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Background Precise prediction of cancer types is vital for cancer diagnosis and therapy. Important cancer marker genes can be inferred through predictive model. Several studies have attempted to build machine learning models for this task however none has taken into consideration the effects of tissue of origin that can potentially bias the identification of cancer markers. Results In this paper, we introduced several Convolutional Neural Network (CNN) models that take unstructured gene expression inputs to classify tumor and non-tumor samples into their designated cancer types or as normal. Based on different designs of gene embeddings and convolution schemes, we implemented three CNN models: 1D-CNN, 2D-Vanilla-CNN, and 2D-Hybrid-CNN. The models were trained and tested on combined 10,340 samples of 33 cancer types and 731 matched normal tissues of The Cancer Genome Atlas (TCGA). Our models achieved excellent prediction accuracies (93.9-95.0%) among 34 classes (33 cancers and normal). Furthermore, we interpreted one of the models, known as 1D-CNN model, with a guided saliency technique and identified a total of 2,090 cancer markers (108 per class). The concordance of differential expression of these markers between the cancer type they represent and others is confirmed. In breast cancer, for instance, our model identified well-known markers, such as GATA3 and ESR1. Finally, we extended the 1D-CNN model for prediction of breast cancer subtypes and achieved an average accuracy of 88.42% among 5 subtypes. The codes can be found at https://github.com/chenlabgccri/CancerTypePrediction., Comment: 34 pages, 5 figures, This paper was presented at ICIBM June, 2019 at Ohio Columbus, and will be published in BMC Genomics journal. Keywords: Deep Learning; Convolutional Neural Networks, The Cancer Genome Atlas; Cancer type prediction; Cancer gene markers; Breast cancer subtype prediction

2′-O-methylation (2′O) is one of the abundant post-transcriptional RNA modifications which can be found in all types of RNA. Detection and functional analysis of 2′O methylation have become challenging problems for biologists ever since its discovery. This paper addresses computational challenges for building Machine Learning and Deep Learning models for predicting 2′O sites. In particular, the impact of sequence length containing 2′O site, embedding method and the type of predictive model are each investigated separately. 30 different predictive models are built and each showed the impact of the mentioned parameters. The area under the precision-recall and receiving operating characteristics curves are utilized to test imbalanced case scenarios in the real world. By comparing the performance of these models, it is shown that embedding methods are crucial for Machine Learning models. However, they do not improve the performance of Deep Learning models. Furthermore, the best predictive model was further investigated to extract significant nucleotides surrounding 2′O sites. Interestingly, based on the significant score matrix achieved by all 2′O samples, it is depicted that model pays the highest attention at the location that the dominant 2′O motifs exist. Dataset and all of the codes are available at https://github.com/MMostavi/2_O_Me_sitePred

Epitranscriptome is an exciting area that studies different types of modifications in transcripts and the prediction of such modification sites from the transcript sequence is of significant interest. However, the scarcity of positive sites for most modifications imposes critical challenges for training robust algorithms. To circumvent this problem, we propose MR-GAN, a generative adversarial network (GAN) based model, which is trained in an unsupervised fashion on the entire pre-mRNA sequences to learn a low dimensional embedding of transcriptomic sequences. MR-GAN was then applied to extract embeddings of the sequences in a training dataset we created for eight epitranscriptome modifications, including m6A, m1A, m1G, m2G, m5C, m5U, 2′-O-Me, Pseudouridine (Ψ) and Dihydrouridine (D), of which the positive samples are very limited. Prediction models were trained based on the embeddings extracted by MR-GAN. We compared the prediction performance with the one-hot encoding of the training sequences and SRAMP, a state-of-the-art m6A site prediction algorithm and demonstrated that the learned embeddings outperform one-hot encoding by a significant margin for up to 15% improvement. Using MR-GAN, we also investigated the sequence motifs for each modification type and uncovered known motifs as well as new motifs not possible with sequences directly. The results demonstrated that transcriptome features extracted using unsupervised learning could lead to high precision for predicting multiple types of epitranscriptome modifications, even when the data size is small and extremely imbalanced.

The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor’s genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.

2'-O-methylation (2'-O-me) of ribose moiety is one of the significant and ubiquitous post-transcriptional RNA modifications which is vital for metabolism and functions of RNA. Although recent development of new technology (Nmseq) enabled biologists to find precise location of 2'-O-me in RNA sequences, there is still a lack of computational tools that can also provide high resolution prediction of this RNA modification. In this paper, we propose a deep learning based method that takes advantage of an embedding method to learn complex feature representation of pre-mRNA sequences and employs a Convolutional Neural Network to fine-tune the features required for accurate prediction of such alteration. Specifically, we adopted dna2vec, a biological sequence embedding method originally inspired by the word2vec model of text analysis, to yield embedded representation of sequences that may or may not contain 2-O-me sites before feeding those features into CNN for classification. Our model was trained using the data collected from Nm-seq experiment. The proposed method achieved AUC and auPRC scores of 90% outperforming existing state-of-the-art algorithms by a significant margin in both balanced and unbalanced class testing scenarios.

One of the challenges that practitioners are faced is how to administer drugs to get the maximum efficiency to suppress the AIDS virus and has the least side effects which resulted from using drugs in AIDS patients. The ultimate goal of this paper is to prime the optimal drug protocols with a controlling technique in a way that consider the aforementioned issues. We exerted Nonlinear Model predictive Control to optimize a nonlinear ODE with either zero or one inputs as candidates for the available drugs which practitioners prescribe for infected individuals. The results of simulations show this method is very effective for our final purpose.