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Differentiation is ubiquitous in the field of mathematics and especially in the field of Machine learning for calculations in gradient-based models. Calculating gradients might be complex and require handling multiple variables. Supervised Learning Vector Quantization models, which are used for classification tasks, also use the Stochastic Gradient Descent method for optimizing their cost functions. There are various methods to calculate these gradients or derivatives, namely Manual Differentiation, Numeric Differentiation, Symbolic Differentiation, and Automatic Differentiation. In this thesis, we evaluate each of the methods mentioned earlier for calculating derivatives and also compare the use of these methods for the variants of Generalized Learning Vector Quantization algorithms.
A relatively new research field of neurosciences, called Connectomics, aims to achieve a full understanding and mapping of neural circuits and fine neuronal structures of the nervous system in a variety of organisms. This detailed information will provide insight in how our brain is influenced by different genetic and psychiatric diseases, how memory traces are stored and ageing influences our brain structure. It is beyond question that new methods for data acquisition will produce large amounts of neuronal image data. This data will exceed the zetabyte range and is impossible to annotate manually for visualization and analysis. Nowadays, machine learning algorithms and specially deep convolutional neuronal networks are heavily used in medical imaging and computer vision, which brings the opportunity of designing fully automated pipelines for image analysis. This work presents a new automated workflow based on three major parts including image processing using consecutive deep convolutional networks, a pixel-grouping step called connected components and 3D visualization via neuroglancer to achieve a dense three dimensional reconstruction of neurons from EM image data.
Sequences are an important data structure in molecular biology, but unfortunately it is difficult for most machine learning algorithms to handle them, as they rely on vectorial data. Recent approaches include methods that rely on proximity data, such as median and relational Learning Vector Quantization. However, many of them are limited in the size of the data they are able to handle. A standard method to generate vectorial features for sequence data does not exist yet. Consequently, a way to make sequence data accessible to preferably interpretable machine learning algorithms needs to be found. This thesis will therefore investigate a new approach called the Sensor Response Principle, which is being adapted to protein sequences. Accordingly, sequence similarity is measured via pairwise sequence alignments with different sequence alignment algorithms and various substitution matrices. The measurements are then used as input for learning with the Generalized Learning Vector Quantization algorithm. A special focus lies on sequence length variability as it is suspected to affect the sequence alignment score and therefore the discriminative quality of the generated feature vectors. Specific datasets were generated from the Pfam protein family database to address this question. Further, the impact of the number of references and choice of substitution matrices is examined.
Prototype-based Vector Quantization is one of the key methods in data processing like data compression or interpretable classification learning. Prototype vectors serve as references for data and data classes. The data are given as vectors representing objects by numerical features. Famous approaches are the Neural Gas Vector Quantizer (NGVQ) for data compression and Learning Vector Quantizers (LVQ) for classification tasks. Frequently, training of those models is time consuming. In the contribution we discuss modifications of these algorithms adopting ideas from quantum computing. The aim for this is a least twofold: First quantum computing provides ideas for enormous speedup making use of quantum mechanical systems and inherent parallelization.
Second, considering data and prototype vectors in terms of quantum systems, implicit data processing is performed, which frequently results in better data separation. We will highlight respective ideas and difficulties when equipping vector quantizers with quantum computing features.
We present dimensionality reduction methods like autoencoders and t-SNE for visualization of high-dimensional data into a two-dimensional map. In this thesis, we initially implement basic and deep autoencoders using breast cancer and mushroom datasets. Next, we build another dimensionality reduction method t-SNE using the same datasets. The obtained visualization results of the datasets using the dimensionality reduction methods are documented in the experiments section of the thesis. The evaluation of classification and clustering for the dimensionality reduction techniques is also performed. The visualization and evaluation results of t-SNE are significantly better than the other dimensionality reduction techniques.
