Many statistical machine approaches could ultimately highlight novel features of the etiology of complex diseases by analyzing multi-omics data. However, they are sensitive to some deviations in distribution when the observed samples are potentially contaminated with adversarial corrupted outliers (e.g., a fictional data distribution). Likewise, statistical advances lag in supporting comprehensive data-driven analyses of complex multi-omics data integration. We propose a novel non-linear M-estimator-based approach, "robust kernel machine regression (RobKMR)," to improve the robustness of statistical machine regression and the diversity of fictional data to examine the higher-order composite effect of multi-omics datasets. We address a robust kernel-centered Gram matrix to estimate the model parameters accurately. We also propose a robust score test to assess the marginal and joint Hadamard product of features from multi-omics data. We apply our proposed approach to a multi-omics dataset of osteoporosis (OP) from Caucasian females. Experiments demonstrate that the proposed approach effectively identifies the inter-related risk factors of OP. With solid evidence (p-value = 0.00001), biological validations, network-based analysis, causal inference, and drug repurposing, the selected three triplets ((DKK1, SMTN, DRGX), (MTND5, FASTKD2, CSMD3), (MTND5, COG3, CSMD3)) are significant biomarkers and directly relate to BMD. Overall, the top three selected genes (DKK1, MTND5, FASTKD2) and one gene (SIDT1 at p-value= 0.001) significantly bond with four drugs- Tacrolimus, Ibandronate, Alendronate, and Bazedoxifene out of 30 candidates for drug repurposing in OP. Further, the proposed approach can be applied to any disease model where multi-omics datasets are available.

When a baby emerges from the womb, the baby's brain already has all the learning essential for the correct functioning of the body. The neural circuits for breathing, heartbeat, kicking, sleeping, blood circulation, etc., are all ready! The baby can track a moving object, orient towards Mom or Dad's face, feed, or even has the desire to walk when you hold it up with feet touching a flat surface. Isn't that interesting when our whole premise is learning based on "DATA"? We start from a zygote, a cell formed by the fusion of the male and female gamete (gametes are the male and female with only 23 chromosomes, unlike the regular cells with 46 chromosomes).

