSV mapping gene symbols to Ensembl IDs | ../gene2ens.csv |
-| `--scfm_genes_file` | scfm gene list file | ../selected_genes_27k.txt |
+| `--scfm_genes_file` | BioMod gene list file | ../selected_genes_27k.txt |
 | `--data_path` | Input h5ad data file | ../zero-shot-batch/pancreas_scib.h5ad |
 | `--output_key` | Key name for embeddings in `adata.obsm` | X_sclong_module |
 | `--n_cells` | Number of cells to process; `all` means all cells | all |
@@ -91,10 +92,10 @@ After the run finishes, the following files are generated under `--output_dir`:
 ## Workflow
 
 1. **Data loading**: load single-cell data in h5ad format.
-2. **Gene mapping**: convert input gene symbols to Ensembl IDs and map them to the scfm gene list.
+2. **Gene mapping**: convert input gene symbols to Ensembl IDs and map them to the BioMod gene list.
 3. **Model inference**:
-   - Reindex gene expression values to the scfm gene order.
-   - Obtain encodings with the sclong_module model.
+   - Reindex gene expression values to the BioMod gene order.
+   - Obtain encodings with the BioMod model.
    - Extract cell embeddings by averaging `module_encodings`.
 4. **Evaluation**: compute batch-correction metrics with scib.
 
@@ -119,7 +120,7 @@ The code extracts cell embeddings in the following priority order:
 
 ## Comparison with Original scLong
 
-| Feature | scLong | sclong_module |
+| Feature | scLong | BioMod |
 | --- | --- | --- |
 | Model architecture | DualEncoder (mini + large) | Single Encoder |
 | Input format | Flat gene expression | Module-based |
@@ -135,6 +136,9 @@ The code extracts cell embeddings in the following priority order:
 
 ## Example Output
 
+The following legacy log illustrates the output format; it does not document a
+validated run with the bundled BioMod model weights.
+
 ```text
 Using device: cuda
 Loading hyper parameters from ./checkpoints/model_params.pkl
@@ -176,7 +180,8 @@ Zero-shot evaluation completed!
 
 **Error**: `KeyError: 'module_encodings'`
 
-**Fix**: check whether the checkpoint is correct and make sure it is an sclong_module model.
+**Fix**: use the BioMod model weights and their matching configuration, and
+check that the model provides the required output.
 
 ### Issue 4: Out of Memory
 

README.md

@@ -18,7 +18,7 @@ tools.
 BioMod is released by **CTC-Atlas**, the joint laboratory of **Raymind Biotechnology
 Co., Ltd** and the **Shenzhen Research Institute of Big Data (SRIBD)**.
 
-[Installation](#installation) · [Pretrained model](#pretrained-model) ·
+[Installation](#installation) · [BioMod model weights](#biomod-model-weights) ·
 [Tasks and examples](#tasks-and-examples) · [Usage notes](#usage-notes) ·
 [Repository layout](#repository-layout)
 
@@ -54,16 +54,16 @@ Pretraining and new-data reconstruction evaluation use CUDA/NCCL. The zero-shot
 runner selects CUDA when available and otherwise uses CPU. Native Windows
 execution has not been validated for the supplied environment.
 
-## Pretrained model
+## BioMod model weights
 
-The **0810 checkpoint** is included in this release. Use its weights and
-configuration together; the configuration records the model architecture and
-gene-module definitions.
+This release includes the **BioMod model weights** and their matching
+configuration. Use these files together; the configuration records the model
+architecture and gene-module definitions.
 
 | Asset | File |
 | --- | --- |
-| Model weights | [mae_tost_70M_75mask_ec2_epoch0.pth](weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth) |
-| Model configuration | [mae_tost_70M_75mask_ec2.pkl](weight/0810model/mae_tost_70M_75mask_ec2.pkl) |
+| BioMod model weights | [mae_tost_70M_75mask_ec2_epoch0.pth](weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth) |
+| Matching model configuration | [mae_tost_70M_75mask_ec2.pkl](weight/0810model/mae_tost_70M_75mask_ec2.pkl) |
 | Model gene order | [selected_genes_27k.txt](model_dependencies/selected_genes_27k.txt) |
 | Gene identifier mapping | [gene2ens.csv](model_dependencies/gene2ens.csv) |
 | Gene2Vec embeddings | [selected_gene2vec_27k.npy](model_dependencies/selected_gene2vec_27k.npy) |
@@ -119,7 +119,7 @@ Before running a notebook, update its configuration cells:
 | `BASE` or `ASSET_ROOT` | Project root |
 | `PROJECT_ROOT` | Inner `BioMod/` source directory |
 | `MODULE_MODEL` or `LEGACY_MODULE_MODEL` | `model_dependencies/` under the project root |
-| Weight and configuration paths | The paired 0810 files listed above |
+| Weight and configuration paths | The BioMod model weights and matching configuration listed above |
 | `MODULE_DATA_DIR` | `BioMod/module_data_all/` under the project root |
 | Data and output paths | Your task inputs and a writable output directory |
 
@@ -286,7 +286,7 @@ BioMod/                                                   # Project root: source
 │   ├── selected_gene2vec_27k.npy
 │   └── selected_genes_27k.txt
 ├── weight/                                               # Pretrained weights and matching model configurations
-│   └── 0810model/                                        # 0810 checkpoint: .pth weights and .pkl configuration
+│   └── 0810model/                                        # BioMod model weights and matching configuration
 │       ├── mae_tost_70M_75mask_ec2.pkl
 │       └── mae_tost_70M_75mask_ec2_epoch0.pth
 ├── .gitattributes