Update README.md

README.md

@@ -16,9 +16,17 @@ pipeline_tag: text-classification
 
 # ESM2 Protein Function Caller
 
-An Evolutionary-scale Model (ESM) for protein function calling from amino acid sequences. Based on the ESM2 Transformer architecture and fine-tuned on the [CAFA 5](https://huggingface.co/datasets/andrewdalpino/CAFA5) dataset, this model predicts the gene ontology (GO) subgraph for a particular protein sequence - giving you insight into the molecular function, biological process, and location of the activity inside the cell.
+An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on [UniRef50](https://www.uniprot.org/help/uniref), and fine-tuned on the [AmiGO](https://huggingface.co/datasets/andrewdalpino/AmiGO) dataset, this model predicts the GO subgraph for a particular protein sequence - giving you insight into the molecular function, biological process, and location of the activity inside the cell.
 
-**Note**: This model specilizes on the `celluar component` subgraph of the gene ontology.
+**Note**: This version only models the `cellular component` subgraph of the gene ontology.
+
+## What are GO terms?
+
+> "The Gene Ontology (GO) is a concept hierarchy that describes the biological function of genes and gene products at different levels of abstraction (Ashburner et al., 2000). It is a good model to describe the multi-faceted nature of protein function."
+
+> "GO is a directed acyclic graph. The nodes in this graph are functional descriptors (terms or classes) connected by relational ties between them (is_a, part_of, etc.). For example, terms 'protein binding activity' and 'binding activity' are related by an is_a relationship; however, the edge in the graph is often reversed to point from binding towards protein binding. This graph contains three subgraphs (subontologies): Molecular Function (MF), Biological Process (BP), and Cellular Component (CC), defined by their root nodes. Biologically, each subgraph represent a different aspect of the protein's function: what it does on a molecular level (MF), which biological processes it participates in (BP) and where in the cell it is located (CC)."
+
+From [CAFA 5 Protein Function Prediction](https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/data)
 
 ## Code Repository
 
@@ -29,18 +37,19 @@ https://github.com/andrewdalpino/esm2-function-classifier
 - **Vocabulary Size**: 33
 - **Embedding Dimensions**: 640
 - **Attention Heads**: 20
-- **Hidden Layers**: 30
+- **Encoder Layers**: 30
 - **Context Length**: 1026
-- **Total Parameters**: 151M
 
-## Example Usage
+## Basic Example
+
+For a basic demonstration we can rank the GO terms for a particular sequence. For a more advanced example see the [predict-subgraph.py](https://github.com/andrewdalpino/esm2-function-classifier/blob/master/predict-subgraph.py) source file.
 
 ```python
 import torch
 
 from transformers import EsmTokenizer, EsmForSequenceClassification
 
-model_name = "andrewdalpino/ESM2-150M-Protein-Cellular-Component"
+model_name = "andrewdalpino/ESM2-35M-Protein-Molecular-Function"
 
 tokenizer = EsmTokenizer.from_pretrained(model_name)
 
@@ -48,15 +57,11 @@ model = EsmForSequenceClassification.from_pretrained(model_name)
 
 model.eval()
 
-sequence = "MCNAWYISVDFEKNREDKSKCIHTRRNSGPKLLEHVMYEVLRDWYCLEGENVYMMGKKWQMPMCSLH"
+sequence = "MCNAWYISVDFEKNREDKSKCIHTRRNSGPKLLEHVMYEVLRDWYCLEGENVYMM"
 
 top_k = 10
 
-out = tokenizer(
-    sequence,
-    max_length=1026,
-    truncation=True,
-)
+out = tokenizer(sequence)
 
 input_ids = out["input_ids"]
 
@@ -76,20 +81,12 @@ terms = [model.config.id2label[index] for index in indices.tolist()]
 print(f"Top {args.top_k} GO Terms:")
 
 for term, probability in zip(terms, probabilities):
-print(f"{probability:.4f}: {term}")
+    print(f"{probability:.4f}: {term}")
 ```
 
-## Training Results
-
-- **Epochs**: 20
-- **Test F1**: 0.63
-- **Test Precision**: 0.78
-- **Test Recall**: 0.53
-
 ## References:
 
 >- A. Rives, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences, 2021.
 >- Z. Lin, et al. Evolutionary-scale prediction of atomic level protein structure with a language model, 2022.
 >- G. A. Merino, et al. Hierarchical deep learning for predicting GO annotations by integrating protein knowledge, 2022.
->- I. Friedberg, et al. CAFA 5 Protein Function Prediction. https://kaggle.com/competitions/cafa-5-protein-function-prediction, 2023.
 >- M. Ashburner, et al. Gene Ontology: tool for the unification of biology, 2000.