Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series

Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were LCE2B, KNG1, IGHV7_81, TG, C6, FGB, ZNF750, CTSV, INGX, and COL4A6 for the whole set; and ARG1, MAGEA3, AKT2, IL1B, S100A7A, CLEC5A, WIF1, TREM1, DEFB1, and GAGE1 for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, n = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series

Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification

Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct disease-specific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown a great potential to address the difficult situation like this. However, deep learning technologies conventionally use grid-like structured data, thus application of deep learning technologies to the classification of human disease subtypes is yet to be explored. Recently, graph based deep learning techniques have emerged, which becomes an opportunity to leverage analyses in network biology. In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN). We utilize graph CNN as a component to learn expression patterns of cooperative gene community, and RN as a component to learn associations between learned patterns. The proposed model is applied to the PAM50 breast cancer subtype classification task, the standard breast cancer subtype classification of clinical utility. In experiments of both subtype classification and patient survival analysis, our proposed method achieved significantly better performances than existing methods. We believe that this work is an important starting point to realize the upcoming personalized medicine.Comment: 8 pages, To be published in proceeding of IJCAI 201

Combining handcrafted features with latent variables in machine learning for prediction of radiationâ induced lung damage

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149351/1/mp13497.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149351/2/mp13497_am.pd

Incorporating Deep Learning Techniques into Outcome Modeling in Non-Small Cell Lung Cancer Patients after Radiation Therapy

Radiation therapy (radiotherapy) together with surgery, chemotherapy, and immunotherapy are common modalities in cancer treatment. In radiotherapy, patients are given high doses of ionizing radiation which is aimed at killing cancer cells and shrinking tumors. Conventional radiotherapy usually gives a standard prescription to all the patients, however, as patients are likely to have heterogeneous responses to the treatment due to multiple prognostic factors, personalization of radiotherapy treatment is desirable. Outcome models can serve as clinical decision-making support tools in the personalized treatment, helping evaluate patients’ treatment options before the treatment or during fractionated treatment. It can further provide insights into designing of new clinical protocols. In the outcome modeling, two indices including tumor control probability (TCP) and normal tissue complication probability (NTCP) are usually investigated. Current outcome models, e.g., analytical models and data-driven models, either fail to take into account complex interactions between physical and biological variables or require complicated feature selection procedures. Therefore, in our studies, deep learning (DL) techniques are incorporated into outcome modeling for prediction of local control (LC), which is TCP in our case, and radiation pneumonitis (RP), which is NTCP in our case, in non-small-cell lung cancer (NSCLC) patients after radiotherapy. These techniques can improve the prediction performance of outcomes and simplify model development procedures. Additionally, longitudinal data association, actuarial prediction, and multi-endpoints prediction are considered in our models. These were carried out in 3 consecutive studies. In the first study, a composite architecture consisting of variational auto-encoder (VAE) and multi-layer perceptron (MLP) was investigated and applied to RP prediction. The architecture enabled the simultaneous dimensionality reduction and prediction. The novel VAE-MLP joint architecture with area under receiver operative characteristics (ROC) curve (AUC) [95% CIs] 0.781 [0.737-0.808] outperformed a strategy which involves separate VAEs and classifiers (AUC 0.624 [ 0.577-0.658]). In the second study, composite architectures consisted of 1D convolutional layer/ locally-connected layer and MLP that took into account longitudinal associations were applied to predict LC. Composite architectures convolutional neural network (CNN)-MLP that can model both longitudinal and non-longitudinal data yielded an AUC 0.832 [ 0.807-0.841]. While plain MLP only yielded an AUC 0.785 [CI: 0.752-0.792] in LC control prediction. In the third study, rather than binary classification, time-to-event information was also incorporated for actuarial prediction. DL architectures ADNN-DVH which consider dosimetric information, ADNN-com which further combined biological and imaging data, and ADNN-com-joint which realized multi-endpoints prediction were investigated. Analytical models were also conducted for comparison purposes. Among all the models, ADNN-com-joint performed the best, yielding c-indexes of 0.705 [0.676-0.734] for RP2, 0.740 [0.714-0.765] for LC and an AU-FROC 0.720 [0.671-0.801] for joint prediction. The performance of proposed models was also tested on a cohort of newly-treated patients and multi-institutional RTOG0617 datasets. These studies taken together indicate that DL techniques can be utilized to improve the performance of outcome models and potentially provide guidance to physicians during decision making. Specifically, a VAE-MLP joint architectures can realize simultaneous dimensionality reduction and prediction, boosting the performance of conventional outcome models. A 1D CNN-MLP joint architecture can utilize temporal-associated variables generated during the span of radiotherapy. A DL model ADNN-com-joint can realize multi-endpoint prediction, which allows considering competing risk factors. All of those contribute to a step toward enabling outcome models as real clinical decision support tools.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162923/1/sunan_1.pd

