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@@ -109,3 +109,7 @@ Unpaired_Cross_molecular_Benchmark/enhancer_design/Val.csv filter=lfs diff=lfs m
 Unpaired_Cross_molecular_Benchmark/Isoform/HSPE1.csv filter=lfs diff=lfs merge=lfs -text
 Unpaired_Cross_molecular_Benchmark/ec/train.csv filter=lfs diff=lfs merge=lfs -text
 Unpaired_Cross_molecular_Benchmark/contact/train.csv filter=lfs diff=lfs merge=lfs -text
+Multi_molecular_Benchmark/AntibodyAntigen/train.csv filter=lfs diff=lfs merge=lfs -text
+Multi_molecular_Benchmark/ncRNAProteinInter/train.csv filter=lfs diff=lfs merge=lfs -text
+Multi_molecular_Benchmark/EnhancerPromoter/train.csv filter=lfs diff=lfs merge=lfs -text
+Multi_molecular_Benchmark/sirnaEfficiency/train.csv filter=lfs diff=lfs merge=lfs -text

Multi_molecular_Benchmark/AntibodyAntigen/train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aee655263ab973774107b1879a845b64fe627c794ddfdf2693dea76f09c0bc4d
+size 24559020

Multi_molecular_Benchmark/EnhancerPromoter/balance.py

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+import pandas as pd
+from sklearn.utils import resample
+
+def balance_data(file_path, target_count):
+    data = pd.read_csv(file_path)
+    # 分离两个类别
+    data_label0 = data[data['label'] == 0]
+    data_label1 = data[data['label'] == 1]
+    
+    # 下采样多数类
+    data_label0_downsampled = data_label0.sample(n=target_count, random_state=42)  # 使用sample进行随机下采样
+
+    # 合并回数据集
+    balanced_data = pd.concat([data_label0_downsampled, data_label1])
+    
+    # 重新保存文件
+    balanced_data.to_csv(file_path, index=False)
+    print(f'Balanced data saved to {file_path}')
+
+
+# 路径配置
+train_file_path = 'train.csv'
+val_file_path = 'val.csv'
+test_file_path = 'test.csv'
+
+# 获取每个类别在train、val和test中的目标数量
+train_majority_count = 7144  # train.csv中Label 1的数量
+val_majority_count = 886    # val.csv中Label 1的数量
+test_majority_count = 881   # test.csv中Label 1的数量
+
+
+# 平衡每个文件
+balance_data(train_file_path, train_majority_count)
+balance_data(val_file_path, val_majority_count)
+balance_data(test_file_path, test_majority_count)

