Collaborating Authors


Unlocking the Mysteries of the Brain With AutoML


The phrase "it's not rocket science" harkens to the wild complexity of rocket building, with millions of pieces and as many opportunities to make errors. That being said, the brain has nearly 100 billion neurons, each of which acts like a "mini-computer." Not all neurons are inter-connected, but there are still around 100 trillion connections. How could we ever understand such a complex computer? Well, humans have been trying for thousands of years, but a "brain code" is yet to be discovered.

Tempus fugit: How time flies during development


“Fugit irreparabile tempus,” wrote Virgil, a reminder that our lives are defined by the irreversible flow of time. As soon as the egg is fertilized, embryonic cells follow a developmental program strictly organized in time. The sequence typically is conserved throughout evolution, but individual events can occur over species-specific time scales. Such differences can have marked effects. For instance, it takes 3 months to generate cerebral cortex neurons in a human but only 1 week in a mouse. This prolonged neurogenesis likely contributes to evolutionary expansion of the human brain ([ 1 ][1]). But the mechanisms underlying developmental time scales remain largely unknown. On pages 1449 and 1450 of this issue, Rayon et al. ([ 2 ][2]) and Matsuda et al. ([ 3 ][3]), respectively, report an association between species-specific developmental time scales and the speed of biochemical reactions that support protein turnover. Cell differentiation during mammalian development uses two types of timing mechanisms (biological clocks) based on oscillations or unidirectional processes (hourglass clocks). Modeling development in pluripotent stem cells (PSCs) from various species shows that the pace of differentiation of many cell types in an in vitro setting largely recapitulates the species-specific timing observed in embryos ([ 4 ][4], [ 5 ][5]). Even when human neurons are transplanted as single cells in a mouse brain, they follow their own prolonged developmental timeline ([ 6 ][6]). This suggests that cell-intrinsic mechanisms, yet to be discovered, dictate the timing of developmental trajectories in a species-specific manner. Matsuda et al. examined a biological rhythm typical of vertebrate embryos: the “somite segmentation clock,” by which the body is built segment (or somite) by segment, thanks to waves of expression of specific genes (oscillations) in presomitic mesodermal (PSM) cells. Using in vitro modeling with mouse and human PSCs, the authors examined waves of expression of HES7 (hes family bHLH transcription factor 7), a segmental-clock master gene. They found similar waves in PSM cells of both species, but the period of oscillations in human cells was ∼5 hours instead of 2 hours (as in mouse cells), consistent with another recent report ([ 7 ][7]). What might underlie such cell-intrinsic differences? Evolutionary divergence in developmental processes usually occurs as a result of changes in the gene regulatory networks (GRNs) that control them ([ 8 ][8]). The authors examined the GRN of segmental oscillations, and except for the period of oscillation, they found no obvious difference between human and mouse gene expression. They then swapped the mouse and human genome sequences containing the HES7 locus. The human HES7 gene transplanted in mouse cells displayed fast oscillations like the mouse gene, whereas the mouse gene transplanted in the human cells displayed slower, human-like oscillations (see the figure). Thus, even DNA cis-regulatory components of the GRN do not appear to dictate the time scale of HES7 oscillations. However, Matsuda et al. found important species-specific differences in a different mechanism: the speed of biochemical reactions leading to protein turnover (production and decay). Human cells displayed slower kinetics of protein expression (including “expression delays” related to RNA transcription, splicing, and translation) and a slower rate of protein decay, mostly related to degradation. Many examined parameters showed a twofold difference in mouse versus human cells, matching the time differences observed for the segmentation clock. ![Figure][9] Same events, distinct timingGRAPHIC: KELLIE HOLOSKI/ SCIENCE Rather than being dominated by clocklike oscillations, the developmental process is specified mostly by cell-fate transitions, by which embryonic cells gradually an d irreversibly become differentiated cells. Could it be that similar mechanisms regulate these hourglass-like timing events as well? Rayon et al. explored this notion using a motor neuron (MN) developmental model from mouse and human PSCs. Examination of MN development in vitro revealed that the underlying GRN is similar in both