Algorithms for complex systems in the life sciences: AI for gene fusion prioritization and multi-omics data integration

Due to the continuous increase in the number and complexity of the genomics and biological data, new computer science techniques are needed to analyse these data and provide valuable insights into the main features. The thesis research topic consists of designing and developing bioinformatics methods for complex systems in life sciences to provide informative models about biological processes. The thesis is divided into two main sub-topics. The first sub-topic concerns machine and deep learning techniques applied to the analysis of aberrant genetic sequences like, for instance, gene fusions. The second one is the development of statistics and deep learning techniques for heterogeneous biological and clinical data integration. Referring to the first sub-topic, a gene fusion is a biological event in which two distinct regions in the DNA create a new fused gene. Gene fusions are a relevant issue in medicine because many gene fusions are involved in cancer, and some of them can even be used as cancer predictors. However, not all of them are necessarily oncogenic. The first part of this thesis is devoted to the automated recognition of oncogenic gene fusions, a very open and challenging problem in cancer development analysis. In this context, an automated model for the recognition of oncogenic gene fusions relying exclusively on the amino acid sequence of the resulting proteins has been developed. The main contributions consist of: 1. creation of a proper database used to train and test the model; 2. development of the methodology through the design and the implementation of a predictive model based on a Convolutional Neural Network (CNN) followed by a bidirectional Long Short Term Memory (LSTM) network; 3. extensive comparative analysis with other reference tools in the literature; 4. engineering of the developed method through the implementation and release of an automated tool for gene fusions prioritization downstream of gene fusion detection tools. Since the previous approach does not consider post-transcriptional regulation effects, new biological features have been considered (e.g., micro RNA data, gene ontologies, and transcription factors) to improve the overall performance, and a new integrated approach based on MLP has explicitly been designed. In the end, extensive comparisons with other methods present in the literature have been made. These contributions led to an improved model that outperforms the previous ones, and it competes with state-of-the-art tools. The rationale behind the second sub-topic of this thesis is the following: due to the widespread of Next Generation Sequencing (NGS) technologies, a large amount of heterogeneous complex data related to several diseases and healthy individuals is now available (e.g., RNA-seq, gene expression data, miRNAs expression data, methylation sequencing data, and many others). Each one of these data is also called omic, and their integrative study is called multi-omics. In this context, the aim is to integrate multi-omics data involving thousands of features (genes, microRNA) and identifying which of them are relevant for a specific biological process. From a computational point of view, finding the best strategies for multi-omics analysis and relevant features identification is a very open challenge. The first chapter dedicated to this second sub-topic focuses on the integrative analysis of gene expression and connectivity data of mouse brains exploiting machine learning techniques. The rational behind this study is the exploration of the capability to evaluate the grade of physical connection between brain regions starting from their gene expression data. Many studies have been performed considering the functional connection of two or more brain areas (which areas are activated in response to a specific stimulus). While, analyzing physical connections (i.e., axon bundles) starting from gene expression data is still an open problem. Despite this study is scientifically very relevant to deepen human brain functioning, ethical reasons strongly limit the availability of samples. For this reason, several studies have been carried out on the mouse brain, anatomically similar to the human one. The neuronal connection data (obtained by viral tracers) of mouse brains were processed to identify brain regions physically connected and then evaluated with these areas’ gene expression data. A multi-layer perceptron was applied to perform the classification task between connected and unconnected regions providing gene expression data as input. Furthermore, a second model was created to infer the degree of connection between distinct brain regions. The implemented models successfully executed the binary classification task (connected regions against unconnected regions) and distinguished the intensity of the connection in low, medium, and high. A second chapter describes a statistical method to reveal pathology-determining microRNA targets in multi-omic datasets. In this work, two multi-omics datasets are used: breast cancer and medulloblastoma datasets. Both the datasets are composed of miRNA, mRNA, and proteomics data related to the same patients. The main computational contribution to the field consists of designing and implementing an algorithm based on the statistical conditional probability to infer the impact of miRNA post-transcriptional regulation on target genes exploiting the protein expression values. The developed methodology allowed a more in-depth understanding and identification of target genes. Also, it proved to be significantly enriched in three well-known databases (miRDB, TargetScan, and miRTarBase), leading to relevant biological insights. Another chapter deals with the classification of multi-omics samples. The literature’s main approaches integrate all the features available for each sample upstream of the classifier (early integration approach) or create separate classifiers for each omic and subsequently define a consensus set rules (late integration approach). In this context, the main contribution consists of introducing the probability concept by creating a model based on Bayesian and MLP networks to achieve a consensus guided by the class label and its probability. This approach has shown how a probabilistic late integration classification is more specific than an early integration approach and can identify samples out of the training domain. To provide new molecular profiles and patients’ categorization, class labels could be helpful. However, they are not always available. Therefore, the need to cluster samples based on their intrinsic characteristics is revealed and dealt with in a specific chapter. Multi-omic clustering in literature is mainly addressed by creating graphs or methods based on multidimensional data reduction. This field’s main contribution is creating a model based on deep learning techniques by implementing an MLP with a specifically designed loss function. The loss represents the input samples in a reduced dimensional space by calculating the intra-cluster and inter-cluster distance at each epoch. This approach reported performances comparable to those of most referred methods in the literature, avoiding pre-processing steps for either feature selection or dimensionality reduction. Moreover, it has no limitations on the number of omics to integrate