Multi_molecular_Benchmark/EnhancerPromoter/stat.py

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+# import pandas as pd
+# train_file = '/home/bingxing2/ailab/group/ai4bio/public/multi-omics/multi-omics/downstream/EnhancerPromoter/train.csv'
+# val_file = '/home/bingxing2/ailab/group/ai4bio/public/multi-omics/multi-omics/downstream/EnhancerPromoter/val.csv'
+# test_file = '/home/bingxing2/ailab/group/ai4bio/public/multi-omics/multi-omics/downstream/EnhancerPromoter/test.csv'
+
+# # File paths
+# file_paths = [train_file, val_file, test_file]
+
+
+# def calculate_combined_statistics(file_paths, column_name):
+#     """ Calculate max and mean lengths of the specified column across multiple CSV files """
+#     combined_df = pd.concat([pd.read_csv(file_path) for file_path in file_paths], ignore_index=True)
+#     # Drop rows with NaN values in the specified column
+#     combined_df = combined_df[combined_df[column_name].notna()]
+#     # Compute lengths of the sequences
+#     lengths = combined_df[column_name].apply(len)
+#     return lengths.max(), lengths.mean()
+
+
+# # Calculate statistics for siRNA_sense_seq
+# print("enhancer statistics:")
+# max_len, mean_len = calculate_combined_statistics(file_paths, 'enhancer')
+# print(f"Max length = {max_len}, Mean length = {mean_len:.2f}")
+
+# # Calculate statistics for gene_target_seq
+# print("\npromoter statistics:")
+# max_len, mean_len = calculate_combined_statistics(file_paths, 'promoter')
+# print(f"Max length = {max_len}, Mean length = {mean_len:.2f}")
+
+
+# import matplotlib.pyplot as plt
+
+# def calculate_lengths(file_paths, column_name):
+#     """ Calculate lengths of the specified column across multiple CSV files """
+#     lengths = []
+#     for file_path in file_paths:
+#         df = pd.read_csv(file_path)
+#         # Drop rows with NaN values in the specified column
+#         df = df[df[column_name].notna()]
+#         # Compute lengths of the sequences
+#         lengths.extend(df[column_name].apply(len))
+#     return lengths
+
+# # File paths
+
+
+# # Calculate lengths for siRNA_sense_seq and gene_target_seq
+# siRNA_lengths = calculate_lengths(file_paths, 'enhancer')
+# gene_target_lengths = calculate_lengths(file_paths, 'promoter')
+
+# # Create histograms
+# plt.figure(figsize=(14, 7))
+
+# # Plot for siRNA_sense_seq lengths
+# plt.subplot(1, 2, 1)
+# plt.hist(siRNA_lengths, bins=30, color='skyblue', edgecolor='black')
+# plt.title('enhancer Length Distribution')
+# plt.xlabel('Length')
+# plt.ylabel('Frequency')
+
+# # Plot for gene_target_seq lengths
+# plt.subplot(1, 2, 2)
+# plt.hist(gene_target_lengths, bins=30, color='lightgreen', edgecolor='black')
+# plt.title('promoter Length Distribution')
+# plt.xlabel('Length')
+# plt.ylabel('Frequency')
+
+# # Save the figure
+# plt.tight_layout()
+# plt.savefig('length_distributions.png')
+
+
+
+import pandas as pd
+
+# 定义 CSV 文件路径
+csv_files = ['train.csv',
+ 'val.csv', 
+ 'test.csv']
+
+# 初始化统计字典
+label_stats = {'train': {0: 0, 1: 0}, 'val': {0: 0, 1: 0}, 'test': {0: 0, 1: 0}}
+
+# 统计每个文件中的 label 数量
+for file in csv_files:
+    # 读取 CSV 文件
+    df = pd.read_csv(file)
+    
+    # 提取文件名（去掉 .csv 后缀）
+    file_name = file.split('.')[0]
+    
+    # 统计 label 列中的 0 和 1 的数量
+    label_counts = df['label'].value_counts()
+    
+    # 更新统计字典
+    for label in label_counts.index:
+        label_stats[file_name][label] = label_counts[label]
+
+# 打印统计结果
+for dataset, counts in label_stats.items():
+    print(f"{dataset}.csv")
+    print(f"Label 0: {counts[0]}")
+    print(f"Label 1: {counts[1]}")
+    print()

Multi_molecular_Benchmark/EnhancerPromoter/train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cfe2519da87dd0b2875006a33f8d28cc1d7b657903bdb35d7b2419df9942b26d
+size 71589312