species, except that human motoneurogenesis takes 2.5 times longer in the human cell model versus the mouse. The authors then examined the influence of sonic hedgehog, the key morphogen that induces MN fate (by changing timing and intensity of the signal), and the MN-development master gene OLIG2 (oligodendrocyte transcription factor 2) (by inserting the human gene in mouse cells) but found no effects that explained the species-specific time differences. They then analyzed protein stability during MN development and found that the mean protein half-life was doubled in human cells compared with mouse cells, which is consistent with the findings of Matsuda et al. Both studies point to protein turnover as a potential source of variation in developmental time scales. Each group tested this hypothesis further by in silico modeling of their experimental systems, which predicted, in each case, a prominent influence of the delay in protein production and protein decay on developmental time scales. That protein turnover affects the timing of development is provocative and attractive but must be validated by experimental evidence for causal relationship between the two (by altering the production and decay of proteins and mRNA, and then examining the developmental time scale). Such experiments will also help to determine the respective contributions of expression delay versus protein decay, on which each study puts a somewhat different emphasis. The consistent results from both studies also raise questions about the mechanisms upstream of interspecies differences in protein turnover. Metabolism is an attractive candidate. Protein turnover requires a considerable amount of energy ([ 9 ][10]), and metabolic rewiring has emerged as a central instructor of cell fate transitions ([ 10 ][11]), although through epigenetic remodeling rather than changes in proteostasis. Another question is whether the same principles apply to developmental events that display more pronounced time scale differences. For example, GRN divergence might operate through specific genes that modulate the timing of human cortical neurogenesis ([ 11 ][12]). Furthermore, metabolism and protein turnover might display differences depending on the cell context or the specific protein involved. And known correlations between developmental timing, life span, and aging across species ([ 12 ][13]) might all be causally linked to differences in metabolism and protein turnover. 1. [↵][14]1. A. M. M. Sousa et al ., Cell 170, 226 (2017). [OpenUrl][15][CrossRef][16][PubMed][17] 2. [↵][18]1. T. Rayon et al ., Science 369, eaba7667 (2020). [OpenUrl][19][Abstract/FREE Full Text][20] 3. [↵][21]1. M. Matsuda et al ., Science 369, 1450 (2020). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. J. van den Ameelen et al ., Trends Neurosci. 37, 334 (2014). [OpenUrl][25][CrossRef][26][PubMed][27] 5. [↵][28]1. M. Ebisuya, 2. J. Briscoe , Development 145, dev164368 (2018). [OpenUrl][29][Abstract/FREE Full Text][30] 6. [↵][31]1. D. Linaro et al ., Neuron 104, 972 (2019). [OpenUrl][32] 7. [↵][33]1. M. Diaz-Cuadros et al ., Nature 580, 113 (2020). [OpenUrl][34][CrossRef][35][PubMed][36] 8. [↵][37]1. E. H. Davidson, 2. D. H. Erwin , Science 311, 796 (2006). [OpenUrl][38][Abstract/FREE Full Text][39] 9. [↵][40]1. J. Labbadia, 2. R. I. Morimoto , Annu. Rev. Biochem. 84, 435 (2015). [OpenUrl][41][CrossRef][42][PubMed][43] 10. [↵][44]1. N. Shyh-Chang et al ., Development 140, 2535 (2013). [OpenUrl][45][Abstract/FREE Full Text][46] 11. [↵][47]1. I. K. Suzuki et al ., Cell 173, 1370 (2018). [OpenUrl][48][CrossRef][49][PubMed][50] 12. [↵][51]1. A. A. Fushan et al ., Aging Cell 14, 352 (2015). [OpenUrl][52][CrossRef][53][PubMed][54] Acknowledgments: P.V. is funded by the European Research Council, Belgian Fonds Wetenschappelijk Onderzoek, Excellence of Science Research programme, AXA Research Fund, Belgian Queen Elizabeth Foundation, and Fondation Université Libre de Bruxelles. R.I. was supported by the Belgian Fonds de la Recherche Scientifique. 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Epigenetic Health Monitoring to Reduce Your Future Illness Risk – EP13: Tom Stubbs (Chronomics) – Hyper Wellbeing Innovation Labs, Inc. Blog


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.

SeekACE - Home Page: IT Development


Hi everyone, I am Rajkiran, General Manager, SeekACE Solutions, today we will be discussing about application of Data Science in Genomics and Drug Discovery. Let me start by defining Genomics. It is the study of sequencing and analysis of genomes. In simple language, a genome consists of the DNA and all the genes of any organisms. After, the compilation of the Human Genome Project, the research has been advancing rapidly and it has inculcated itself in the realms of big data and data science.