A reliable neural network-based decision support system for breast cancer prediction

PhD ThesisAxillary lymph node (ALN) metastasis status is an important prognostic marker in breast cancer and is widely employed for tumour staging and defining an adjuvant therapy. In an attempt to avoid invasive procedures which are currently employed for the diagnosis of nodal metastasis, several markers have been identified and tested for the prediction of ALN metastasis status in recent years. However, the nonlinear and complex relationship between these markers and nodal status has inhibited the effectiveness of conventional statistical methods as classification tools for diagnosing metastasis to ALNs. The aim of this study is to propose a reliable artificial neural network (ANN) based decision support system for ALN metastasis status prediction. ANNs have been chosen in this study for their special characteristics including nonlinear modelling, robustness to inter-class variability and having adaptable weights which makes them suitable for data driven analysis without making any prior assumptions about the underlying data distributions. To achieve this aim, the probabilistic neural network (PNN) evaluated with the .632 bootstrap is investigated and proposed as an effective and reliable tool for prediction of ALN metastasis. For this purpose, results are compared with the multilayer perceptron (MLP) neural network and two network evaluation methods: holdout and cross validation (CV). A set of six markers have been identified and analysed in detail for this purpose. These markers include tumour size, oestrogen receptor (ER), progesterone receptor (PR), p53, Ki-67 and age. The outcome of each patient is defined as metastasis or non-metastasis, diagnosed by surgery. This study makes three contributions: firstly it suggests the application of the PNN as a classifier for predicting the ALN metastasis, secondly it proposes a the .632 bootstrap evaluation of the ANN outcome, as a reliable tool for the purpose of ALN status prediction, and thirdly it proposes a novel set of markers for accurately predicting the state of nodal metastasis in breast cancer. Results reveal that PNN provides better sensitivity, specificity and accuracy in most marker combinations compared to MLP. The results of evaluation methods’ comparison demonstrate the high variability and the existence of outliers when using the holdout and 5-fold CV methods. This variability is reduced when using the .632 bootstrap. The best prediction accuracy, obtained by combining ER, p53, Ki-67 and age was 69% while tumour size and p53 were the most significant individual markers. The classification accuracy of this panel of markers emphasises their potential for predicting nodal spread in individual patients. This approach could significantly reduce the need for invasive procedures, and reduce post-operative stress and morbidity. Moreover, it can reduce the time lag between investigation and decision making in patient management.ORS Award Schem

