Despite great advances, molecular cancer pathology is often limited to use a small number of biomarkers rather than the whole transcriptome, partly due to the computational challenges. Here, we introduce a novel architecture of DNNs that is capable of simultaneous inference of various properties of biological samples, through multi-task and transfer learning. We employed this architecture on mRNA transcription profiles of 10787 clinical samples from 34 classes (one healthy and 33 different types of cancer) from 27 tissues. Our system significantly outperforms prior works and classical machine learning approaches in predicting tissue-of-origin, normal or disease state and cancer type of each sample. Furthermore, it can predict miRNA transcription profile of each sample, which enables performing miRNA expression research when only mRNA transcriptome data are available. We also show this system is very robust against noise and missing values. Collectively, our results highlight applications of artificial intelligence in molecular cancer pathology and oncological research.

Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure of deep autoencoders that can encode a GEP into a vector of 30 numeric values, which we call the cell identity code (CIC). The original GEP can be reproduced from the CIC with an accuracy comparable to technical replicates of the same experiment. Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes. This network can use CIC to reproduce the GEP of the cell types it has never seen during the training. It also can resist some noise in the measurement of the GEP. Furthermore, we introduce classifier autoencoder, an architecture that can accurately identify cell type based on the GEP or the CIC.

This robot consists of a moving mechanism, motion-control hardware, software, and a wireless communication system. The motion mechanism consists of a drive unit, a steer unit, and a castor wheel. Motion control is carried out by a special control board that uses two microcontrollers to carry out the software system decisions and transfers them to the robot mechanics. The software system performs real-time object recognition at the rate of 16 frames a second.

In practice, by calculating the distance between ball center and robot geometrical center, the robot is commanded to rotate around the ball center. Figure 1 shows a picture of our player robot. Our fast robotics research centers to construct a team of image-processing algorithm can process as robots that could play indoor soccer with many as 16 frames a second and can recognize another team according certain rules and regulations. Our team became done using a wireless network under TCP champion among 21 teams in the middlesize-league (transmission control protocol) protocols. Therefore, player robot, a particular mechanics was we designed a special mechanics that provided designed and implemented that, together with a fast and flexible omnidirectional movement the motor's current feedbacks, to a good extent especially when looking for the ball and dribbling. Therefore, object finding and one castor wheel in the rear.