There are multiple ways to gain information about an individual and its health status, but an increasingly popular field in medicine has become the analysis of human breath, which carries a lot of information about metabolic processes within the individuals body. The information in exhaled breath consists of volatile (organic) compounds (VOCs). These VOCs are products of metabolic processes within the individuals body, thus might be an indicator for diseases disturbing those processes. The compounds are to be detected by mass-spectrometric (MS) or ion-mobility spectrometric (IMS) techniques, making the analysis of these compounds not only bounded to exhaled breath. The resulting data is spectral data, capturing concentrations of the VOCs indirectly through intensities. However, a number of about 3000 VOCs [1] could already be determined in human exhaled breath. The number of research paper about VOC-analysis and detection had risen nearly constantly over the last decade 1. Furthermore, the technique to identify VOCs could also be used to capture biomarker from alien species within the individuals body. Extracting VOCs from an individual can be done by non- or minimal invasive techniques. However, the manual identification of VOCs and biomarkers related to a certain disease or infection is not feasible due to the complexity of the sample and often unknown metabolic products, thus automized techniques are needed. [1–4] To establish breath analysis as a diagnosis tool, machine learning methodes could be used. Machine learning has become a popular and common technique when dealing with medical data, due to the rapid analysis. Taking this advantage, breath analysis using machine learning could become the model of choice for diagnosis, keeping in mind that conventional methodes are laboratory based and thus when trying detect bacterial infection need sometimes several days to identify the organism. [5]
In this paper, we conduct experiments to optimize the learning rates for the Generalized Learning Vector Quantization (GLVQ) model. Our approach leverages insights from cog- nitive science rooted in the profound intricacies of human thinking. Recognizing that human-like thinking has propelled humankind to its current state, we explore the applica- bility of cognitive science principles in enhancing machine learning. Prior research has demonstrated promising results when applying learning rate methods inspired by cognitive science to Learning Vector Quantization (LVQ) models. In this study, we extend this approach to GLVQ models. Specifically, we examine five distinct cognitive science-inspired GLVQ variants: Conditional Probability (CP), Dual Factor Heuristic (DFH), Middle Symmetry (MS), Loose Symmetry (LS), and Loose Symme- try with Rarity (LSR). Our experiments involve a comprehensive analysis of the performance of these cogni- tive science-derived learning rate techniques across various datasets, aiming to identify optimal settings and variants of cognitive science GLVQ model training. Through this research, we seek to unlock new avenues for enhancing the learning process in machine learning models by drawing inspiration from the rich complexities of human cognition. Keywords: machine learning, GLVQ, cognitive science, cognitive bias, learning rate op- timization, optimizers, human-like learning, Conditional Probability (CP), Dual Factor Heuristic (DFH), Middle Symmetry (MS), Loose Symmetry (LS), Loose Symmetry with Rarity (LSR).
Drought is one of the most common and dangerous threats plants have to face, costing the global agricultural sector billions of dollars every year and leading to the loss of tons of harvest. Until people drastically reduce their consumption of animal products or cellular agriculture comes of age, more and more crops will need to be produced to sustain the ever growing human population. Even then, as more areas on earth are becoming prone to drought due to climate change, we may still have to find or breed plant varieties more suitable to grow and prosper in these changing environments.
Plants respond to drought stress with a complex interplay of hormones, transcription factors, and many other functional or regulatory proteins and mapping out this web of agents is no trivial task. In the last two to three decades or so, machine learning has become immensely popular and is increasingly used to find patterns in situations that are too complex for the human mind to overlook. Even though much of the hype is focused on the latest developments in deep learning, relatively simple methods often yield superior results, especially when data is limited and expensive to gather.
This Master Thesis, conducted at the IPK in Gatersleben, develops an approach for shedding light on the phenotypic and transcriptomic processes that occur when a plant is subjected to stress. It centers around a random forest feature selection algorithm and although it is used here to illuminate drought stress response in Arabidopsis thaliana, it can be applied to all kinds of stresses in all kinds of plants.
Genetic sequence variations at the level of gene promoters influence the binding of transcription factors. In plants, this often leads to differential gene expression across natural accessions and crop cultivars. Some of these differences are propagated through molecular networks and lead to macroscopic phenotypes. However, the link between promoter sequence variation and the variation of its activity is not yet well understood. In this project, we use the power of deep learning in 728 genotypes of Arabidopsis thaliana to shed light on some aspects of that link. Convolutional neural networks were successfully implemented to predict the likelihood of a gene being expressed from its promoter sequence. These networks were also capable of highlighting known and putative new sequence motifs causal for the expression of genes. We tested our algorithms in various scenarios, including single and multiple point mutations, as well as indels on synthetic and real promoter sequences and the respective performance characteristics of the algorithm have been estimated. Finally, we showed that the decision boundary to classify genes as expressed and non-expressed depends on the sensitivity of the transcriptome profiling assay and changing it has an impact on the algorithm’s performance.
Recently a deep neural network architecture designed to work on graph- structured data have been capturing notice as well as getting implemented in various domains and application. However, learning representation (feature embedding) from graphical data picking pace in research and constructing graph(s) from dataset remains a challenge. The ability to map the data to lower dimensions further makes the task easier while providing comfort in applying many operations. Graph neural network (GNN) is one of the novel neural network models that is catching attention as it is outperforming in various applications like recommender systems, social networks, chemical synthesis, and many more. This thesis discusses a unique approach for a fundamental task on graphs; node classification. The feature embedding for a node is aggregated by applying a Recurrent neural network (RNN), then a GNN model is trained to classify a node with the help of aggregated features and Q learning supports in optimizing the shape of neural networks. This thesis starts with the working principles of the Feedforward neural network, recurrent units like simple RNN, Long short-term memory (LSTM), and Gated recurrent unit (GRU), followed by concepts of Reinforcement learning (RL) and the Q learning algorithm. An overview of the fundamentals of graphs, followed by the GNN architecture and workflow, is discussed subsequently. Some basic GNN models are discussed in brief later before it approaches the technical implementation details, the output of the model, and a comparison with a few other models such as GraphSage and Graph attention network (GAN).