We each developed from a single cell—a fertilized egg—that divided and divided and eventually gave rise to the trillions of cells, of hundreds of types, that constitute the tissues and organs of our adult bodies. Advancing our understanding of the molecular programs underlying the emergence and differentiation of these diverse cell types is of fundamental interest and will affect almost every aspect of biology and medicine. Recently, technological advances have made it possible to directly measure the gene expression patterns of individual cells ([ 1 ][1]). Such methods can be used to clarify cell types and to determine the developmental stage of individual cells ([ 2 ][2]). Single-cell transcriptional profiling of successive developmental stages has the potential to be particularly informative, as the data can be used to reconstruct developmental processes, as well as characterize the underlying genetic programs ([ 3 ][3], [ 4 ][4]). ![Figure][5] A genomic technique for tracking cellular development High-throughput single-cell genomic methods enable a global view of cell type diversifcation by transcriptome and epigenome CREDIT: N. DESAI/ SCIENCE FROM CAO ET AL. ([7][6]) AND BIORENDER When I began my doctoral studies in Jay Shendure's lab at the University of Washington, available single-cell sequencing techniques relied on the isolation of individual cells within physical compartments and thus were limited in terms of both throughput and cost. As a graduate student, I developed four high-throughput single-cell genomic techniques to overcome these limitations ([ 5 ][7]–[ 8 ][8]). Leveraging these methods, I profiled millions of single-cell transcriptomes from organisms, in species that included worms, mice, and humans. By quantifying the dynamics of embryonic development at single-cell resolution, I was able to map out the global genetic programs that control cell proliferation and differentiation at the whole-organism scale. By the 1980s, biologists had documented every developmental step in the nematode Caenorhabditis elegans , from a single-cell embryo to the adult worm, and mapped the connections of all of the worm's neurons ([ 9 ][9]). However, although the nematode worm has a relatively small cell number (558 cells at hatching), a comprehensive understanding of the molecular basis for the specification of these cell types remains difficult. To resolve cellular heterogeneity, I first developed a method to specifically label the transcriptomes of large numbers of single cells, which we called sci-RNA-seq (single-cell combinatorial indexing RNA sequencing) ([ 5 ][7]). This method is based on combinatorial indexing, a strategy using split-pool barcoding of nucleic acids to label vast numbers of single cells within a single experiment ([ 9 ][9]). In this study, I profiled nearly 50,000 cells from C. elegans at the L2 stage, which is more than 50-fold “shotgun cellular coverage” of its somatic cell composition. We further defined consensus expression profiles for 27 cell types and identified rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. This was the first study to show that single-cell transcriptional profiling is sufficient to separate all major cell types from an entire animal. C. elegans development follows a tightly controlled genetic program. Other multicellular organisms, such as mice and humans, have much more developmental flexibility. However, conventional approaches for mammalian single-cell profiling lack the throughput and resolution to obtain a global view of the molecular states and trajectories of the rapidly diversifying and expanding cell types. To investigate cell state dynamics in mammalian development, I developed an even more scalable single-cell profiling technique, sci-RNA-seq3 ([ 7 ][6]), and used it to trace the development path of 2 million mouse cells as they traversed diverse paths in a 4-day window of development corresponding to organogenesis (embryonic day 9.5 to embryonic day 13.5). From these data, we characterized the dynamics of cell proliferation and key regulators for each cell lineage, a potentially foundational resource for understanding how the hundreds of cell types forming a mammalian body are generated in development. This was, and remains, the largest publicly available single-cell transcriptional dataset. The sci-RNA-seq3 method enabled this dataset to be generated rapidly, within a few weeks, by a single individual. A major challenge regarding current single-cell assays is that nearly all such methods capture just one aspect of cellular biology (typically mRNA expression), limiting the ability to relate different components to one another and to infer causal relationships. Another technique that I developed, sci-CAR (single-cell combinatorial indexing chromatin accessibility and mRNA) ([ 6 ][10]), was created with the goal of overcoming this limitation, allowing the user to jointly profile the epigenome (chromatin accessibility) and transcriptome (mRNA). I applied sci-CAR to the mouse whole kidneys, recovering all major cell types and linking cis-regulatory sites to their target genes on the basis of the covariance of chromatin accessibility and transcription across large numbers of single cells. To further explore the gene regulatory mechanisms, I invented sci-fate ([ 8 ][8]), a new method that identifies the temporal dynamics of transcription by distinguishing newly synthesized mRNA transcripts from “older” mRNA transcripts in thousands of individual cells. Applying the strategy to cancer cell state dynamics in response to glucocorticoids, we were able to link transcription factors (TFs) with their target genes on the basis of the covariance between TF expression and the amount of newly synthesized RNA across thousands of cells. In summary, my dissertation involved developing the technical framework for quantifying gene expression and chromatin dynamics across thousands to millions of single cells and applying these technologies to profile complex, developing organisms. The methods that I developed enable such projects to be achievable by a single individual, rather than requiring large consortia. Looking ahead, I anticipate that the integration of single-cell views of the transcriptome, epigenome, proteome, and spatial-temporal information throughout development will enable an increasingly complete view of how life is formed. GRAND PRIZE WINNER Junyue Cao Junyue Cao received his undergraduate degree from Peking University and a Ph.D. from the University of Washington. After completing his postdoctoral fellowship at the University of Washington, Junyue Cao started his lab as an assistant professor and lab head of single-cell genomics and population dynamics at the Rockefeller University in 2020. His current research focuses on studying how a cell population in our body maintains homeostasis by developing genomic techniques to profile and perturb cell dynamics at single-cell resolution. CATEGORY WINNER: ECOLOGY AND EVOLUTION Orsi Decker Orsi Decker completed her undergraduate degree at Eötvös Loránd University in Budapest, Hungary. She went on to receive her master's degree in Ecology and Evolution at the University of Amsterdam. Decker completed her doctoral research at La Trobe University in