1. Liga Classic Group 3 stats & predictions

Resultados Recientes

• FC Luzern vs. FC St. Gallen: Un partido reñido que terminó en empate 1-1, demostrando la paridad entre ambos equipos.
• FC Sion vs. BSC Young Boys: Los Young Boys se llevaron la victoria con un marcador de 2-0, gracias a goles en la segunda mitad.
• FC Thun vs. FC Luzern: Un encuentro emocionante que terminó 3-2 a favor del FC Thun, mostrando su capacidad para remontar.

Suspensos y Lesiones

Es importante estar al tanto de las bajas clave que pueden afectar el desempeño de los equipos:

• FC St. Gallen: El defensor central principal estará fuera por varias semanas debido a una lesión en el tendón de Aquiles.
• BSC Young Boys: El mediocampista estrella se perderá el próximo partido tras recibir una tarjeta roja en el último encuentro.

Análisis Táctico

Cada equipo tiene sus fortalezas y debilidades tácticas. A continuación, se presenta un análisis detallado:

• FC Luzern: Su defensa sólida puede ser clave contra equipos que dependen mucho del ataque directo.
• FC St. Gallen: Con un ataque veloz, pueden explotar cualquier debilidad en la defensa rival.
• FC Sion: Su habilidad para presionar alto puede desorganizar a equipos menos disciplinados tácticamente.
• BSC Young Boys: La combinación de juventud y experiencia les permite adaptarse rápidamente a diferentes situaciones durante el partido.
• FC Thun: Su juego colectivo puede ser efectivo contra equipos que se cierran demasiado atrás.

Predicciones de Resultados

Aquí te ofrecemos algunas predicciones basadas en el análisis reciente:

• Próximo Partido: FC Luzern vs. FC Sion: Predicción: Empate 1-1. Ambos equipos tienen defensas fuertes y podrían neutralizarse mutuamente.
• Próximo Partido: BSC Young Boys vs. FC Thun: Predicción: Victoria para BSC Young Boys 2-1. Los Young Boys tienen ventaja en forma y plantilla.
• Próximo Partido: FC St. Gallen vs. FC Thun: Predicción: Victoria para FC St. Gallen 2-0. A pesar de las bajas, su ataque puede superar la defensa del Thun.

Apostar a Favoritos vs. Outsiders

Cuando apuestes a favoritos como BSC Young Boys, considera apostar a resultados específicos como "Victoria con ambos equipos marcando". Por otro lado, apostar a outsiders como FC Sion podría ofrecer mejores cuotas si logran sorprender a un favorito.

Apostar al Total de Goles

Partidos con equipos conocidos por su ofensiva, como FC St. Gallen contra defensas más débiles, pueden ser buenos candidatos para apostar al "Más de 2.5 goles". Analiza las estadísticas recientes para tomar decisiones informadas.

Apostar a Resultados Parciales

A veces, apostar a resultados parciales puede ser más rentable que apostar al resultado final del partido completo. Observa cómo se desarrolla el primer tiempo para ajustar tus apuestas según sea necesario.

Goles Anotados y Recibidos por Equipo

• FC Luzern: Goles Anotados: 15 | Goles Recibidos: 10 | Diferencia de Goles: +5
• FC St. Gallen: Goles Anotados: 18 | Goles Recibidos: 12 | Diferencia de Goles: +6
• FC Sion: Goles Anotados: 14 | Goles Recibidos: 14 | Diferencia de Goles: 0
• BSC Young Boys: Goles Anotados: 20 | Goles Recibidas: 8 | Diferencia de Goles: +12
• FC Thun: Goles Anotados: 13 | Goles Recibidas: 15 | Diferencia de Goles: -2

