Protein–protein interactions are important for the majority of biological processes. A significant number of computational methods have been developed to predict protein–protein interactions using protein sequence, structural and genomic data. Vast experimental data is publicly available on the Internet, but it is scattered across numerous databases. This fact motivated us to create and evaluate new high-throughput datasets of interacting proteins. We extracted interaction data from DIP, MINT, BioGRID and IntAct databases. Then we constructed descriptive features for machine learning purposes based on data from Gene Ontology and DOMINE. Thereafter, four well-established machine learning methods: Support Vector Machine, Random Forest, Decision Tree and Naïve Bayes, were used on these datasets to build an Ensemble Learning method based on majority voting. In cross-validation experiment, sensitivity exceeded 80% and classification/prediction accuracy reached 90% for the Ensemble Learning method. We extended the experiment to a bigger and more realistic dataset maintaining sensitivity over 70%. These results confirmed that our datasets are suitable for performing PPI prediction and Ensemble Learning method is well suited for this task. Both the processed PPI datasets and the software are available at http://sysbio.icm.edu.pl/indra/EL-PPI/home.html.

The present pandemic forced our daily interactions to move into the virtual world. People had to adapt to new communication media that afford different ways of interaction. Remote communication decreases the availability and salience of some cues but also may enable and highlight others. Importantly, basic movement dynamics, which are crucial for any interaction as they are responsible for the informational and affective coupling, are affected. It is therefore essential to discover exactly how these dynamics change. In this exploratory study of six interacting dyads we use traditional variability measures and cross recurrence quantification analysis to compare the movement coordination dynamics in quasi-natural dialogues in four situations: (1) remote video-mediated conversations with a self-view mirror image present, (2) remote video-mediated conversations without a self-view, (3) face-to-face conversations with a self-view, and (4) face-to-face conversations without a self-view. We discovered that in remote interactions movements pertaining to communicative gestures were exaggerated, while the stability of interpersonal coordination was greatly decreased. The presence of the self-view image made the gestures less exaggerated, but did not affect the coordination. The dynamical analyses are helpful in understanding the interaction processes and may be useful in explaining phenomena connected with video-mediated communication, such as “Zoom fatigue”.

Accurate identification of protein–protein interactions (PPI) is the key step in understanding proteins’ biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein–protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein–protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent).

This study aims to investigate how daily activities affect mood in the context of social distancing guidelines enforced during the COVID‐19 pandemic. Using Ecological Momentary Assessment (EMA) administered four times a day during a 2‐week period, we asked participants ( N = 91) about their mood and the activities they engaged in. Seven individuals were selected for a follow‐up, open‐ended questionnaire. Results show that a stable routine, including physical exercise, hobbies, regular sleep hours, and minimal time spent in front of the computer, helps maintain a good mood. Coping strategies such as planning and scheduling help keep routines and circadian rhythms stable. Face‐to‐face contact is associated with a more positive mood, while similar interaction through electronic communication has a less positive effect. We observe an effect related to the infodemic phenomenon: Daily reports on COVID‐19 cases and deaths affect mood fluctuations. This is an important consideration in shaping public information policies.

We describe a method for assessing data set complexity based on the estimation of the underlining probability distribution and Hellinger distance. In contrast to some popular complexity measures, it is not focused on the shape of a decision boundary in a classification task but on the amount of available data with respect to the attribute structure. Complexity is expressed in terms of graphical plot, which we call complexity curve. It demonstrates the relative increase of available information with the growth of sample size. We perform theoretical and experimental examination of properties of the introduced complexity measure and show its relation to the variance component of classification error. We then compare it with popular data complexity measures on 81 diverse data sets and show that it can contribute to explaining performance of specific classifiers on these sets. We also apply our methodology to a panel of simple benchmark data sets, demonstrating how it can be used in practice to gain insights into data characteristics. Moreover, we show that the complexity curve is an effective tool for reducing the size of the training set (data pruning), allowing to significantly speed up the learning process without compromising classification accuracy. The associated code is available to download at:https://github.com/zubekj/complexity_curve(open source Python implementation).

Research concerning concepts in the cognitive sciences has been dominated by the information-processing approach, which has resulted in a certain narrowing of the range of questions and methods of investigation. Recent trends have sought to broaden the scope of such research, but they have not yet been integrated within a theoretical framework that would allow us to reconcile new perspectives with the insights already obtained. In this paper, we focus on the processes involved in early concept acquisition and demonstrate that certain aspects of these processes remain largely understudied. These aspects include the primacy of movement and coordination with others within a structured social environment as well as the importance of first-person experiences pertaining to perception and action. We argue that alternative approaches to cognition, such as ecological psychology, enactivism and interactivism, are helpful for foregrounding these understudied areas. These approaches can complement the extant research concerning concepts to help us obtain a more comprehensive view of knowledge structures, thus providing us with a new perspective on recurring problems, suggesting novel questions and enriching our methodological toolbox. This article is part of the theme issue ‘Concepts in interaction: social engagement and inner experiences’.