Additional file 3 of KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites
Creators: name: Akbari Rokn Abadi, Saeedeh
name: Tabatabaei, SeyedehFatemeh
name: Koohi, Somayyeh
Publisher: name: figshare
Year: 2024
Subjects: subject: Artificial Intelligence and Image Processing
subject: FOS: Computer and information sciences

description: Additional file 3: Figure S1. An example of CGR and FCGR encoding methods. Table S1. Details of the KDeep models adopted for DNA and RNA data. Table S2. Details of 24 sub-datasets of RBP-24 dataset. Figure S2. The hardware performance adopting five encoding methods: one-hot, word2vec (50), word2vec (100), 2Lk (3, 2), and 2Lk (3, 3) with the same architecture for dataset1 of RBP-31; a) GPU Power Usage (W), b) GPU Power Usage (%), c) GPU Memory Allocated (%), d) GPU Time Spent Accessing Memory (%), e) GPU Temp (℃), f) GPU Utilization (%), g) Network Traffic (bytes), h) Disk Utilization (%), i) Process CPU Threads In Use, j) Process Memory Available (non-swap) (MB), k) Process Memory In Use (non-swap) (%), l) Process Memory In Use (non-swap) (MB), m) System Memory Utilization (%), n) CPU Utilization (%). Figure S3. The hardware performance adopting five encoding methods: one-hot, word2vec (50), word2vec (100), 2Lk (3, 2), and 2Lk (3, 3) with the same architecture for dataset 1 of RBP-24; a) GPU Power Usage (W), b) GPU Power Usage (%), c) GPU Memory Allocated (%), d) GPU Time Spent Accessing Memory (%), e) GPU Temp (℃), f) GPU Utilization (%), g) Network Traffic (bytes), h) Disk Utilization (%), i) Process CPU Threads In Use, j) Process Memory Available (non-swap) (MB), k) Process Memory In Use (non-swap) (%), l) Process Memory In Use (non-swap) (MB), m) System Memory Utilization (%), n) CPU Utilization (%). Table S3. Comparing auROC and auPR of five encodings methods: one-hot, word2vec (50), word2vec (100), 2Lk (3, 2), and 2Lk (3, 3) with fixed predictor architecture KDeep for first dataset of RBP-24. Table S4. Hyperparameter and model detail and samples number. Figure S4 Total number of trainable parameters for 5 sequence encoding method – RNA datasets. Figure S5. Comparing auROC and auPR distribution of three binding site predictor methods for DNAsite dataset; a) auROC for Dnase samples of DNAsite dataset, b) auPR for Dnase samples of DNAsite dataset, c) auROC for TF samples of DNAsite dataset, d) auPR for TF samples of DNAsite dataset, e) auROC for Histone samples of DNAsite dataset, f) auPR for Histone samples of DNAsite dataset. Figure S6. Total number of trainable parameters for 3 DNA binding site predictor methods. Figure S7. Interpretation process in the KDeep method. Figure S8. Extracted RNA motifs, as compared to the known motifs, using the TOMTOM tool with KDeep for jaspar dataset; a to e for experience 2 for RBP-24 datasets, f to m for experience 4 of RBP-31 datasets, n to x for experience 20 of RBP-31 datasets. Figure S9. Extracted DNA motifs, as compared to the known motifs, using the TOMTOM tool with KDeep for jaspar dataset. Figure S10. Extracted DNA motifs, as compared to the known motifs, using the TOMTOM tool with KDeep+ for jaspar dataset. Figure S11. Heatmap of output score of attention layer in KDeep+ for DNA. a) Heatmap is related to all the samples that have at least one of the 14 (399, 383, 382, 377, 373, 351, 314, 313, 392, 269, 223, 184) TF type labels positive. b) Heatmap is related to one sample that have at least one of the 14 (399, 383, 382, 377, 373, 351, 314, 313, 292, 269, 223, 184) TF type labels positive and it shows that the middle of this sample has the ability of protein binding.