Exploiting ladder networks for gene expression classification

The application of deep learning to biology is of increasing relevance, but it is difficult; one of the main difficulties is the lack of massive amounts of training data. However, some recent applications of deep learning to the classification of labeled cancer datasets have been successful. Along this direction, in this paper, we apply Ladder networks, a recent and interesting network model, to the binary cancer classification problem; our results improve over the state of the art in deep learning and over the conventional state of the art in machine learning; achieving such results required a careful adaptation of the available datasets and tuning of the network

Drosophila CG3303 is an essential endoribonuclease linked to TDP-43-mediated neurodegeneration

Endoribonucleases participate in almost every step of eukaryotic RNA metabolism, acting either as degradative or biosynthetic enzymes. We previously identified the founding member of the Eukaryotic EndoU ribonuclease family, whose components display unique biochemical features and are flexibly involved in important biological processes, such as ribosome biogenesis, tumorigenesis and viral replication. Here we report the discovery of the CG3303 gene product, which we named DendoU, as a novel family member in Drosophila. Functional characterisation revealed that DendoU is essential for Drosophila viability and nervous system activity. Pan-neuronal silencing of dendoU resulted in fly immature phenotypes, highly reduced lifespan and dramatic motor performance defects. Neuron-subtype selective silencing showed that DendoU is particularly important in cholinergic circuits. At the molecular level, we unveiled that DendoU is a positive regulator of the neurodegeneration-associated protein dTDP-43, whose downregulation recapitulates the ensemble of dendoU-dependent phenotypes. This interdisciplinary work, which comprehends in silico, in vitro and in vivo studies, unveils a relevant role for DendoU in Drosophila nervous system physio-pathology and highlights that DendoU-mediated neurotoxicity is, at least in part, contributed by dTDP-43 loss-of-function

Semi-Supervised Learning with Scarce Annotations

While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small. In this work, we consider the problem of SSL multi-class classification with very few labelled instances. We introduce two key ideas. The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label. The second idea is a new algorithm for SSL that can exploit well such a pre-trained representation. The algorithm works by alternating two phases, one fitting the labelled points and one fitting the unlabelled ones, with carefully-controlled information flow between them. The benefits are greatly reducing overfitting of the labelled data and avoiding issue with balancing labelled and unlabelled losses during training. We show empirically that this method can successfully train competitive models with as few as 10 labelled data points per class. More in general, we show that the idea of bootstrapping features using self-supervised learning always improves SSL on standard benchmarks. We show that our algorithm works increasingly well compared to other methods when refining from other tasks or datasets.Comment: Workshop on Deep Vision, CVPR 202

Adversarial Variational Embedding for Robust Semi-supervised Learning

Semi-supervised learning is sought for leveraging the unlabelled data when labelled data is difficult or expensive to acquire. Deep generative models (e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial Networks (GANs) have recently shown promising performance in semi-supervised classification for the excellent discriminative representing ability. However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance. In particular, the learned latent representation depends on a non-exclusive component which is stochastically sampled from the prior distribution. Moreover, the semi-supervised GAN models generate data from pre-defined distribution (e.g., Gaussian noises) which is independent of the input data distribution and may obstruct the convergence and is difficult to control the distribution of the generated data. To address the aforementioned issues, we propose a novel Adversarial Variational Embedding (AVAE) framework for robust and effective semi-supervised learning to leverage both the advantage of GAN as a high quality generative model and VAE as a posterior distribution learner. The proposed approach first produces an exclusive latent code by the model which we call VAE++, and meanwhile, provides a meaningful prior distribution for the generator of GAN. The proposed approach is evaluated over four different real-world applications and we show that our method outperforms the state-of-the-art models, which confirms that the combination of VAE++ and GAN can provide significant improvements in semisupervised classification.Comment: 9 pages, Accepted by Research Track in KDD 201

Molecular Profiling of the Adult Corticospinal Tract Reveals Transcriptional, Anatomical, and Reactive Heterogeneity

