Update README.md

README.md

@@ -6,17 +6,15 @@ tags:
 - chemistry
 - mass-spectrometry
 - isotopic-patterns
-- binary-classification
 - chlorine
+- tabular
 pretty_name: Cl-Containing Compound (MS1 Features)
 size_categories:
 - 100K<n<1M
 source_datasets:
 - PubChem (molecular formulas)
 task_categories:
-- binary-classification
-task_ids:
-- binary-classification
+- tabular-classification
 configs:
 - config_name: default
   data_files:
@@ -26,42 +24,22 @@ configs:
     path: 20%_154568_test_binary.rds
 dataset_info:
   features:
-    - name: mz0           # mz of M
+    - name: mz0
       dtype: float32
-    - name: int2_o_int0   # M+2 / M
+    - name: int2_o_int0
       dtype: float32
-    - name: int1_o_int0   # M+1 / M
+    - name: int1_o_int0
       dtype: float32
-    - name: RI2_RI1       # (M+2/M) - (M+1/M)
+    - name: RI2_RI1
       dtype: float32
-    - name: mz_2_0        # mz(M+2) - mz(M)
+    - name: mz_2_0
       dtype: float32
-    - name: mz_1_0        # mz(M+1) - mz(M)
+    - name: mz_1_0
       dtype: float32
-    - name: has_cl        # 1=Cl-containing, 0=non-Cl
+    - name: has_cl
       dtype: int8
   config_name: default
 ---
 
 # Dataset Summary
-Binary classification of chlorine presence based on simulated MS1 isotopic patterns. MS1 peaks (M, M+1, M+2, M+3, M+4) were simulated for PubChem molecular formulas, and six features were engineered: mz0, int2_o_int0, int1_o_int0, RI2_RI1, mz_2_0, mz_1_0. Label has_cl indicates whether the formula contains chlorine.
-
-- Raw counts: 968,442 non-Cl; 386,420 Cl (1Cl: 185,303; 2Cl: 117,566; 3–5Cl: 83,551).
-- Balancing: downsampled non-Cl to 386,420 → total 772,840.
-- Splits: train 618,272 (80%), test 154,568 (20%).
-- Files: 968442_non_cl_filter_S10.rds, 386420_cl_data.rds, 80%_618272_train_binary.rds, 20%_154568_test_binary.rds.
-
-## Citation
-```bibtex
-@article{doi:10.1021/acs.analchem.3c05124,
-  author = {Zhao, Tingting and Wawryk, Nicholas J. P. and Xing, Shipei and Low, Brian and Li, Gigi and Yu, Huaxu and Wang, Yukai and Shen, Qiming and Li, Xing-Fang and Huan, Tao},
-  title = {ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis},
-  journal = {Analytical Chemistry},
-  volume = {96},
-  number = {6},
-  pages = {2590-2598},
-  year = {2024},
-  doi = {10.1021/acs.analchem.3c05124},
-  note = {PMID: 38294426},
-  url = {https://doi.org/10.1021/acs.analchem.3c05124},
-  eprint
+Binary classification of chlorine presence from simulated MS1 isotopic features (mz0, int2_o_int0, int1_o_int0, RI2_RI1, mz_2_0, mz_1_0). Splits: 80% train (618,272), 20% test