Neural Biclustering in Gene Expression Analysis

Clustering in high dimensional spaces is a very difficult task. Dealing with DNA microarrays is even more difficult because gene subsets are coregulated and coexpressed only under specific conditions. Biclusterng addresses the problem of finding such submanifolds by exploiting both gene and condition (tissue) clustering. The paper proposes a self-organizing neural network, GH EXIN, which builds a hierarchical tree by adapting its architecture to data. It is integrated in a framework in which gene and tissue clustering are alternated and controlled by the quality of the bicluster. Examples of the approach and a biological validation of results are also given

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

Gradient-Based Competitive Learning: Theory

Deep learning has been recently used to extract the relevant features for representing input data also in the unsupervised setting. However, state-of-the-art techniques focus mostly on algorithmic efficiency and accuracy rather than mimicking the input manifold. On the contrary, competitive learning is a powerful tool for replicating the input distribution topology. It is cognitive/biologically inspired as it is founded on Hebbian learning, a neuropsychological theory claiming that neurons can increase their specialization by competing for the right to respond to/represent a subset of the input data. This paper introduces a novel perspective by combining these two techniques: unsupervised gradient-based and competitive learning. The theory is based on the intuition that neural networks can learn topological structures by working directly on the transpose of the input matrix. At this purpose, the vanilla competitive layer and its dual are presented. The former is representative of a standard competitive layer for deep clustering, while the latter is trained on the transposed matrix. The equivalence of the layers is extensively proven both theoretically and experimentally. The dual competitive layer has better properties. Unlike the vanilla layer, it directly outputs the prototypes of the data inputs, while still allowing learning by backpropagation. More importantly, this paper proves theoretically that the dual layer is better suited for handling high-dimensional data (e.g., for biological applications), because the estimation of the weights is driven by a constraining subspace which does not depend on the input dimensionality, but only on the dataset cardinality. This paper has introduced a novel approach for unsupervised gradient-based competitive learning. This approach is very promising both in the case of small datasets of high-dimensional data and for better exploiting the advantages of a deep architecture: the dual layer perfectly integrates with the deep layers. A theoretical justification is also given by using the analysis of the gradient flow for both vanilla and dual layers

Shallow Neural Network for Biometrics from the ECG-WATCH

Applications such as surveillance, banking and healthcare deal with sensitive data whose confidentiality and integrity depends on accurate human recognition. In this sense, the crucial mechanism for performing an effective access control is authentication, which unequivocally yields user identity. In 2018, just in North America, around 445K identity thefts have been denounced. The most adopted strategy for automatic identity recognition uses a secret for encrypting and decrypting the authentication information. This approach works very well until the secret is kept safe. Electrocardiograms (ECGs) can be exploited for biometric purposes because both the physiological and geometrical differences in each human heart correspond to uniqueness in the ECG morphology. Compared with classical biometric techniques, e.g. fingerprints, ECG-based methods can definitely be considered a more reliable and safer way for user authentication due to ECG inherent robustness to circumvention, obfuscation and replay attacks. In this paper, the ECG WATCH, a non-expensive wristwatch for recording ECGs anytime, anywhere, in just 10 s, is proposed for user authentication. The ECG WATCH acquisitions have been used to train a shallow neural network, which has reached a 99% classification accuracy and 100% intruder recognition rate

Unsupervised Multi-Omic Data Fusion: the Neural Graph Learning Network

In recent years, due to the high availability of omic data, data-driven biology has greatly expanded. However, the analysis of different data sources is still an open challenge. A few multi-omics approaches have been proposed in the literature, none of which takes into consideration the intrinsic topology of each omic, though. In this work, an unsupervised learning method based on a deep neural network is proposed. Foreach omic, a separate network is trained, whose outputs are fused into a single graph; at this purpose, an innovative loss function has been designed to better represent the data cluster manifolds. The graph adjacency matrix is exploited to determine similarities among samples. With this approach, omics having a different number of features are merged into a unique representation. Quantitative and qualitative analyses show that the proposed method has comparable results to the state of the art. The method has great intrinsic flexibility as it can be customized according to the complexity of the tasks and it has a lot of room for future improvements compared to more fine-tuned methods, opening the way for future research

Topological Gradient-based Competitive Learning

Topological learning is a wide research area aiming at uncovering the mutual spatial relationships between the elements of a set. Some of the most common and oldest approaches involve the use of unsupervised competitive neural networks. However, these methods are not based on gradient optimization which has been proven to provide striking results in feature extraction also in unsupervised learning. Unfortunately, by focusing mostly on algorithmic efficiency and accuracy, deep clustering techniques are composed of overly complex feature extractors, while using trivial algorithms in their top layer. The aim of this work is to present a novel comprehensive theory aspiring at bridging competitive learning with gradient-based learning, thus allowing the use of extremely powerful deep neural networks for feature extraction and projection combined with the remarkable flexibility and expressiveness of competitive learning. In this paper we fully demonstrate the theoretical equivalence of two novel gradient-based competitive layers. Preliminary experiments show how the dual approach, trained on the transpose of the input matrix i.e. X T , lead to faster convergence rate and higher training accuracy both in low and high-dimensional scenarios

MiREx: mRNA levels prediction from gene sequence and miRNA target knowledge

Messenger RNA (mRNA) has an essential role in the protein production process. Predicting mRNA expression levels accurately is crucial for understanding gene regulation, and various models (statistical and neural network-based) have been developed for this purpose. A few models predict mRNA expression levels from the DNA sequence, exploiting the DNA sequence and gene features (e.g., number of exons/introns, gene length). Other models include information about long-range interaction molecules (i.e., enhancers/silencers) and transcriptional regulators as predictive features, such as transcription factors (TFs) and small RNAs (e.g., microRNAs - miRNAs). Recently, a convolutional neural network (CNN) model, called Xpresso, has been proposed for mRNA expression level prediction leveraging the promoter sequence and mRNAs’ half-life features (gene features). To push forward the mRNA level prediction, we present miREx, a CNN-based tool that includes information about miRNA targets and expression levels in the model. Indeed, each miRNA can target specific genes, and the model exploits this information to guide the learning process. In detail, not all miRNAs are included, only a selected subset with the highest impact on the model. MiREx has been evaluated on four cancer primary sites from the genomics data commons (GDC) database: lung, kidney, breast, and corpus uteri. Results show that mRNA level prediction benefits from selected miRNA targets and expression information. Future model developments could include other transcriptional regulators or be trained with proteomics data to infer protein levels

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