Melbourne, Australia, where she investigated the extinctions of native digging mammals and their context-dependent impacts on soil processes. She is currently a postdoctoral researcher at La Trobe University, where she is examining how land restoration efforts could be improved to regain soil functions through the introduction of soil fauna to degraded areas. [www.sciencemag.org/content/370/6519/925.1][11] CATEGORY WINNER: MOLECULAR MEDICINE Dasha Nelidova Dasha Nelidova completed her undergraduate degrees at the University of Auckland, New Zealand. She completed her Ph.D. in neurobiology at the Friedrich Miescher Institute for Biomedical Research in Basel, Switzerland. Nelidova is currently a postdoctoral researcher at the Institute of Molecular and Clinical Ophthalmology Basel, where she is working to develop new translational technologies for treating retinal diseases that lead to blindness. [www.sciencemag.org/content/370/6519/925.2][12] CATEGORY WINNER: CELL AND MOLECULAR BIOLOGY William E. Allen William E. Allen received his undergraduate degree from Brown University in 2012, M.Phil. in Computational Biology from the University of Cambridge in 2013, and Ph.D. in Neurosciences from Stanford University in 2019. At Stanford, he worked to develop new tools for the large-scale characterization of neural circuit structure and function, which he applied to understand the neural basis of thirst. After completing his Ph.D., William started as an independent Junior Fellow in the Society of Fellows at Harvard University, where he is developing and applying new approaches to map mammalian brain function and dysfunction over an animal's life span. [www.sciencemag.org/content/370/6519/925.3][13] 1. [↵][14]1. D. Ramsköld et al ., Nat. Biotechnol. 30, 777 (2012). [OpenUrl][15][CrossRef][16][PubMed][17] 2. [↵][18]1. C. Trapnell , Genome Res. 25, 1491 (2015). [OpenUrl][19][Abstract/FREE Full Text][20] 3. [↵][21]1. D. E. Wagner et al ., Science 360, 981 (2018). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. K. Davie et al ., Cell 174, 982 (2018). [OpenUrl][25][CrossRef][26][PubMed][27] 5. [↵][28]1. J. Cao et al ., Science 357, 661 (2017). [OpenUrl][29][Abstract/FREE Full Text][30] 6. [↵][31]1. J. Cao et al ., Science 361, 1380 (2018). [OpenUrl][32][Abstract/FREE Full Text][33] 7. [↵][34]1. J. Cao et al ., Nature 566, 496 (2019). [OpenUrl][35][CrossRef][36][PubMed][37] 8. [↵][38]1. J. Cao, 2. W. Zhou, 3. F. Steemers, 4. C. Trapnell, 5. J. Shendure , Nat. Biotechnol. 38, 980 (2020). [OpenUrl][39][CrossRef][40][PubMed][41] 9. [↵][42]1. D. A. Cusanovich et al ., Science 348, 910 (2015). [OpenUrl][43][Abstract/FREE Full Text][44] [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: pending:yes [6]: #ref-7 [7]: #ref-5 [8]: #ref-8 [9]: #ref-9 [10]: #ref-6 [11]: http://www.sciencemag.org/content/370/6519/925.1 [12]: http://www.sciencemag.org/content/370/6519/925.2 [13]: http://www.sciencemag.org/content/370/6519/925.3 [14]: #xref-ref-1-1 "View reference 1 in text" [15]: {openurl}?query=rft.jtitle%253DNature%2Bbiotechnology%26rft.stitle%253DNat%2BBiotechnol%26rft.aulast%253DRamskold%26rft.auinit1%253DD.%26rft.volume%253D30%26rft.issue%253D8%26rft.spage%253D777%26rft.epage%253D782%26rft.atitle%253DFull-length%2BmRNA-Seq%2Bfrom%2Bsingle-cell%2Blevels%2Bof%2BRNA%2Band%2Bindividual%2Bcirculating%2Btumor%2Bcells.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnbt.2282%26rft_id%253Dinfo%253Apmid%252F22820318%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [16]: /lookup/external-ref?access_num=10.1038/nbt.2282&link_type=DOI [17]: /lookup/external-ref?access_num=22820318&link_type=MED&atom=%2Fsci%2F370%2F6519%2F924.atom [18]: #xref-ref-2-1 "View reference 2 in text" [19]: {openurl}?query=rft.jtitle%253DGenome%2BRes.%26rft_id%253Dinfo%253Adoi%252F10.1101%252Fgr.190595.115%26rft_id%253Dinfo%253Apmid%252F26430159%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NjoiZ2Vub21lIjtzOjU6InJlc2lkIjtzOjEwOiIyNS8xMC8xNDkxIjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzcwLzY1MTkvOTI0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [21]: #xref-ref-3-1 "View reference 3 in text" [22]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aar4362%26rft_id%253Dinfo%253Apmid%252F29700229%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNjAvNjM5Mi85ODEiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxOS85MjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [24]: #xref-ref-4-1 "View reference 4 in text" [25]: {openurl}?query=rft.jtitle%253DCell%26rft.volume%253D174%26rft.spage%253D982%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.cell.2018.05.057%26rft_id%253Dinfo%253Apmid%252F29909982%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [26]: /lookup/external-ref?access_num=10.1016/j.cell.2018.05.057&link_type=DOI [27]: /lookup/external-ref?access_num=29909982&link_type=MED&atom=%2Fsci%2F370%2F6519%2F924.atom [28]: #xref-ref-5-1 "View reference 5 in text" [29]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DCao%26rft.auinit1%253DJ.%26rft.volume%253D357%26rft.issue%253D6352%26rft.spage%253D661%26rft.epage%253D667%26rft.atitle%253DComprehensive%2Bsingle-cell%2Btranscriptional%2Bprofiling%2Bof%2Ba%2Bmulticellular%2Borganism%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aam8940%26rft_id%253Dinfo%253Apmid%252F28818938%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [30]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNTcvNjM1Mi82NjEiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxOS85MjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [31]: #xref-ref-6-1 "View reference 6 in text" [32]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aau0730%26rft_id%253Dinfo%253Apmid%252F30166440%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [33]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIzNjEvNjQwOS8xMzgwIjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzcwLzY1MTkvOTI0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [34]: #xref-ref-7-1 "View reference 7 in text" [35]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D566%26rft.spage%253D496%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41586-019-0969-x%26rft_id%253Dinfo%253Apmid%252F30787437%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [36]: /lookup/external-ref?access_num=10.1038/s41586-019-0969-x&link_type=DOI [37]: /lookup/external-ref?access_num=30787437&link_type=MED&atom=%2Fsci%2F370%2F6519%2F924.atom [38]: #xref-ref-8-1 "View reference 8 in text" [39]: {openurl}?query=rft.jtitle%253DNat.%2BBiotechnol.%26rft.volume%253D38%26rft.spage%253D980%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41587-020-0480-9%26rft_id%253Dinfo%253Apmid%252F32284584%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [40]: /lookup/external-ref?access_num=10.1038/s41587-020-0480-9&link_type=DOI [41]: /lookup/external-ref?access_num=32284584&link_type=MED&atom=%2Fsci%2F370%2F6519%2F924.atom [42]: #xref-ref-9-1 "View reference 9 in text" [43]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aab1601%26rft_id%253Dinfo%253Apmid%252F25953818%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [44]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNDgvNjIzNy85MTAiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxOS85MjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9

