Additional file 1 of Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Additional file 1. Novel DTIs predicted by SDGAE.xlsx: it contains 30 candidate targets for all drugs in the dataset. The candidate targets for each drug are sorted in descending order according to their prediction scores

Datasheet1_Global research trends and emerging opportunities for integrin adhesion complexes in cardiac repair: a scientometric analysis.docx

ObjectiveCardiac regenerative medicine has gained significant attention in recent years, and integrins are known to play a critical role in mediating cardiac development and repair, especially after an injury from the myocardial infarction (MI). Given the extensive research history and interdisciplinary nature of this field, a quantitative retrospective analysis and visualization of related topics is necessary.Materials and methodsWe performed a scientometric analysis of published papers on cardiac integrin adhesion complexes (IACs), including analysis of annual publications, disciplinary evolution, keyword co-occurrence, and literature co-citation.ResultsA total of 2,664 publications were finally included in the past 20 years. The United States is the largest contributor to the study and is leading this area of research globally. The journal Circulation Research attracts the largest number of high-quality publications. The study of IACs in cardiac repair/regenerative therapies involves multiple disciplines, particularly in materials science and developmental biology. Keywords of research frontiers were represented by Tenasin-C (2019–2023) and inflammation (2020–2023).ConclusionIntegrins are topics with ongoing enthusiasm in biological development and tissue regeneration. The rapidly emerging role of matricellular proteins and non-protein components of the extracellular matrix (ECM) in regulating matrix structure and function may be a further breakthrough point in the future; the emerging role of IACs and their downstream molecular signaling in cardiac repair are also of great interest, such as induction of cardiac proliferation, differentiation, maturation, and metabolism, fibroblast activation, and inflammatory modulation.</p

Classification of Time Series Gene Expression in Clinical Studies via Integration of Biological Network

<div><p>The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of–the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction.</p> </div

Classification accuracies of different discretization methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).

<p>Classification accuracies of different discretization methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).</p

Classification accuracies of distinct classification methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).

<p>Classification accuracies of distinct classification methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).</p

Precision, Recall and F-measure of different classification approaches.

<p>The bars and error ticks represent mean values and standard deviations respectively. (A) shows the result for Baranzini dataset. (B) shows the result for Goertsches dataset.</p

Classification accuracies of PPI-SVM-KNN with the change of parameter C.

<p>The bars and error ticks represent mean values and standard deviations respectively. (A) shows the result for Baranzini dataset. (B) shows the result for Goertsches dataset.</p

Prediction accuracies of different classification approaches with the change of measurements.

<p>The points in the figure represent mean values. (A) shows the accuracies from time point 3 to time point 7 for Baranzini dataset. (B) shows the accuracies from time point 3 to time point 5 for Goertsches dataset.</p

Classification accuracies of PPI-SVM-KNN with the change of parameter K from 3 to 9.

<p>The bars and error ticks represent mean values and standard deviations respectively. (A) shows the result for Baranzini dataset. (B) shows the result for Goertsches dataset.</p

Schematic overview of classification of time series gene expression.

<p>The prediction process primarily consists of 4 or 5 steps. Firstly, gene states are inferred by an HMM/GMM hybrid model. Secondly, the QL-biclustering algorithm extracts biclusters of every patient from the gene state matrix. Thirdly, every bicluster is scored according to its genes' connection in the protein-protein interaction (PPI) network. Finally, the label of every test patient is predicted by PPI-SVM-KNN based on patient similarity, taking into account both bicluster similarity and its PPIScore.</p