The scoring of poses in protein-protein docking: current capabilities and future directions

BACKGROUND: Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling. RESULTS: We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically. CONCLUSIONS: All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm

Étude algorithmique et combinatoire de la méthode de Kemeny-Young et du consensus de classements

Une permutation est une liste qui ordonne des objets ou des candidats en fonction d’une préférence ou d’un critère. Des exemples sont les résultats d’un moteur de recherche sur l’internet, des classements d’athlètes, des listes de gènes liés à une maladie données par des méthodes de prédiction ou simplement des préférences d’activités à faire pour la pro- chaine fin de semaine. On peut être intéressé à agréger plusieurs permutations pour en obtenir une permutation consensus. Ce problème est bien connu en science politique et plusieurs méthodes existent pour agréger des permutations, chacune ayant ses propriétés mathématiques. Parmi ces méthodes, la méthode de Kemeny-Young, aussi nommée la médiane de permutations, permet de trouver un consensus qui minimise la somme des distances entre ce consensus et l’ensemble de permutations. Cette méthode détient plu- sieurs propriétés désirables. Par contre, elle est difficile à calculer, ouvrant par ce fait, la voie à de nombreux travaux de recherche. Une généralisation de ce problème permet de considérer les classements qui contiennent des égalités entre les objets classés et qui peuvent être incomplets en ne considérant qu’un sous-ensemble d’objets. Dans cette thèse nous étudions la méthode de Kemeny-Young sous différents aspects : — Premièrement, une réduction d’espace de recherche est proposée. Elle permet d’améliorer les temps de calcul d’approches exactes pour le problème. — Deuxièmement, une heuristique bien paramétrée est développée et sert par le gui- dage d’un algorithme exact branch-and-bound. Cet algorithme utilise aussi une nouvelle réduction d’espace. — Troisièmement, le cas particulier du problème sur trois permutations est investigué. Une réduction d’espace de recherche basée sur les graphes est proposée pour ce cas, suivi d’une borne inférieure très stricte. Deux conjectures sont émises et font le lien entre ce cas et le problème du 3-Hitting Set. — Finalement, une généralisation du problème est proposée et permet d’étendre nos travaux de réduction d’espace de recherche à l’agrégation de classements.A permutation is a list that orders objects or candidates with a preference function or a criterion. Some examples include results from a search engine on the internet, athlete rankings, lists of genes related to a disease given by prediction methods or simply the preference of activities for the next weekend. One might be interested to aggregate a set of permutations to get a consensus permutation. This problem is well known in political science and many methods exists that can aggregate permutations, each one having its mathematical properties. Among those methods, the Kemeny-Young method, also known as the median of permutations, finds a consensus that minimise the sum of distances between that consensus and the set of permutations. This method holds many desirable properties. On the other end, this method is difficult to calculate, thus opening the way for research works. A generalization of this problem considers rankings containing ties between the ranked objects and rankings that might be incomplete by considering only a subset of objects. In this thesis, we study the Kemeny-Young method under different aspects : — Firstly, a search space reduction technique is proposed. It improves the time com- plexity of exact algorithms for the problem. — Secondly, a well parameterized heuristic is developed and is used as guidance in a branch-and-bound exact algorithm. This algorithm also uses a new search space reduction technique. — Thirdly, the special case of the problem on three permutations is investigated. A search space reduction technique based on graphs is presented for this case, followed by a very tight lower bound. Two conjectures are stated and are linking this case with the 3-Hitting Set problem. — Finally, a generalization of the problem is proposed and allows us to extend our work on search space reduction techniques to the rank aggregation problem

Spacelab Science Results Study

Beginning with OSTA-1 in November 1981 and ending with Neurolab in March 1998, a total of 36 Shuttle missions carried various Spacelab components such as the Spacelab module, pallet, instrument pointing system, or mission peculiar experiment support structure. The experiments carried out during these flights included astrophysics, solar physics, plasma physics, atmospheric science, Earth observations, and a wide range of microgravity experiments in life sciences, biotechnology, materials science, and fluid physics which includes combustion and critical point phenomena. In all, some 764 experiments were conducted by investigators from the U.S., Europe, and Japan. The purpose of this Spacelab Science Results Study is to document the contributions made in each of the major research areas by giving a brief synopsis of the more significant experiments and an extensive list of the publications that were produced. We have also endeavored to show how these results impacted the existing body of knowledge, where they have spawned new fields, and if appropriate, where the knowledge they produced has been applied

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Feature Papers of Drones - Volume II

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 24–41 are focused on drone applications, but emphasize two types: firstly, those related to agriculture and forestry (articles 24–35) where the number of applications of drones dominates all other possible applications. These articles review the latest research and future directions for precision agriculture, vegetation monitoring, change monitoring, forestry management, and forest fires. Secondly, articles 36–41 addresses the water and marine application of drones for ecological and conservation-related applications with emphasis on the monitoring of water resources and habitat monitoring. Finally, articles 42–54 looks at just a few of the huge variety of potential applications of civil drones from different points of view, including the following: the social acceptance of drone operations in urban areas or their influential factors; 3D reconstruction applications; sensor technologies to either improve the performance of existing applications or to open up new working areas; and machine and deep learning development