In this thirteenth episode, Tom Stubbs, Co-Founder/CEO of Chronomics starts with introducing epigenetics. He describes the technology and expertise that he's brought together to create the only company in the world advancing the forefront of epigenetic biomarkers. He explains how their A.I. based health biomarker engine will be used to reduce your risk of future illness. Thank you for having me on the show. Pleasure to be here and looking forward to chatting with you. Tom: We are very much focused on measuring health so people can avoid disease. Lee: Measuring health so that people can avoid disease, that sounds a little bit cryptic. I mean, essentially we're focused on providing people with objective measures that capture the broader definition of health. So not merely health being the absence of disease, but actually as defined by the World Health Organization over 70 years ago, health being the complete physical, mental and social wellbeing of a person. And we think that this is super important, because with the rise of aging populations and the growth in chronic conditions globally, such as heart disease and type two diabetes, there's a growing need for healthcare to shift towards prevention. And to enable this shift, we need measures to capture the largest risk factors for these conditions ahead of time so that people can prevent through action. Lee: So I think I was one of the first users of Chronomics. I had contacted yourselves at the end of 2018 and took a whole genome sequence and an epigenetic test. We first were putting the product out 2018, and yes, you were among one of the first users of the product. Pleasure to have had you and still have you as a customer, Lee. Lee: And I remember yourselves very favorably, because I was a little bit skeptical because Tommy Woods had informed me that the business model of quite a few companies in the OMIC space is to give you a large questionnaire, apply AI to it, and I've had it demonstrated now to me that based on a simple questionnaire, AI can derive a lot of information about you on the health front, predictive, way more than the OMICS can in some cases. And these companies are doing this heavy OMICS data acquisition, not so much to give you data at the moment, I mean, information, but in order that may be in 5, 10 years, that vast sum of data that can then do something with. And so, I was skeptical at Chronomics maybe doing that, and I said, please make a special case for me. Give me my results without the questionnaire. Tom: Yeah, I do remember this, Lee. And then I said, hey look, if I'm doing a whole genome sequence, I actually want a copy of it. So send me every letter.

