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Drug discovery is time- and resource-consuming. To this end, computational approaches that are applied in de novo drug design play an important role to improve the efficiency and decrease costs to develop novel drugs. Over several decades, a variety of methods have been proposed and applied in practice. Traditionally, drug design problems are always taken as combinational optimization in discrete chemical space. Hence optimization methods were exploited to search for new drug molecules to meet multiple objectives. With the accumulation of data and the development of machine learning methods, computational drug design methods have gradually shifted to a new paradigm. There has been particular interest in the potential application of deep learning methods to drug design. In this chapter, we will give a brief description of these two different de novo methods, compare their application scopes and discuss their possible development in the future.Combining artificial neural networks with evolutive/bioinspired approaches is a technique that can solve a variety of issues regarding the topology determination and training for neural networks or for process optimization. In this chapter, the main mechanisms used in neuroevolution are discussed and some biochemical separation examples are given to underline the efficiency of these tools. For the current case studies (reactive extraction of folic acid and pertraction of vitamin C), the bioinspired metaheuristic included in the neuroevolutive procedures is represented by Differential Evolution, an algorithm that has shown a great potential to solve a variety of problems from multiple domains.Research over the past two decades has uncovered an unexpected complexity and intricacy of gene expression regulation in bacteria. Bacteria have (1) numerous small noncoding RNAs (sRNAs) which are ubiquitous regulators of gene expression, (2) a flexible and diverse promoter structure, and (3) transcription termination as another means of gene expression regulation.To understand bacteria gene expression regulation, one needs to identify promoters, terminators, and sRNAs together with their targets. Here we describe the state of the art in computational methods to perform promoter recognition, sRNA identification, and sRNA target prediction. Additionally, we provide step-by-step instructions to use current approaches to perform these tasks.Similarity has always been a key aspect in computer science and statistics. Any time two element vectors are compared, many different similarity approaches can be used, depending on the final goal of the comparison (Euclidean distance, Pearson correlation coefficient, Spearman’s rank correlation coefficient, and others). But if the comparison has to be applied to more complex data samples, with features having different dimensionality and types which might need compression before processing, these measures would be unsuitable. In these cases, a siamese neural network may be the best choice it consists of two identical artificial neural networks each capable of learning the hidden representation of an input vector. The two neural networks are both feedforward perceptrons, and employ error back-propagation during training; they work parallelly in tandem and compare their outputs at the end, usually through a cosine distance. The output generated by a siamese neural network execution can be considered the semantic similarity between the projected representation of the two input vectors. In this overview we first describe the siamese neural network architecture, and then we outline its main applications in a number of computational fields since its appearance in 1994. Additionally, we list the programming languages, software packages, tutorials, and guides that can be practically used by readers to implement this powerful machine learning model.With the biomedical field generating large quantities of time series data, there has been a growing interest in developing and refining machine learning methods that allow its mining and exploitation. Classification is one of the most important and challenging machine learning tasks related to time series. Many biomedical phenomena, such as the brain’s activity or blood pressure, change over time. The objective of this chapter is to provide a gentle introduction to time series classification. In the first part we describe the characteristics of time series data and challenges in its analysis. The second part provides an overview of common machine learning methods used for time series classification. A real-world use case, the early recognition of sepsis, demonstrates the applicability of the methods discussed.Mutations in protein-coding regions can lead to large biological changes and are associated with genetic conditions, including cancers and Mendelian diseases, as well as drug resistance. Although whole genome and exome sequencing help to elucidate potential genotype-phenotype correlations, there is a large gap between the identification of new variants and deciphering their molecular consequences. A comprehensive understanding of these mechanistic consequences is crucial to better understand and treat diseases in a more personalized and effective way. This is particularly relevant considering estimates that over 80% of mutations associated with a disease are incorrectly assumed to be causative. Wnt inhibitor A thorough analysis of potential effects of mutations is required to correctly identify the molecular mechanisms of disease and enable the distinction between disease-causing and non-disease-causing variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype-phenotype correlation methods by using the wealth of protein structural information.

Overtraining syndrome, caused by prolonged excessive stress, results in reduced performance and cortisol responsiveness in athletes. It is necessary to collect saliva samples sequentially within circadian rhythm for assessing exercise stress by measuring cortisol concentrations, and automated cortisol measurements using electrochemiluminescence immunoassay (ECLIA) may be useful for measuring a large number of saliva samples. In this study, we evaluated the appropriate use of cortisol-based exercise stress assessment within the circadian rhythm, which may diagnose and prevent overtraining syndrome in athletes.

We collected saliva and sera from 54 healthy participants and analyzed the correlation between salivary cortisol concentrations measured by ECLIA and enzyme-linked immunosorbent assay (ELISA) or serum cortisol analysis. We also collected saliva continuously from 12 female long-distance runners on 2 consecutive days involving different intensities and types of exercise early in the morning and in the afternoon and measured salivary cortisol concentrations using ECLIA.