Construction of a risk assessment and prediction model for athlete doping use based on bioinformatics

A suitable approach to identifying doping behavior among athletes is to use advanced techniques. Bioinformatics can analyze large biological databases. It has potential approaches for mapping out decision models. Doping substances can severely distort an athlete’s biomechanical performance. For example, stimulants may enhance short-term power output but disrupt the natural rhythm and coordination of muscle contractions, leading to imbalanced forces and increased risk of musculoskeletal injuries. This abnormal biomechanical loading can affect joint stability and movement efficiency. n training, doping gives a false impression of enhanced capacity. Athletes might overtrain, ignoring proper recovery periods. Their bodies, under the influence of doping, can’t follow the normal adaptive process of training, leading to a breakdown in the physiological systems. Recovery is also hampered. Doping can disrupt the body’s hormonal and metabolic balance, slowing down tissue repair and regeneration. Genetic predispositions, which might make an athlete more receptive to doping’s effects, along with lower recovery rates and high competitive stress levels, are identified as key doping risk factors. Bioinformatics collects multi-source data like genomic profiles, hormone levels, and metabolic markers. Advanced tools analyze these to expose patterns and correlations related to doping risks. Machine learning trains a prediction model using historical doping data and biological signatures. Validated via simulations and real-world tests, it predicts doping risks. Sports authorities can use the resulting risk matrix to detect potential dopers early, promoting clean sports.