100,345 research outputs found
Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes
Complexes of physically interacting proteins constitute fundamental
functional units responsible for driving biological processes within cells. A
faithful reconstruction of the entire set of complexes is therefore essential
to understand the functional organization of cells. In this review, we discuss
the key contributions of computational methods developed till date
(approximately between 2003 and 2015) for identifying complexes from the
network of interacting proteins (PPI network). We evaluate in depth the
performance of these methods on PPI datasets from yeast, and highlight
challenges faced by these methods, in particular detection of sparse and small
or sub- complexes and discerning of overlapping complexes. We describe methods
for integrating diverse information including expression profiles and 3D
structures of proteins with PPI networks to understand the dynamics of complex
formation, for instance, of time-based assembly of complex subunits and
formation of fuzzy complexes from intrinsically disordered proteins. Finally,
we discuss methods for identifying dysfunctional complexes in human diseases,
an application that is proving invaluable to understand disease mechanisms and
to discover novel therapeutic targets. We hope this review aptly commemorates a
decade of research on computational prediction of complexes and constitutes a
valuable reference for further advancements in this exciting area.Comment: 1 Tabl
Machine learning for early detection of traffic congestion using public transport traffic data
The purpose of this project is to provide better knowledge of how the bus travel times is affected by congestion and other problems in the urban traffic environment. The main source of data for this study is second-level measurements coming from all buses in the Linköping region showing the location of each vehicle.The main goal of this thesis is to propose, implement, test and optimize a machine learning algorithm based on data collected from regional buses from Sweden so that it is able to perform predictions on the future state of the urban traffic.El objetivo principal de este proyecto es proponer, implementar, probar y optimizar un algoritmo de aprendizaje automático basado en datos recopilados de autobuses regionales de Suecia para que poder realizar predicciones sobre el estado futuro del tráfico urbano.L'objectiu principal d'aquest projecte Ă©s proposar, implementar, provar i optimitzar un algoritme de machine learning basat en dades recollides a partir d'autobusos regionals de Suècia de manera per poder realitzar prediccions sobre l'estat futur del trĂ nsit urbĂ
Deep Predictive Models for Collision Risk Assessment in Autonomous Driving
In this paper, we investigate a predictive approach for collision risk
assessment in autonomous and assisted driving. A deep predictive model is
trained to anticipate imminent accidents from traditional video streams. In
particular, the model learns to identify cues in RGB images that are predictive
of hazardous upcoming situations. In contrast to previous work, our approach
incorporates (a) temporal information during decision making, (b) multi-modal
information about the environment, as well as the proprioceptive state and
steering actions of the controlled vehicle, and (c) information about the
uncertainty inherent to the task. To this end, we discuss Deep Predictive
Models and present an implementation using a Bayesian Convolutional LSTM.
Experiments in a simple simulation environment show that the approach can learn
to predict impending accidents with reasonable accuracy, especially when
multiple cameras are used as input sources.Comment: 8 pages, 4 figure
Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams
In this work, we introduce a new algorithm for analyzing a diagram, which
contains visual and textual information in an abstract and integrated way.
Whereas diagrams contain richer information compared with individual
image-based or language-based data, proper solutions for automatically
understanding them have not been proposed due to their innate characteristics
of multi-modality and arbitrariness of layouts. To tackle this problem, we
propose a unified diagram-parsing network for generating knowledge from
diagrams based on an object detector and a recurrent neural network designed
for a graphical structure. Specifically, we propose a dynamic graph-generation
network that is based on dynamic memory and graph theory. We explore the
dynamics of information in a diagram with activation of gates in gated
recurrent unit (GRU) cells. On publicly available diagram datasets, our model
demonstrates a state-of-the-art result that outperforms other baselines.
Moreover, further experiments on question answering shows potentials of the
proposed method for various applications
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