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms

The importance of Predictive Maintenance is critical for engineering industries, such as manufacturing, aerospace and energy. Unexpected failures cause unpredictable downtime, which can be disruptive and high costs due to reduced productivity. This forces industries to ensure the reliability of their equip-ment. In order to increase the reliability of equipment, maintenance actions, such as repairs, replacements, equipment updates, and corrective actions are employed. These actions affect the flexibility, quality of operation and manu-facturing time. It is therefore essential to plan maintenance before failure occurs.Traditional maintenance techniques rely on checks conducted routinely based on running hours of the machine. The drawback of this approach is that maintenance is sometimes performed before it is required. Therefore, conducting maintenance based on the actual condition of the equipment is the optimal solu-tion. This requires collecting real-time data on the condition of the equipment, using sensors (to detect events and send information to computer processor).Predictive Maintenance uses these types of techniques or analytics to inform about the current, and future state of the equipment. In the last decade, with the introduction of the Internet of Things (IoT), Machine Learning (ML), cloud computing and Big Data Analytics, manufacturing industry has moved forward towards implementing Predictive Maintenance, resulting in increased uptime and quality control, optimisation of maintenance routes, improved worker safety and greater productivity.The present thesis describes a novel computational strategy of Predictive Maintenance (fault diagnosis and fault prognosis) with ML and Deep Learning applications for an FG304 series external gear pump, also known as a domino pump. In the absence of a comprehensive set of experimental data, synthetic data generation techniques are implemented for Predictive Maintenance by perturbing the frequency content of time series generated using High-Fidelity computational techniques. In addition, various types of feature extraction methods considered to extract most discriminatory informations from the data. For fault diagnosis, three types of ML classification algorithms are employed, namely Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB) algorithms. For prognosis, ML regression algorithms, such as MLP and SVM, are utilised. Although significant work has been reported by previous authors, it remains difficult to optimise the choice of hyper-parameters (important parameters whose value is used to control the learning process) for each specific ML algorithm. For instance, the type of SVM kernel function or the selection of the MLP activation function and the optimum number of hidden layers (and neurons).It is widely understood that the reliability of ML algorithms is strongly depen-dent upon the existence of a sufficiently large quantity of high-quality training data. In the present thesis, due to the unavailability of experimental data, a novel high-fidelity in-silico dataset is generated via a Computational Fluid Dynamic (CFD) model, which has been used for the training of the underlying ML metamodel. In addition, a large number of scenarios are recreated, ranging from healthy to faulty ones (e.g. clogging, radial gap variations, axial gap variations, viscosity variations, speed variations). Furthermore, the high-fidelity dataset is re-enacted by using degradation functions to predict the remaining useful life (fault prognosis) of an external gear pump.The thesis explores and compares the performance of MLP, SVM and NB algo-rithms for fault diagnosis and MLP and SVM for fault prognosis. In order to enable fast training and reliable testing of the MLP algorithm, some predefined network architectures, like 2n neurons per hidden layer, are used to speed up the identification of the precise number of neurons (shown to be useful when the sample data set is sufficiently large). Finally, a series of benchmark tests are presented, enabling to conclude that for fault diagnosis, the use of wavelet features and a MLP algorithm can provide the best accuracy, and the MLP al-gorithm provides the best prediction results for fault prognosis. In addition, benchmark examples are simulated to demonstrate the mesh convergence for the CFD model whereas, quantification analysis and noise influence on training data are performed for ML algorithms