Articles

A goal of biology is to identify the molecular mechanisms that control differential gene expression. Tasaki et al. have developed a framework that integrates genomic data into a deep learning model of transcriptome regulations to predict multiple transcriptional effects in tissue- and person-specific transcriptomes.

Tumour mutational burden (TMB) shows promise as a biomarker in cancer immunotherapy, but it usually requires whole-exome sequencing, which is costly, time-consuming and unavailable at most hospitals. The authors develop a machine learning algorithm that uses standard H&E histopathological images to quickly, inexpensively and accurately predict TMB. The approach may have applications as a tool to screen and prioritize patient samples and subsequent treatments.

Gene sets can provide valuable information for gaining insight into disease mechanisms and cellular functions. In this paper, the authors use a Gaussian approach to represent gene sets and gene networks in a low-dimensional space, allowing for accurate prediction and decreased computational complexity.

Spiking neural networks and in-memory computing are both promising routes towards energy-efficient hardware for deep learning. Woźniak et al. incorporate the biologically inspired dynamics of spiking neurons into conventional recurrent neural network units and in-memory computing, and show how this allows for accurate and energy-efficient deep learning.

Vascular abnormalities are challenging for diagnostic imaging due to the complexity of vasculature and the non-uniform scattering from biological tissues. The authors present an unsupervised learning algorithm for vascular feature recognition from small sets of biomedical images acquired from different modalities. They demonstrate the utility of their diagnostic approach on vascular images of thrombosis, internal bleeding and colitis.

A lot of scientific literature is unstructured, which makes extracting information for biomedical databases difficult. Hong and colleagues show that a distant supervision approach, using latent tree learning and recurrent units, can extract drug–target interactions from literature that were previously unknown.

A fundamental problem in network science is how to find an optimal set of key players whose activation or removal significantly impacts network functionality. The authors propose a deep reinforcement learning framework that can be trained on small networks to understand the organizing principles of complex networked systems.

The rise of deep neural networks allows for new ways to design molecules that interact with biological structures. An approach that uses conditional recurrent neural networks generates molecules with properties near specified conditions.

Predicting overall survival for patients with confirmed non-small-cell lung cancer is an important issue in clinical practice. The authors developed and validated in four independent patient cohorts a shallow convolutional neural network that can predict the outcomes of individuals using pre-treatment CT images. The authors further show that the survival model can be used, via transfer learning, for classifying benign versus malignant nodules.

While computerization and digitization of medicine have advanced substantially, management tools in healthcare have not yet benefited much from these developments due to the extreme complexity and variability of healthcare operations. The ability of machine learning algorithms to build strong models from a large number of weakly predictive features, and to identify key factors in complex feature sets, is tested in operational problems involving hospital datasets on workflow and patient waiting time.

Early and accurate clinical assessment of disease severity in COVID-19 patients is essential for planning the allocation of scarce hospital resources. An explainable machine learning tool trained on blood sample data from 485 patients from Wuhan selected three biomarkers for predicting mortality of individual patients with high accuracy.

Current neural networks attempt to learn spatial and temporal information as a whole, limiting their ability to process complex video data. Pang et al. improve performance by introducing a network structure which learns to implicitly decouple complex spatial and temporal concepts.

The deep convolutional recurrent neural network ‘PredNet’ can be trained to predict future video frames in a self-supervised manner. A surprising result is that it captures a wide array of phenomena observed in natural neuronal systems, ranging from low-level visual cortical neuron response properties to high-level perceptual illusions, hinting at potential similarities between recurrent predictive neural network models and computations in the brain.

4D MRI scans can reconstruct cardiovascular flow, although they typically take many minutes, hindering real-time assessment. Vishnevskiy et al. develop a deep variational network to permit high-fidelity image reconstruction in a matter of seconds, allowing integration of 4D flow MRI into clinical workflows.

A lack of accurate and efficient variant calling methods has held back single-molecule sequencing technologies from clinical applications. The authors present a deep-learning method for fast and accurate germline small variant calling, using single-molecule sequencing data.

Finding the best ratio of ingredients for polymerization reactions can be time consuming and wasteful. An automated microreactor process with integrated machine learning analysis initiates reactions, measures the resulting yield and cleans itself without human intervention. It can test concentrations of reagents systematically to find the combination with the highest production, while producing a low amount of waste.

Integrating knowledge about the circuit-level organization of the brain into neuromorphic artificial systems is a challenging research problem. The authors present a neural algorithm for the learning of odourant signals and their robust identification under noise, based on the architecture of the mammalian olfactory bulb and implemented on the Intel Loihi neuromorphic system.

With the aid of deep learning, the space of chemical molecules, such as candidates for drugs, can be constrained to find new bioactive molecules. A new open source tool can generate libraries of novel molecules with user defined properties.

When predicting the interaction of proteins with potential drugs, the protein can be encoded as its one-dimensional sequence or a three-dimensional structure, which can capture more relevant features of the protein, but also makes the task to predict the interactions harder. A new method predicts these interactions using a two-dimensional distance matrix representation of a protein, which can be processed like a two-dimensional image, striking a balance between the data being simple to process and rich in relevant structures.

Getting safe and fast access to blood vessels is vital to many methods of treatment and diagnosis in medicine. Robot-assisted or even fully autonomous methods can potentially do the task more reliably than humans, especially when veins are hard to detect. In this work, a method is tested that uses deep learning to find blood vessels and track the movement of a patient’s arm.