Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs’ targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.

Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multiclass classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other wellknown machine learning algorithms.

Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression.

Rye (Secale cereale) is an important diploid (2n = 14, RR) crop species of the Triticeae and a better understanding of its organellar genome variation can aid in its improvement. Previous genetic analyses of rye focused on the nuclear genome. In the present study, the objective was to investigate the organellar genome diversity and relationships of 96 accessions representing diverse geographic regions using chloroplast (cp) and mitochondrial (mt) DNA PCR-RFLPs. Seven cpDNA and 4 mtDNA coding and noncoding regions were amplified using universal cpDNA and mtDNA primer pairs. Each amplified fragment was digested with 13 different restriction enzymes. mtDNA analysis indicated that the number of polymorphic loci (20) was low and genetic differentiation (GST) was 0.60, excluding the outgroups (hexaploid wheat, Triticum aestivum, 2n = 6x = 42, AABBDD; triticale, xTriticosecale Wittmack, 2n = 6x = 42, AABBRR). cpDNA analysis revealed a low level of polymorphism (40%) among the accessions, and GST was 0.39. Of the 96 genotypes studied, 70 could not be differentiated using cpDNA PCR-RFLPs even though they are from different geographic regions. This is most likely due to germplasm exchange, indicating that genotypes might have a common genetic background. Two cpDNA and 3 mtDNA fragments were significantly correlated to the site of germplasm collection. However, there was no clear trend. These results indicate that the level of organellar polymorphism is low among the cultivated rye genotypes. The cpDNA and mtDNA PCR-RFLP markers used in the present study could be used as molecular markers in rye genetics and breeding programs.