MindReader: Unsupervised Classification of Electroencephalographic Data.

Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes interpretation time-consuming, resource-hungry, and overall an expensive process. Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data and optimizing the allocation of human resources towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader's predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use.

Whole-genome comparison of endogenous retrovirus segregation across wild and domestic host species populations.

Although recent advances in sequencing and computational analyses have facilitated use of endogenous retroviruses (ERVs) for deciphering coevolution among retroviruses and their hosts, sampling effects from different host populations present major challenges. Here we utilize available whole-genome data from wild and domesticated European rabbit (Oryctolagus cuniculus sp.) populations, sequenced as DNA pools by paired-end Illumina technology, for identifying segregating reference as well as nonreference ERV loci, to reveal their variation along the host phylogeny and domestication history. To produce new viruses, retroviruses must insert a proviral DNA copy into the host nuclear DNA. Occasional proviral insertions into the host germline have been passed down through generations as inherited ERVs during millions of years. These ERVs represent retroviruses that were active at the time of infection and thus present a remarkable record of historical virus-host associations. To examine segregating ERVs in host populations, we apply a reference library search strategy for anchoring ERV-associated short-sequence read pairs from pooled whole-genome sequences to reference genome assembly positions. We show that most ERVs segregate along host phylogeny but also uncover radiation of some ERVs, identified as segregating loci among wild and domestic rabbits. The study targets pertinent issues regarding genome sampling when examining virus-host evolution from the genomic ERV record and offers improved scope regarding common strategies for single-nucleotide variant analyses in host population comparative genomics.