Systematic auditing is essential to debiasing machine learning in biology

Representational biases that are common in biological data can inflate prediction performance and confound our understanding of how and what machine learning (ML) models learn from large complicated datasets. However, auditing for these biases is not a common practice in ML in the life sciences. Here, we devise a systematic auditing framework and harness it to audit three different ML applications of significant therapeutic interest: prediction frameworks of protein-protein interactions, drug-target bioactivity, and MHC-peptide binding. Through this, we identify unrecognized biases that hinder the ML process and result in low model generalizability. Ultimately, we show that, when there is insufficient signal in the training data, ML models are likely to learn primarily from representational biases.

Time Variant Multi Perspective Hierarchical Clustering Algorithm for Predicting Student Interest in Sports Mining

Predicting performance of students in sports is analyzed and studied. There are many techniques identified for the prediction of sports interest and they are not producing expected value. Towards performance development, a novel time variant multi perspective hierarchical clustering approach towards user interest prediction. The proposed time variant model reads the sports log and groups them according to the time domain. The entire log has been split into different of clusters as like time window. Then using window log, the method splits the logs according to different sports. For each time window, the method identifies the list of actions or sports played or tagged or chat with other users. Using the class of log, the method identifies the category of sports log and for each category of sports, the method compute the sports strike strength (SSS). Based on the value of SSS, the method identifies the user interest. Similarly, the interest of the student at each time window has been identified and used to generate the knowledge. The proposed method improves the performance of sports interest prediction on students with less false ratio.