17Q21.31 Sub-Haplotypes Underlying H1-associated Risk for Parkinson’s Disease and Progressive Supranuclear Palsy Converge on Altered Glial Regulation

semanticscholar（2019）

Ronald M. Loeb Center for Alzheimer's disease | Department of Pathology | University of California Institute for Memory Impairments and Neurological Disorders | Pamela Sklar Division of Psychiatric Genomics Icahn Institute for Data Science and Genomic Technology | National Institute on Aging

Cited 3|Views10

Abstract

KR Bowles, DA Pugh, K Farrell, N Han, J TCW, Y Liu, SA Liang, L Qian, J Bendl, JF Fullard, AE Renton, A Casella, MA Iida, S Bandres-Ciga, Z Gan-Or, P Heutink, A Siitonen, S Bertelsen, CM Karch, SJ Frucht, BH Kopell , I Peter, YJ Park, PK Crane, JSK Kauwe, KL Boehme, GU Höglinger, PART working group, International Parkinson’s Disease Genomics Consortium (IPDGC), Progressive Supranuclear Palsy Genetics Consortium, A Charney , P Roussos, JC Wang, WW Poon, T Raj, JF Crary & AM Goate

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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