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Transcription factor regulatory networks underlie major features of cellular identity and complex function such as pluripotency, development and differentiation. Li and colleagues develop a graph neural network to predict transcription factor regulatory networks based on single-cell ATAC-seq data.
Accurate prediction of complex systems such as protein folding, weather forecasting and social dynamics is a core challenge in various disciplines. By fusing machine learning algorithms and classic equation-free methodologies, it is possible to reduce the computational effort of large-scale simulations by up to two orders of magnitude while maintaining the prediction accuracy of the full system dynamics.
Multiplex immunofluorescence imaging can provide a wealth of data compared to immunohistochemical staining, which is cheaper and more widely available. Ghahremani et al. present DeepLIIF, a GAN-based cell segmentation and classification approach, which is trained on co-registered images of these two modalities to provide the insights from the more data-rich muliplex data from simpler IHC images.
Fragmentation of peptides leaves characteristic patterns in mass spectrometry data, which can be used to identify protein sequences, but this method is challenging for mutated or modified sequences for which limited information exist. Altenburg et al. use an ad hoc learning approach to learn relevant patterns directly from unannotated fragmentation spectra.
An automated workflow for scanning probe microscopy, steered by an active learning framework, can efficiently explore relationships between local domain structure and physical properties. Such a capability is demonstrated in a piezoresponse force microscopy experiment to guide measurements of ferroelectric materials.
To perform electronic structure calculations in quantum chemistry systems, methods are needed that are both accurate and scalable as the size of the molecule of interest increases. Barrett and colleagues employ an autoregressive neural-network ansatz that allows them to study larger molecules than previously attempted with neural-network quantum state approaches.
The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.
The human leukocyte antigen (HLA) complex plays an important role in building an immune response, but it is hard to predict which peptides will bind to it. Chu et al. present a transformer-based approach to identify which peptides have a high binding affinity to HLA, a task that can also be translated to other binding problems.
It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.
The spatial homogeneity of urban road networks can be quantified in a fine-grained manner with graph neural networks. This method is studied across 11,790 inner-city road networks around the world and can be used to study socioeconomic development and help with urban planning.
Large language models identify patterns in the relations between words and capture their relations in an embedding space. Schramowski and colleagues show that a direction in this space can be identified that separates ‘right’ and ‘wrong’ actions as judged by human survey participants.
GPUs, which are highly parallel computer processing units, were originally designed for graphics applications, but they have played an important role in accelerating the development of deep learning methods. In this Review, Pandey and colleagues summarize how GPUs have advanced machine learning in the field of drug discovery.
Deep learning methods have in recent years shown promising results in characterizing proteins and extracting complex sequence–structure–function relationships. This Analysis describes a benchmarking study to compare the performances and advantages of recent deep learning approaches in a range of protein prediction tasks.
Knowledge of the wide array of epigenomic signals provides biological insight into the state of a give cell type, but it is infeasible to experimentally characterize all possible types of epigenomic signal in the multitude of cell types in the human body. The authors present Ocelot, a machine learning approach for imputing cell-type-specific epigenomic signals along the genome.
Quantum annealers are computational models implemented on quantum hardware that can efficiently solve combinatorial optimization problems. Annealing schedules with enhanced performance can be discovered with a Monte Carlo tree search algorithm and an enhanced version incorporating value and policy neural networks—as inspired by DeepMind’s AlphaZero.