Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging

CrimeTensor : Fine-Scale Crime Prediction via Tensor Learning with Spatiotemporal Consistency

Crime poses a major threat to human life and property, which has been recognized as one of the most crucial problems in our society. Predicting the number of crime incidents in each region of a city before they happen is of great importance to fight against crime. There has been a great deal of research focused on crime prediction, ranging from introducing diversified data sources to exploring various prediction models. However, most of the existing approaches fail to offer fine-scale prediction results and take little notice of the intricate spatial-temporal-categorical correlations contained in crime incidents. In this article, we propose a tailor-made framework called CrimeTensor to predict the number of crime incidents belonging to different categories within each target region via tensor learning with spatiotemporal consistency. In particular, we model the crime data as a tensor and present an objective function which tries to take full advantage of the spatial, temporal, and categorical correlations contained in crime incidents. Moreover, a well-designed optimization algorithm which transforms the objective into a compact form and then applies CP decomposition to find the optimal solution is elaborated to solve the objective function. Furthermore, we develop an enhanced framework which takes a set of pre-selected regions to conduct prediction so as to further improve the computational efficiency of the optimization algorithm. Finally, extensive experiments are performed on both proprietary and public datasets and our framework significantly outperforms all the baselines in terms of each evaluation metric.

Pan-cancer classification by regularized multi-task learning

Classifying pan-cancer samples using gene expression patterns is a crucial challenge for the accurate diagnosis and treatment of cancer patients. Machine learning algorithms have been considered proven tools to perform downstream analysis and capture the deviations in gene expression patterns across diversified diseases. In our present work, we have developed PC-RMTL, a pan-cancer classification model using regularized multi-task learning (RMTL) for classifying 21 cancer types and adjacent normal samples using RNASeq data obtained from TCGA. PC-RMTL is observed to outperform when compared with five state-of-the-art classification algorithms, viz. SVM with the linear kernel (SVM-Lin), SVM with radial basis function kernel (SVM-RBF), random forest (RF), k-nearest neighbours (kNN), and decision trees (DT). The PC-RMTL achieves 96.07% accuracy and 95.80% MCC score for a completely unknown independent test set. The only method that appears as the real competitor is SVM-Lin, which nearly equalizes the accuracy in prediction of PC-RMTL but only when complete feature sets are provided for training; otherwise, PC-RMTL outperformed all other classification models. To the best of our knowledge, this is a significant improvement over all the existing works in pan-cancer classification as they have failed to classify many cancer types from one another reliably. We have also compared gene expression patterns of the top discriminating genes across the cancers and performed their functional enrichment analysis that uncovers several interesting facts in distinguishing pan-cancer samples.

Non-invasive assessment of liver quality in transplantation based on thermal imaging analysis

BACKGROUND AND OBJECTIVE

Liver quality evaluation is one of the vital steps for predicting the success of liver transplantation. Current evaluation methods, such as biopsy and visual inspection, which are either invasive or lack of consistent standards, provide limited predictive value of long-term transplant viability. Objective analytical models, based on the real-time infrared images of livers during perfusion and preservation, are proposed as novel methods to precisely evaluate donated liver quality.

METHODS

In this study, by using principal component analysis to extract infrared image features as predictors, we construct a multivariate logistic regression model for single liver quality evaluation, and a multi-task learning logistic regression model for cross-liver quality evaluation.

RESULTS

The single liver quality predictions show testing errors of 0%. The leave-one-liver-out predictions show testing errors ranging from 9% to 36%.

CONCLUSIONS

It is found that there is a strong correlation between the viability of livers and the infrared image features in both single liver and cross-liver quality evaluations. These analytical methods also determine that the selected significant infrared image features indicate regional difference in viability and show that more stringent pre-implantation evaluation may be needed to predict transplant outcomes.