Rendimiento en Casa vs Fuera de Casa

• FC Luzern - Rendimiento en Casa: 5 Victorias, 1 Empate, 0 Derrotas | Fuera de Casa: 2 Victorias, 2 Empates, 3 Derrotas
• FC St. Gallen - Rendimiento en Casa: 6 Victorias, 0 Empates, 1 Derrota | Fuera de Casa: 1 Victoria, 3 Empates, 3 Derrotas
gjain92/Protein-Protein-Interaction-Prediction<|file_sep|>/code/README.md # Code for Protein-Protein Interaction Prediction using Deep Learning The code is written in Python and uses the following packages: * Numpy * Scipy * Keras (with Tensorflow backend) * Pandas ## Data Preprocessing We provide data pre-processing scripts for the following data types: * Protein sequences * Protein structures (PDB) * GO annotations ### Protein Sequences We provide two scripts for pre-processing protein sequences: * get_features.py * get_seq_data.py The script `get_features.py` reads protein sequences from the UniProt database and encodes them as one-hot vectors using amino acid alphabet. The script `get_seq_data.py` reads protein sequences from the UniProt database and encodes them using various amino acid physiochemical properties as described in [DeepDDI](https://github.com/charithranga/deepddi). ### Protein Structures (PDB) We provide two scripts for pre-processing protein structures: * get_struct_features.py * get_struct_data.py The script `get_struct_features.py` reads protein structures from the PDB database and encodes them as one-hot vectors using amino acid alphabet. The script `get_struct_data.py` reads protein structures from the PDB database and encodes them using various amino acid physiochemical properties as described in [DeepDDI](https://github.com/charithranga/deepddi). ### GO Annotations We provide two scripts for pre-processing GO annotations: * get_go_annotations.py * get_go_data.py The script `get_go_annotations.py` reads GO annotations from the UniProt database. The script `get_go_data.py` encodes GO annotations as one-hot vectors. ## Model Training We provide scripts for training the following models: * Deep Interaction Network (DIN) model * Convolutional Neural Network (CNN) model * Long Short Term Memory (LSTM) model * Attention-based LSTM model The script `din_model.py` trains DIN model. The script `cnn_model.py` trains CNN model. The script `lstm_model.py` trains LSTM model. The script `attn_lstm_model.py` trains Attention-based LSTM model. ## Model Evaluation We provide scripts for evaluating the following models: * Deep Interaction Network (DIN) model * Convolutional Neural Network (CNN) model * Long Short Term Memory (LSTM) model * Attention-based LSTM model The script `din_evaluate.py` evaluates DIN model on test dataset. The script `cnn_evaluate.py` evaluates CNN model on test dataset. The script `lstm_evaluate.py` evaluates LSTM model on test dataset. The script `attn_lstm_evaluate.py` evaluates Attention-based LSTM model on test dataset. <|repo_name|>gjain92/Protein-Protein-Interaction-Prediction<|file_sep|>/code/attn_lstm_evaluate.py import os import sys import numpy as np import pandas as pd from keras.models import load_model from keras import backend as K from sklearn.metrics import roc_auc_score def evaluate_attn_lstm_model(model_path): # Load test data X_test = np.load("data/X_test.npy") Y_test = np.load("data/Y_test.npy") print("Test data loaded") # Load trained attention-based LSTM model model = load_model(model_path) print("Attention-based LSTM model loaded") # Evaluate attention-based LSTM model on test data Y_pred = model.predict(X_test) AUC = roc_auc_score(Y_test[:,0], Y_pred[:,0]) print("AUC on test set:", AUC) if __name__ == '__main__': if len(sys.argv) != 2: print("Usage:", sys.argv[0], "n") exit(1) model_path = sys.argv[1] if not os.path.exists(model_path): print("Model not found:", model_path) exit(1) evaluate_attn_lstm_model(model_path)<|repo_name|>gjain92/Protein-Protein-Interaction-Prediction<|file_sep|>/code/get_seq_data.py import os import sys import subprocess import pandas as pd def get_seq_data(): # Read sequence information from UniProt database seq_df = pd.read_csv('data/uniprot/uniprot_sprot.xml.gz', sep='t', compression='gzip', usecols=['Entry', 'Sequence']) seq_df.columns = ['UniprotID', 'Sequence'] seq_df.set_index('UniprotID', inplace=True) print("Sequence information read from UniProt database") # Map UniProt IDs to PDB IDs pdbmap_df = pd.read_csv('data/pdb/pdb_chain_uniprot.txt', sep='t') pdbmap_df.columns = ['PDBID', 'ChainID', 'UniprotID'] pdbmap_df.set_index(['PDBID', 'ChainID'], inplace=True) print("UniProt IDs mapped to PDB IDs") # Read PDB information from PDB database pdb_df = pd.read_csv('data/pdb/pdb_chain_info.txt', sep='t') pdb_df.columns = ['PDBID', 'ChainID', 'EntityID'] pdb_df.set_index(['PDBID', 'ChainID'], inplace=True) print("PDB information read from PDB database") # Merge sequence information with PDB information using UniProt IDs seq_pdb_df = seq_df.join(pdbmap_df).join(pdb_df) print("Sequence information merged with PDB information") # Read sequence features file downloaded from DeepDDI repository features_file = open('data/deepddi/features.csv') features_file.readline() features_dict = {} for line in features_file: features_dict[line.split(',')[0]] = line.strip().split(',')[1:-1] features_file.close() print("Sequence features read from DeepDDI repository") # Initialize output dataframe out_df = pd.DataFrame(columns=['UniprotID', 'Sequence']) out_df.set_index('UniprotID', inplace=True) # Iterate over each row of merged dataframe and extract sequence features for row in seq_pdb_df.iterrows(): uniprot_id = row[0][0] seq_features = [] for i in range(len(row[1]['Sequence'])): residue_id = row[1]['EntityID'] + '_' + str(i+1) residue_feature_line = features_dict[residue_id] residue_features = [float(x) for x in residue_feature_line] seq_features.append(residue_features) out_df.loc[uniprot_id] = [seq_features] out_df.to_csv('data/seq_data.csv') print("Sequence features extracted and saved") if __name__ == '__main__': get_seq_data()<|file_sep|># Protein-Protein Interaction Prediction using Deep Learning ## Introduction This repository contains code for predicting protein-protein interactions using deep learning methods. ## Data Sources We used the following data sources: * [UniProt](https://www.uniprot.org/) for protein sequences and annotations. * [PDB](https://www.rcsb.org/) for protein structures. * [DeepDDI](https://github.com/charithranga/deepddi) for sequence features. ## Data Preprocessing We provide data pre-processing scripts for the following data types: * Protein sequences * Protein structures (PDB) * GO annotations ### Protein Sequences We provide two scripts for pre-processing protein sequences: * get_features.py * get_seq_data.py The script `get_features.py` reads protein sequences from the UniProt database and encodes them as one-hot vectors using amino acid alphabet. The script `get_seq_data.py` reads protein sequences from the UniProt database and encodes them using various amino acid physiochemical properties as described in [DeepDDI](https://github.com/charithranga/deepddi). ### Protein Structures (PDB) We provide two scripts for pre-processing protein structures: * get_struct_features.py * get_struct_data.py The script `get_struct_features.py` reads protein structures from the PDB database and encodes them as one-hot vectors using amino acid alphabet. The script `get_struct_data.py` reads protein structures from the PDB database and encodes them using various amino acid physiochemical properties as described in [DeepDDI](https://github.com/charithranga/deepddi). ### GO Annotations We provide two scripts for pre-processing GO annotations: * get_go_annotations.py * get_go_data.py The script `get_go_annotations.py` reads GO annotations from the UniProt database. The script `get_go_data.py` encodes GO annotations as one-hot vectors. ## Model Training We provide scripts for training the following models: * Deep Interaction Network (DIN) model * Convolutional Neural Network (CNN) model * Long Short Term Memory (LSTM) model * Attention-based LSTM model The script `din_model.py` trains DIN