Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

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The Structure and Function of Complex Networks

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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Highly accurate protein structure prediction with AlphaFold

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

In a recent letter (Science, 1992, 257, 522) the
time-dependent wavepacket approach to reactive-scattering was utilized
to extract state-to-state scattering cross-sections for
D+H
 2
(v, j)→H+DH(, @#x0304;) at
high energies. Results from 1.3 up to 2.25 eV total energy, with a
fine enough grid to enable appropriate averages simulating the
experimental conditions, are reported. We briefly review the formalism
used for the simulations and explain how coordinate grid-reduction
techniques concentrate the numerical effort in a small, strong
interaction region, enabling calculations of state-resolved
cross-sections for this demanding reaction. We then study the results
in more detail by examining their variation with collision energy,
final rotational state, and the individual initial H
 2

magnetic quantum number (measured along the incident linear momentum
of the D atom relative to the H
 2
 centre-of-mass).

State-to-state time-dependent wavepacket approach to reactivescattering: State-resolved cross-sections forD+H2(v=1, j=1,m)→H+DH(v,j)

Quantitative structure-activity relationship (QSAR) models are important tools used in discovering new drug candidates and identifying potentially harmful environmental chemicals These models often face two fundamental challenges: limited amount of available biological activity data and noise or uncertainty in the activity data themselves To address these challenges, we introduce and explore a QSAR model based on custom distance metrics in the structure-activity space The model is built on top of the k-nearest neighbor model, incorporating non-linearity not only in the chemical structure space, but also in the biological activity space The model is tuned and evaluated using activity data for human estrogen receptor from the US EPA ToxCast and Tox21 databases The model closely trails the CERAPP consensus model (built on top of 48 individual human estrogen receptor activity models) in agonist activity predictions and consistently outperforms the CERAPP consensus model in antagonist activity predictions We suggest that incorporating non-linear distance metrics may significantly improve QSAR model performance when the available biological activity data are limited

/pdf/exploring-non-linear-distance-metrics-in-the-structure-4ncgz4ph3u.pdf

Exploring non-linear distance metrics in the structure-activity space: QSAR models for human estrogen receptor.

Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: Retinoic acid as an example.

Variability in the ABCA1 gene but not HMG-CoA reductase predicts low-density lipoprotein lowering effects of statins

Poster presented to the Society of Environmental Toxicology and Chemistry (SETAC) 40th annual meeting in Nov 2019

/pdf/variability-in-in-vivo-toxicity-studies-defining-the-upper-5ca603do5m.pdf

Richard S. Judson

Papers

State-to-state time-dependent wavepacket approach to reactivescattering: State-resolved cross-sections forD+H2(v=1, j=1,m)→H+DH(v,j)

Exploring non-linear distance metrics in the structure-activity space: QSAR models for human estrogen receptor.

Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: Retinoic acid as an example.

Variability in the ABCA1 gene but not HMG-CoA reductase predicts low-density lipoprotein lowering effects of statins

Variability in in vivo Toxicity Studies: Defining the upper limit of predictivity for models of systemic effect levels