With the rapid development of next generation sequencing technology, the amount of biological sequence data of the cancer genome increases exponentially, which calls for efficient and effective algorithms that may identify patterns hidden underneath the raw data that may distinguish cancer Achilles' heels. From a signal processing point of view, biological units of information, including DNA and protein sequences, have been viewed as one-dimensional signals. Therefore, researchers have been applying signal processing techniques to mine the potentially significant patterns within these sequences. More specifically, in recent years, wavelet transforms have become an important mathematical analysis tool, with a wide and ever increasing range of applications. The versatility of wavelet analytic techniques has forged new interdisciplinary bounds by offering common solutions to apparently diverse problems and providing a new unifying perspective on problems of cancer genome research. In this paper, we provide a survey of how wavelet analysis has been applied to cancer bioinformatics questions. Specifically, we discuss several approaches of representing the biological sequence data numerically and methods of using wavelet analysis on the numerical sequences.
Driver mutations propel oncogenesis and occur much less frequently than passenger mutations. The need for automatic and accurate identification of driver mutations has increased dramatically with the exponential growth of mutation data. Current computational solutions to identify driver mutations rely on sequence homology. Here we construct a machine learning-based framework that does not rely on sequence homology or domain knowledge to predict driver missense mutations. A windowing approach to represent the local environment of the sequence around the mutation point as a mutation sample is applied, followed by extraction of three sequence-level features from each sample. After selecting the most significant features, the support vector machine and multimodal fusion strategies are employed to give final predictions. The proposed framework achieves relatively high performance and outperforms current state-of-the-art algorithms. The ease of deploying the proposed framework and the relatively accurate performance make this solution applicable to large-scale mutation data analyses.
: The microtubule skeleton plays an essential role in nerve cells as the most important structural determinant of morphology and as a highway for axonal transport processes. Many neurodegenerative diseases are characterized by changes in the structure and organization of microtubules and microtubule-regulating proteins such as the microtubule-associated protein tau, which exhibits characteristic changes in a whole class of diseases collectively referred to as tauopathies. Changes in the dynamics of microtubules appear to occur early under neurodegenerative conditions and are also likely to contribute to age-related dysfunction of neurons. Thus, modulating microtubule dynamics and correcting impaired microtubule stability can be a useful neuroprotective strategy to counteract disruption of the microtubule system in disease and aging. In this article, we review current microtubule-directed approaches for the treatment of neurodegenerative diseases with microtubules as drug target, tau as drug target, and posttranslational modifications as potential modifiers of the microtubule system. We discuss limitations of the approaches that can be traced back to the rather unspecific mechanism of action, which causes undesirable side effects on non-neuronal cell types or which are due to the disruption of non-microtubule-related interactions. We also develop some thoughts on how the specificity of the approaches can be improved and what further targets could be used for modulating substances.
Plant diseases are a major cause of degraded fruit quality and yield losses. These losses can be significantly reduced with early detection of diseases to ensure their timely treatment, particularly in developing countries. In this regard, an expert system based on deep learning model where the expert knowledge, particularly the one acquired by plant pathologist, is recursively learned by the system and is applied using a smart phone application for use in the target field environment, is being proposed. In this paper, a robust disease detection method is developed based on convolutional neural network (CNN), where its powerful features extraction capabilities are leveraged to detect diseases in images of fruits and leaves. The features extraction pipelines of several state-of-the-art pretrained networks are fine-tuned to achieve optimal detection performance. A novel dataset is collected from peach orchards and extensively augmented using both label-preserving and non-label-preserving transformations. The augmented dataset is used to study the effects of finetuning the pretrained networks' feature extraction pipeline as opposed to keeping the network parameters unchanged. The CNN models, particularly EfficientNet exhibited superior performance on the target dataset once their feature extraction pipelines are fine-tuned. The optimal model is able to achieve 96.6% average accuracy, 90% sensitivity and precision, and 98% specificity on the test set of images.
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