Multi_molecular_Benchmark/ncRNAProteinInter/stat.py

@@ -0,0 +1,104 @@
+# import pandas as pd
+# train_file = '/home/bingxing2/ailab/group/ai4bio/public/multi-omics/multi-omics/downstream/EnhancerPromoter/train.csv'
+# val_file = '/home/bingxing2/ailab/group/ai4bio/public/multi-omics/multi-omics/downstream/EnhancerPromoter/val.csv'
+# test_file = '/home/bingxing2/ailab/group/ai4bio/public/multi-omics/multi-omics/downstream/EnhancerPromoter/test.csv'
+
+# # File paths
+# file_paths = [train_file, val_file, test_file]
+
+
+# def calculate_combined_statistics(file_paths, column_name):
+#     """ Calculate max and mean lengths of the specified column across multiple CSV files """
+#     combined_df = pd.concat([pd.read_csv(file_path) for file_path in file_paths], ignore_index=True)
+#     # Drop rows with NaN values in the specified column
+#     combined_df = combined_df[combined_df[column_name].notna()]
+#     # Compute lengths of the sequences
+#     lengths = combined_df[column_name].apply(len)
+#     return lengths.max(), lengths.mean()
+
+
+# # Calculate statistics for siRNA_sense_seq
+# print("enhancer statistics:")
+# max_len, mean_len = calculate_combined_statistics(file_paths, 'enhancer')
+# print(f"Max length = {max_len}, Mean length = {mean_len:.2f}")
+
+# # Calculate statistics for gene_target_seq
+# print("\npromoter statistics:")
+# max_len, mean_len = calculate_combined_statistics(file_paths, 'promoter')
+# print(f"Max length = {max_len}, Mean length = {mean_len:.2f}")
+
+
+# import matplotlib.pyplot as plt
+
+# def calculate_lengths(file_paths, column_name):
+#     """ Calculate lengths of the specified column across multiple CSV files """
+#     lengths = []
+#     for file_path in file_paths:
+#         df = pd.read_csv(file_path)
+#         # Drop rows with NaN values in the specified column
+#         df = df[df[column_name].notna()]
+#         # Compute lengths of the sequences
+#         lengths.extend(df[column_name].apply(len))
+#     return lengths
+
+# # File paths
+
+
+# # Calculate lengths for siRNA_sense_seq and gene_target_seq
+# siRNA_lengths = calculate_lengths(file_paths, 'enhancer')
+# gene_target_lengths = calculate_lengths(file_paths, 'promoter')
+
+# # Create histograms
+# plt.figure(figsize=(14, 7))
+
+# # Plot for siRNA_sense_seq lengths
+# plt.subplot(1, 2, 1)
+# plt.hist(siRNA_lengths, bins=30, color='skyblue', edgecolor='black')
+# plt.title('enhancer Length Distribution')
+# plt.xlabel('Length')
+# plt.ylabel('Frequency')
+
+# # Plot for gene_target_seq lengths
+# plt.subplot(1, 2, 2)
+# plt.hist(gene_target_lengths, bins=30, color='lightgreen', edgecolor='black')
+# plt.title('promoter Length Distribution')
+# plt.xlabel('Length')
+# plt.ylabel('Frequency')
+
+# # Save the figure
+# plt.tight_layout()
+# plt.savefig('length_distributions.png')
+
+
+
+import pandas as pd
+
+# 定义 CSV 文件路径
+csv_files = ['train.csv',
+ 'val.csv', 
+ 'test.csv']
+
+# 初始化统计字典
+label_stats = {'train': {0: 0, 1: 0}, 'val': {0: 0, 1: 0}, 'test': {0: 0, 1: 0}}
+
+# 统计每个文件中的 label 数量
+for file in csv_files:
+    # 读取 CSV 文件
+    df = pd.read_csv(file)
+    
+    # 提取文件名（去掉 .csv 后缀）
+    file_name = file.split('.')[0]
+    
+    # 统计 label 列中的 0 和 1 的数量
+    label_counts = df['label'].value_counts()
+    
+    # 更新统计字典
+    for label in label_counts.index:
+        label_stats[file_name][label] = label_counts[label]
+
+# 打印统计结果
+for dataset, counts in label_stats.items():
+    print(f"{dataset}.csv")
+    print(f"Label 0: {counts[0]}")
+    print(f"Label 1: {counts[1]}")
+    print()

Multi_molecular_Benchmark/ncRNAProteinInter/train.csv

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+size 35832781

Multi_molecular_Benchmark/sirnaEfficiency/train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:edf9d0aecfd6911a7f4cee76e2413d478646399fa7cac626ac598c36ad2cb3a3
+size 80975764