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Tag: Genomics
Handbook of Machine Learning Applications for Genomics, Hardcover by Roy, San…
Handbook of Machine Learning Applications for Genomics, Hardcover by Roy, San…
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Are you interested in the intersections of machine learning and genomics? Look no further than the comprehensive Handbook of Machine Learning Applications for Genomics, Hardcover by Roy, San.This essential guide delves into the cutting-edge applications of machine learning in genomics, offering insights and strategies for leveraging these technologies to advance research in the field. From analyzing large-scale genomic data to predicting gene functions, this book covers a wide range of topics that are crucial for understanding the complex relationship between machine learning and genomics.
Written by renowned expert San Roy, this handbook is a must-have resource for researchers, practitioners, and students alike who are interested in harnessing the power of machine learning for genomics. With its in-depth analysis and practical examples, this book is sure to become a valuable reference for anyone looking to explore the exciting possibilities at the intersection of machine learning and genomics.
#Handbook #Machine #Learning #Applications #Genomics #Hardcover #Roy #San..,dnnGenomics yields biological and phenotypic insights into bipolar disorder
The management group for this paper was led by O.A.A. The management group comprised a subset of authors responsible for the study design, conduct, primary and final interpretation, and included A.M., A.J.F., N.M., A.D.F., R.A.O., H.J.E., K.S.O. and O.A.A; this group was also responsible for primary drafting and editing of the manuscript. The analytical team, led by K.S.O., was responsible for the main analyses presented in the paper, and included M. Koromina, T.v.d.V., T.B., F.S.D., J.M.K.Y., K.-H.L., X.W., J.R.I.C., B.L.M., C.C.M, A.V.R., P.A.L., E. Koch, A. Harder, N.P., J.B. and K.S.O. Imputation, quality control and GWAS were conducted by K.S.O., M. Koromina, B.L.M., K.-H.L., X.W. and J.M.K.Y. Heritability and genetic correlation analyses were performed by K.S.O. MiXeR was done by K.S.O. and A. Shadrin. Polygenic association was conducted by T.v.d.V., T.B., B.L.M. and P.A.L. Gene and gene set analyses were done by K.S.O., C.C.M. and A.V.R. Cell-type-specific analyses were performed by F.S.D. Single-nucleus RNA sequencing enrichment was done by A. Harder and J.H.-L. Fine-mapping was conducted by M. Koromina. Rare variant analyses were performed by C. Liao. QTL integrative analyses were done by M. Koromina, T.B. and F.S.D. Enhancer–promoter interactions were analysed by J.B. Credible gene prioritization was performed by K.S.O. and M. Koromina. Temporal clustering was done by N.P. Drug enrichment was conducted by J.R.I.C. and E. Koch. Clinical assessments were performed by A.A., A.C., A.C.-B., A.D.B., A.D.F., A.E.V., A.H.Y., A. Havdahl, A.M., A.M.M., A. Perry, A. Pfennig, A.R., A. Serretti, A.V., B. Carpiniello, B.E., B.S., B.T.B., C.A.M., C. Lavebratt, C. Loughland, C.N.P., C.O., C.S., D.D., D.H., D.J.M., D. J. Smith, D.M., D.Q., E.C.S., E.E.T., E.J.R., E. Kim, E. Sigurdsson, E.S.G., E. Stordal, E.V., E.Z.R., F. Senner, F.S.G., F. Stein, F. Streit, F.T.F., G.B., G.K., G.M., H.A., H.-J.L., H.M., H.V., H.Y.P., I.D.W., I.J., I.M., I.R.G., J.A.R.-Q., J.B.P., J.B.V., J.G.-P., J. Garnham, J. Grove, J.H., J.I.N., J. L. Kalman, J. L. Kennedy, J.L.S., J. Lawrence, J. Lissowska, J.M.P., J.P.R., J.R.D., J.W.S., K.A., K.D., K.G.