Classification of tumor types using XGBoost machine learning model: a vector space transformation of genomic alterations.

BACKGROUND: Machine learning (ML) represents a powerful tool to capture relationships between molecular alterations and cancer types and to extract biological information. Here, we developed a plain ML model aimed at distinguishing cancer types based on genetic lesions, providing an additional tool to improve cancer diagnosis, particularly for tumors of unknown origin. METHODS: TCGA data from 9,927 samples spanning 32 different cancer types were downloaded from cBioportal. A vector space model type data transformation technique was designed to build consistently homogeneous new datasets containing, as predictive features, calls for somatic point mutations and copy number variations at chromosome arm-level, thus allowing the use of the XGBoost classifier models. Considering the imbalance in the dataset, due to large difference in the number of cases for each tumor, two preprocessing strategies were considered: i) setting a percentage cut-off threshold to remove less represented cancer types, ii) dividing cancer types into different groups based on biological criteria and training a specific XGBoost model for each of them. The performance of all trained models was mainly assessed by the out-of-sample balanced accuracy (BACC) and the AUC scores. RESULTS: The XGBoost classifier achieved the best performance (BACC 77%; AUC 97%) on a dataset containing the 10 most represented tumor types. Moreover, dividing the 18 most represented cancers into three different groups (endocrine-related carcinomas, other carcinomas and other cancers),such analysis models achieved 78%, 71% and 86% BACC, respectively, with AUC scores greater than 96%. In addition, the model capable of linking each group to a specific cancer type reached 81% BACC and 94% AUC. Overall, the diagnostic potential of our model was comparable/higher with respect to others already described in literature and based on similar molecular data and ML approaches. CONCLUSIONS: A boosted ML approach able to accurately discriminate different cancer types was developed. The methodology builds datasets simpler and more interpretable than the original data, while keeping enough information to accurately train standard ML models without resorting to sophisticated Deep Learning architectures. In combination with histopathological examinations, this approach could improve cancer diagnosis by using specific DNA alterations, processed by a replicable and easy-to-use automated technology. The study encourages new investigations which could further increase the classifier's performance, for example by considering more features and dividing tumors into their main molecular subtypes.

Insights into the epidemiology, pathogenesis, and differential diagnosis of schistosomiasis.

Schistosomiasis is a neglected tropical disease that is prevalent in low- and middle-income countries. There are five human pathogenic species, of which Schistosoma haematobium, Schistosoma mansoni and Schistosoma japonicum are the most prevalent worldwide and cause the greatest burden of disease in terms of mortality and morbidity. In addition, hybrid schistosomes have been identified through molecular analysis. Human infection occurs when cercariae, the larval form of the parasite, penetrate the skin of people while bathing in contaminated waters such as lakes and rivers. Schistosomiasis can cause both urogenital and intestinal symptoms. Urogenital symptoms include haematuria, bladder fibrosis, kidney damage, and an increased risk of bladder cancer. Intestinal symptoms may include abdominal pain, sometimes accompanied by diarrhoea and blood in the stool. Schistosomiasis affects more than 250 million people and causes approximately 70 million Disability-Adjusted Life Years (DALYs), mainly in Africa, South America, and Asia. To control infection, it is essential to establish sensitive and specific diagnostic tests for epidemiological surveillance and morbidity reduction. This review provides an overview of schistosomiasis, with a focus on available diagnostic tools for Schistosoma spp. Current molecular detection methods and progress in the development of new diagnostics for schistosomiasis infection are also discussed.

The new SUMPOT to predict postoperative complications using an Artificial Neural Network.

An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohort of 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units, high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, and a testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of the accuracy in detecting those patients who will develop postoperative complications. A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperative complications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classification accuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51 were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified). The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications, suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirm its applicability in routine clinical practice.

Decomposition Techniques for Multilayer Perceptron Training.

In this paper, we consider the learning problem of multilayer perceptrons (MLPs) formulated as the problem of minimizing a smooth error function. As well known, the learning problem of MLPs can be a difficult nonlinear nonconvex optimization problem. Typical difficulties can be the presence of extensive flat regions and steep sided valleys in the error surface, and the possible large number of training data and of free network parameters. We define a wide class of batch learning algorithms for MLP, based on the use of block decomposition techniques in the minimization of the error function. The learning problem is decomposed into a sequence of smaller and structured minimization problems in order to advantageously exploit the structure of the objective function. Theoretical convergence results are established, and a specific algorithm is constructed and evaluated through an extensive numerical experimentation. The comparisons with the state-of-the-art learning algorithms show the effectiveness of the proposed techniques.

Efficient synthesis of scutellarein.

Scutellarein is a component of Scutellaria, recently known as a potent cytotoxic agent on human leukaemia cells. The aim of this study was the synthesis of scutellarein and its methylated derivative. The new features are the innovating method to afford flavones from flavanones and the A-ring regioselective bromination step that lead to the target molecule by a facile and high-yielding pathway.