Birds exhibit a variety of flight styles, primarily classified as flapping, which is characterized by rapid up-and-down wing movements, and soaring, which involves gliding with wings outstretched. Each species usually performs specific flight styles, and this has been argued in terms of morphological and physiological adaptation. However, it remains a challenge to evaluate the contribution of each factor to the difference in flight styles. In this study, using phenotypic data from 635 migratory bird species, such as body mass, wing length, and breeding periods, we quantified the relative importance of each feature using Feature Importance and SHAP values, and used them to construct weighted L1 distance matrices and construct NJ trees. Comparison with traditional phylogenetic logistic regression revealed similarity in top-ranked features, but also differences in overall weight distributions and clustering patterns in NJ trees. Our results highlight the complexity of constructing a biologically useful distance matrix from correlated phenotypic features, while the complementary nature of these weighting methods suggests the potential utility of multi-faceted approaches to assessing feature contributions.
Protein-compound interaction prediction is an important problem in drug discovery. Numerous machine learning methods have been proposed using protein sequences and compound structures as features. Several methods have used biological network information as additional features including protein-protein interactions and compound bioactivities. However, previous studies have only used network data from mammals such as human and mouse. Here we develop a new method for protein-compound interaction prediction that uses features learned from the relationships between microorganisms and secondary metabolites in nature (microbial chemical communication network; MCCN). We used node2vec representation learning to extract compound features from the MCCN, and deep canonical correlation analysis (CCA) to obtain the features for compounds not included in the MCCN. By incorporating these MCCN-derived features into an existing protein-compound interaction prediction method, we showed that prediction performance was improved in several benchmark experiments. We also discussed how to improve our method by incorporating microbiome co-occurrence information into the MCCN.
Molecular docking simulations utilizing scoring functions are pivotal for assessing the stability of complex formations. The unique biochemical characteristics of antibody-antigen interfaces, however, present challenges in applying general parameter sets of scoring functions to these molecules, necessitating the customization of the scoring function to enhance prediction accuracy for structural configurations and binding affinities. In response to this, we have developed models within the Rosetta software framework, widely recognized for its utility in predicting antigen-antibody docking, to optimize the parameters of its scoring function. Through a quantitative evaluation of the shape of decoy distribution generated by Rosetta, we have been able to refine the parameters for each antibody-antigen complex, yielding a notable improvement in the prediction accuracy of the software for a given dataset. Furthermore, we have identified a distinct parameter set that is effective for the majority of complexes in our dataset, though not universally applicable. This study introduces a novel approach to customizing scoring functions, potentially contributing to advancements in drug discovery and deepening our understanding of the complexities inherent in antibody-antigen interactions at a molecular level.
Identifying factors that contribute to microbial growth is important for realizing efficient production of useful substrates. Our objective was to predict unknown metabolic pathways from experimental time-series data in model organisms such as Escherichia coli. We focused on a previous method that replaces the computation of auto-regression in the Granger causality test with non-parametric multiplicative regression (NPMR) to allow inferences on noisy and nonlinear data. We then proposed a new causal inference method that creates a multi-dimensional space based on the error between the time series predicted by NPMR and the original time series. We confirmed that the inference accuracy of the proposed method outperforms that of NPMR by 50% using short time series generated by coupled logistic equations, which allows for adjustment of the strength of the causal relationship. The proposed method was applied to simulation data obtained from a kinetic model for glycolysis in E. coli and achieved 61% accuracy.
p.4 left column:Let Mn be the position vector of each optical mouse with respect to the center O of the treadmill sphere (white circle in Figure 3).Consider a vector extending from O by translating the tangent vector Tn of the motion.These normalized outer products (multiplied by the radius r of the spherical treadmill) are expressed as:
Eukaryotic genomes contain exons and introns, and it is necessary to accurately identify exon-intron boundaries, i.e., splice sites, to annotate genomes. To address this problem, many previous works have proposed annotation methods/tools based on RNA-seq evidence. Many recent works exploit neural networks (NNs) as their prediction models, but only a few can be used to generate new genome annotation in practice. In this study, we propose AtLASS, a fully automated method for predicting splice sites from genomic and RNA-seq data using attention-based Bi-LSTM (Bidirectional Long Short-Term Memory). We exploit two-stage training on RNA-seq data to address the problem of biased label problem, thereby reducing the false positives. The experiments on the genomes of three species show that the performance of the proposed method itself is comparable to that of existing methods, but we can achieve better performance by combining the outputs of the proposed method and the existing method. The proposed method is the first program specialized in end-to-end splice site prediction using NNs.