On paper alone you would never guess that I grew up poor and hungry. My most recent annual salary was over $700,000. I am a Truman National Security Fellow and a term member at the Council on Foreign Relations. My publisher has just released my latest book series on quantitative finance in worldwide distribution. None of it feels like enough. I feel as though I am wired for a permanent state of fight or flight, waiting for the other shoe to drop, or the metaphorical week when I don't eat. I've chosen not to have children, partly because--despite any success--I still don't feel I have a safety net. I have a huge minimum checking account balance in mind before I would ever consider having children. If you knew me personally, you might get glimpses of stress, self-doubt, anxiety, and depression.

A'new deep learning' method, DeepCpG, designed by researchers at EMBL-EBI, the Babraham Institute and the Sanger Institute helps scientists better understand the epigenome – the biochemical activity around the genome. Published in Genome Biology, DeepCpG leverages'deep neural networks', a multi-layered machine learning model inspired by the brain. Machine learning provides a valuable tool for research into health and disease. Deep learning is one of the most active fields in machine learning, which has led to recent advancements in computer image classification, text translation and speech recognition. But deep learning also has major potential in computational biology, particularly for regulatory genomics and cellular imaging. "We now have this amazing'book' of the human genome, thanks to projects like 1000 Genomes, divided up nicely into chapters and annotated in parts.

On paper alone you would never guess that I grew up poor and hungry. My most recent annual salary was over $700,000. I am a Truman National Security Fellow and a term member at the Council on Foreign Relations. My publisher has just released my latest book series on quantitative finance in worldwide distribution. None of it feels like enough though. I feel as though I am wired for a permanent state of flight or fight, waiting for the other shoe to drop, or the metaphorical week when I don't eat. I've chosen not to have children, partly because--despite any success--I still don't feel I have a safety net. I have a huge minimum checking account balance in mind before I would ever consider having children. If you knew me personally, you might get glimpses of stress, self-doubt, anxiety, and depression.

On a velvety March evening in Mandeville Canyon, high above the rest of Los Angeles, Norman Lear's living room was jammed with powerful people eager to learn the secrets of longevity. When the symposium's first speaker asked how many people there wanted to live to two hundred, if they could remain healthy, almost every hand went up. The venture capitalists were keeping slim to maintain their imposing vitality, the scientists were keeping slim because they'd read--and in some cases done--the research on caloric restriction, and the Hollywood stars were keeping slim because of course. When Liz Blackburn, who won a Nobel Prize for her work in genetics, took questions, Goldie Hawn, regal on a comfy sofa, purred, "I have a question about the mitochondria. I've been told about a molecule called glutathione that helps the health of the cell?" Glutathione is a powerful antioxidant that protects cells and their mitochondria, which provide energy; some in Hollywood call it "the God molecule." But taken in excess it can muffle a number of bodily repair mechanisms, leading to liver and kidney problems or even the rapid and potentially fatal sloughing of your skin. Blackburn gently suggested that a varied, healthy diet was best, and that no single molecule was the answer to the puzzle of aging. Yet the premise of the evening was that answers, and maybe even an encompassing solution, were just around the corner. The party was the kickoff event for the National Academy of Medicine's Grand Challenge in Healthy Longevity, which will award at least twenty-five million dollars for breakthroughs in the field. Victor Dzau, the academy's president, stood to acknowledge several of the scientists in the room. He praised their work with enzymes that help regulate aging; with teasing out genes that control life span in various dog breeds; and with a technique by which an old mouse is surgically connected to a young mouse, shares its blood, and within weeks becomes younger. Joon Yun, a doctor who runs a health-care hedge fund, announced that he and his wife had given the first two million dollars toward funding the challenge. "I have the idea that aging is plastic, that it's encoded," he said. "If something is encoded, you can crack the code." To growing applause, he went on, "If you can crack the code, you can hack the code!" It's a big ask: more than a hundred and fifty thousand people die every day, the majority of aging-related diseases. Yet Yun believes, he told me, that if we hack the code correctly, "thermodynamically, there should be no reason we can't defer entropy indefinitely. We can end aging forever." Nicole Shanahan, the founder of a patent-management business, announced that her company would oversee longevity-related patents that Yun had pledged to the cause.

Thuy Truong thought her aching back was just a pulled muscle from working out. But then came a high fever that wouldn't go away during a visit to Vietnam. When a friend insisted Truong, 30, go to an emergency room, doctors told her the last thing she expected to hear: She had lung cancer. Back in Los Angeles, Truong learned the cancer was at stage 4 and she had about eight months to live. "My whole world was flipped upside down," says Truong, who had been splitting her time between the San Francisco Bay Area and Asia for a new project after selling her startup.

The sequencing of the human genome laid the foundation for the study of genetic variation and its links to a wide range of diseases. But the genome itself is only part of the story, as genes can be switched on and off by a range of chemical modifications, known as "epigenetic marks." Now, a decade after the human genome was sequenced, the National Institutes of Health's Roadmap Epigenomics Consortium has created a similar map of the human epigenome. Manolis Kellis, a professor of computer science and a member of MIT's Computer Science and Artificial Intelligence Laboratory and of the Broad Institute, led the effort to integrate and analyze the datasets produced by the project, which constitute the most comprehensive view of the human epigenome to date. In a paper published today in the journal Nature, Kellis and his colleagues report 111 reference human epigenomes and study their regulatory circuitry, in a bid to understand their role in human traits and diseases.