Cancer of unknown primary site (CUPS) is a type of metastatic tumor for which the sites of tumor origin cannot be determined. Precise diagnosis of the tissue origin for metastatic CUPS is crucial for developing treatment schemes to improve patient prognosis. Recently, there have been many studies using various cancer biomarkers to predict the tissue-of-origin (TOO) of CUPS. However, only a very few of them use copy number alteration (CNA) to trance TOO. In this paper, a two-step computational framework called CNA_origin is introduced to predict the tissue-of-origin of a tumor from its gene CNA levels. CNA_origin set up an intellectual deep-learning network mainly composed of an autoencoder and a convolution neural network (CNN). Based on real datasets released from the public database, CNA_origin had an overall accuracy of 83.81% on 10-fold cross-validation and 79% on independent datasets for predicting tumor origin, which improved the accuracy by 7.75 and 9.72% compared with the method published in a previous paper. Our results suggested that the autoencoder model can extract key characteristics of CNA and that the CNN classifier model developed in this study can predict the origin of tumors robustly and effectively. CNA_origin was written in Python and can be downloaded from https://github.com/YingLianghnu/CNA_origin.

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Differential evolution (DE) is one of the most popular and powerful evolutionary algorithms for the real-parameter global continuous optimization problems. However, how to adapt into combinatorial optimization problems without sacrificing the original evolution mechanism of DE is harder work to the researchers to design an efficient binary differential evolution (BDE). To tackle this problem, this paper presents a novel BDE based on dichotomous mechanism for knapsack problems, called DBDE, in which two new proposed methods (i.e., dichotomous mutation and dichotomous crossover) are employed. DBDE almost has any difference with original DE and no additional module or computation has been introduced. The experimental studies have been conducted on a suite of 0-1 knapsack problems and multidimensional knapsack problems. Experimental results have verified the quality and effectiveness of DBDE. Comparison with three state-of-the-art BDE variants and other two state-of-the-art binary particle swarm optimization (PSO) algorithms has proved that DBDE is a new competitive algorithm.

When different types of test are performed on software, from unit test, to component test to system test many bugs can be detected and recorded in bug reports. Developers must then fix them one by one. However, an important job before fixing bugs is to locate them in source code. Given a large scale software project with hundreds of bugs, it is a tedious job to locate the problems in source code. Feature location is a solution of this problem. Feature location seeks to identify pieces of source code corresponding to a specific feature, where a feature is defined as a function in software. Since bugs have the same attributes as features, they can be treated as features. In this paper, we provide a technique to achieve feature location. The approach uses a combination of lexical information and structural information. We combine Latent Semantic Indexing with Call Graphs to on a small test case to assist in feature location. Comparing our approach to an approach that uses LSI shows improved accuracy ad effectiveness.