The corticospinal tract (CST) is the major descending motor tract responsible for voluntary movement in all mammals. Corticospinal neurons (CSNs) have cell bodies in layer 5 of the primary motor cortex, with axons that descend through the internal capsule, decussate at the medullary pyramids, and innervate every spinal segment along the entire spinal neuro-axis. Injury, disease, and neurodegeneration within this pathway result in chronic, irreversible functional deficits in motor and sensory function due to inhibitory extrinsic substrates, including central nervous system (CNS) myelin and chondroitin sulfate proteoglycans (CSPGs) in the extracellular matrix, and poor intrinsic growth capacity. Current strategies for repairing the CST after injury remain woefully incomplete due to the underexplored molecular diversity among neurons in this tract. To study the heterogeneity of uninjured adult CSNs, we developed a method for robust dissociation of cortical layer 5 pyramidal neurons, ensuring optimal cytoplasmic integrity. Using our protocol, we combined retrograde tracing from the cervical and lumbar spinal cord with single-cell RNA sequencing (scRNAseq) to build a transcriptional atlas of adult CSNs. Using publicly available datasets, we ascribed anatomical identity to molecularly distinct CSNs, showing that CSNs segregate based on supraspinal connectivity, in addition to spinal connectivity. By leveraging machine learning tools, we built a classifier that can reliably identify CSNs in M1 from layers 2, 3 and 5 pyramidal neurons. To explore CSN diversity in the context of injury, we performed bulk RNA sequencing (RNAseq) of CSNs from mice that had undergone spinal cord injury (SCI) and coupled it with two models of enhanced plasticity, a genetic knockout of Nogo Receptor 1 (Ngr1) or task-specific rehabilitation. Combining these bulk RNAseq studies with data from the single-cell CSN atlas enabled us to putatively assign each CSN with a plasticity index, revealing that intratelencephalic CSNs have an enhanced, innate plasticity potential. Lastly, by comparing CSNs during uniquely defined phases of postnatal patterning, we identified putative positive and negative regulators of long-distance axon growth. Together, these studies represent the first transcriptional characterization of the CST at the single-cell level. These data enable future studies that will explore the molecular mechanisms associated with CST plasticity and repair, and ultimately facilitate development of therapies that enhance functional recovery for individuals with SCI

Classification of Prostate Cancer Patients into Indolent and Aggressive Using Machine Learning

Prostate cancer (PCa) is the second most common cancer in men in the US. Many Prostate cancers are Indolent and don’t result in cancer mortality, even without treatment. However, a significant proportion of patients with Prostate cancer have aggressive tumors that progress rapidly to metastatic disease and are often dangerous. Currently, treatment decisions for PCa patients are guided by various stratification algorithms. Among these parameters, the most important predictor of PCa mortality is the Gleason Grade (ranges from 6 to 10). Although current risk stratification tools are moderately effective, limitation remains in their ability to distinguish truly Indolent from aggressive and potentially lethal disease. Here we propose the use of Machine Learning (ML) for the classification of PC patients as having either indolent or aggressive using transcriptome data. We hypothesize that genomic alterations could lead to measurable changes distinguishing indolent from aggressive tumors. We also trained a Stacking-based model with a different set of combinations of classifiers. The highest overall accuracy of our stacking model (all samples with Gleason Grade: 6, 7, 8, 9, and 10) is 95.758% and (samples with Gleason Grade: 6, 8, 9, and 10) is 97.19%

Brownian motion: a paradigm of soft matter and biological physics

This is a pedagogical introduction to Brownian motion on the occasion of the 100th anniversary of Einstein's 1905 paper on the subject. After briefly reviewing Einstein's work in its contemporary context, we pursue some lines of further developments and applications in soft condensed matter and biology. Over the last century Brownian motion became promoted from an odd curiosity of marginal scientific interest to a guiding theme pervading all of the modern (live) sciences.Comment: 30 pages, revie