Daily Digest March 12, 2020 – BioDecoded


Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. Researchers used machine learning to develop an early-warning system that integrates measurements from multiple organ systems using a high-resolution database with 240 patient-years of data. It predicts 90% of circulatory-failure events in the test set, with 82% identified more than 2 h in advance, resulting in an area under the receiver operating characteristic curve of 0.94 and an area under the precision-recall curve of 0.63. ProteoClade is a Python toolkit that performs taxa-specific peptide assignment, protein inference, and quantitation for multi-species proteomics experiments. ProteoClade scales to hundreds of millions of protein sequences, requires minimal computational resources, and is open source, multi-platform, and accessible to non-programmers.

Nonparametric regression and classification with joint sparsity constraints

Neural Information Processing Systems

We propose new families of models and algorithms for high-dimensional nonparametric learning with joint sparsity constraints. Our approach is based on a regularization method that enforces common sparsity patterns across different function components in a nonparametric additive model. The algorithms employ a coordinate descent approach that is based on a functional soft-thresholding operator. The methods are illustrated with experiments on synthetic data and gene microarray data. Papers published at the Neural Information Processing Systems Conference.

6 expert essays on the future of biotech


What exactly is biotechnology, and how could it change our approach to human health? As the age of big data transforms the potential of this emerging field, members of the World Economic Forum's Global Future Council on Biotechnology tell you everything you need to know. What if your doctor could predict your heart attack before you had it – and prevent it? Or what if we could cure a child's cancer by exploiting the bacteria in their gut? These types of biotechnology solutions aimed at improving human health are already being explored. As more and more data (so called "big data") is available across disparate domains such as electronic health records, genomics, metabolomics, and even life-style information, further insights and opportunities for biotechnology will become apparent. However, to achieve the maximal potential both technical and ethical issues will need to be addressed. As we look to the future, let's first revisit previous examples of where combining data with scientific understanding has led to new health solutions. Biotechnology is a rapidly changing field that continues to transform both in scope and impact. Karl Ereky first coined the term biotechnology in 1919.

Industry News


Find here a listing of the latest industry news in genomics, genetics, precision medicine, and beyond. Updates are provided on a monthly basis. Sign-Up for our newsletter and never miss out on the latest news and updates. As 2019 came to an end, Veritas Genetics struggled to get funding due to concerns it had previously taken money from China. It was forced to cease US operations and is in talks with potential buyers. The GenomeAsia 100K Project announced its pilot phase with hopes to tackle the underrepresentation of non-Europeans in human genetic studies and enable genetic discoveries across Asia. Veritas Genetics, the start-up that can sequence a human genome for less than $600, ceases US operations and is in talks with potential buyers Veritas Genetics ceases US operations but will continue Veritas Europe and Latin America. It had trouble raising funding due to previous China investments and is looking to be acquired. Illumina loses DNA sequencing patents The European Patent ...

We're on the verge of AI developed drugs becoming a reality


The hope of The Human Genome Project was that it would herald a new age of precision medicine. However, the challenge turned out to be more complex and nuanced than had been imagined. Of nearly 25,000 human genes, only 2,418 have been associated with specific diseases, explaining only a small fraction of all human pathologies. In 2020, we will begin to harness the power of artificial intelligence (AI) to create new, life-saving medicine. In the past decade, we have learned a great deal about the complexity of diseases.

Two Postdoc positions (m/f/d) in 'Computational proteomics/deep


The Matthias Mann lab at the Max Planck Institute of Biochemistry is a leader in the field of mass spectrometry-based proteomics and has pushed the development and application of this technology for over two decades. The Fabian Theis lab at the Helmholtz Center Munich has a long-standing reputation for pioneering machine learning and AI methods in molecular biology, in particular on single-cell genomics and microscopy. They have recently joined forces in a project to develop novel deep learning techniques for peptide analysis and predictions on multiple levels, which potentially revolutionizes proteomic workflows in terms of accuracy and efficiency. Together the Theis and Mann labs are looking for two highly motivated postdoc candidates for working in a team that will combine newest developments in both Machine Learning and proteomics. This technology will be applied to the diagnosis and prognosis of disease on the basis of MS-based proteomics.