-S., K.J.O., L.A.J., L.B., L.F., L. Martinsson, L. Sirignano, L.T., L.Z., M.A., M. Bauer, M. Brum, M. Budde, M.C., M.C.O., M.F., M.G., M.G.-S., M.G.M., M.H.R., M. Haraldsson, M. Hautzinger, M.I., M.J.G., M.J.O., M. Kogevinas, M. Landén, M. Lundberg, M. Manchia, M. Mattheisen, M.P.B., M.P.V., M. Rietschel, M. Tesfaye, M.T.P., M. Tesli, N.A.-R., N.B., N.B.-K., N.C., N.D., N.G.M., N.I., O.A.A., O.B.S., O.K.D., O.M., P. B. Mitchell, P. B. Mortensen, P.C., P.F., P.M.C., R.A., R.B., R.S.K., S.A.K., S. Bengeser, S.K.-S., S.L., S.L.M., S.P., T.G.S., T.H., T.H.H., T.K., T.M.K., T.O., T.S., T.W., T.W.W., U.D., U.H., V.M., W.B., W. Maier and W. Myung. Data processing and analyses were performed by A.C., A.D.B., A.F.P., A. Harder, A.J.F., A.M.D., A. Shadrin, A.V.R., A.X.M., B. Coombes, B.L.M., B.M.-M., B.M.B., B.S.W., C.B.P., C. Cruceanu, C.C.M., C. Chatzinakos, C. Liao, C.M.N., C.S.W., C. Terao, C. Toma, D.A., D.M.H., D.W.M., E.A., E.A.S., E.C.B., E.C.C., E. Koch, E.M., E.S.G., F.D., F.J.M., F.S.D., G.A.R., G.B., G.P., G.T., H.-C.C., H. Stefansson, H. Sung, H.-H.W., I.C., J.B., J.C.-D., J.D.M., J.F., J.F.F., J.G.T., J. Grove, J.H.-L., J.K., J.M.B., J.M.F., J.M.K.Y., J.R.I.C., J.S.J., J.T.R.W., K.K., K.-H.L., K.S.O., L.G.S., L.J., L. Milani, L. Sindermann, M.-C.H., M.I., M.J.C., M. Koromina, M. Leber, M.M.N., M. Mattheisen, M. Ribasés, M. Rivera, M.S.A., M.S., M. Tesfaye, N.B.F., N.I., N.M., N.P., N.W.M., O.B.S., O.F., O.K.D., P.A.H., P.A.L., P.A.T., P.D.S., P.F.S., P.H., P.-H.K., P.M., P.P.Z., P.R., Q.S.L., R.J.S., R.M.M., R.Y., S.A., S. Børte, S. Cichon, S.D., S.D.G., S.E.M., S.H., S.H.W., S.J., S.R., S.-J.T., T.A.G., T.B., T.B.B., T.C., T.D.A., T.E.T., T.F.M.A., T.O., T.S., T.v.d.V., T.W., T.W.M., V.E.-P., W. Myung, X.W. and Y.K. Funding was obtained by A.C., A.D.B., A.H.Y., A.M.M., B.E., B.M.N., B.T.B., C.N.P., C. Pantelis, C.S.W., C. Terao, D. J. Stein, D.M., D.S., E.S.G., F.B., F.J.M., G.A.R., G.B., G.P.P., G.T., H.J.E., I.B.H., I.J., I.M., I.N.F., J.A.K., J.B.P., J.B.V., J.I.N., J.M.B., J.M.F., J.R.D., J.W.S., K.H., L.A., L.A.J., L.B., M.A., M. Boehnke, M.C.O., M.F., M.G.-S., M.H.R., M.I., M.J.G., M.J.O., M. Leboyer, M. Landén, M.M.N., M.N., M. Rietschel, M.S., M.T.P., N.C., N.G.M., N.I., O.A.A., O.M., P.A.T., P. B. Mitchell, P. B. Mortensen, P.P.Z., P.R.S., R.A.O., R.J.S., R.M.M., S.E.M., S.J., S.L., T.B.B., T.G.S., T.O., T.S., T.W., T.W.W., W.H.B. and Y.K. Recruitment and genotyping were performed by A.C., A.D.B., A.D.F., A.E.V., A.H.F., A.J.F., A.M.M., A.M., A.R., A. Serretti, A. Squassina, B. Carpiniello, B.-C.L., B.E., B.M.-M., B.M.N., B.T.B., C.A.M., C.B.P., C. Lochner, C.M.N., C.M.O., C.N.P., C. Pantelis, C. Pisanu, C.S.W., D.C.W., D.D., D.J.K., E.A., E.S.G., E. Stordal, E.V., E.Z.R., F.A.H., F.B., F.J.M., F.M., F.S.G., F. Stein, G.A.R., G.B., G.D.H., G.M., G.P.P., G.T., H.J.E., H. Stefansson, H.-H.W., I.B.H., I.D.W., I.J., J.A.R.-Q., J.B.V., J.H., J.H.K., J.H.-L., J.I.N., J.J.L., J. Lissowska, J.M.B., J.M.F., J.M.P., J.R.D., J.R.K., J.-W.K., J.W.S., J.-A.Z., K.H., K.J.O., K.S., L.A., L.A.J., L.J.S., L. Milani, L.T., M.A., M. Aslan, M.C.O., M.F., M.G., M.G.-S., M.I., M.J.C., M.J.G., M.J.O., M. Leboyer, M. Landén, M.M.N., M. Manchia, M.N., M. Ribasés, M. Rietschel, M.S., M.T.P., N.C., N.G.M., N.I., O.A.A., O.M., P.A.L., P. B. Mitchell, P. B. Mortensen, P.D.H., P.F., P.-H.K., P.R., P.R.S., Q.S.L., R.A., R.A.O., R.S.K., S.A.P., S. Bengesser, S. Cichon, S. Catts, S.E.M., S.L.M., S.R., T.G.S., T.H., T.K., T.S., T.W., T.W.W., U.D., U.S., V.J.C., W.H.B., W. Myung and Y.K.L. Numerous authors beyond the initial writing group contributed to data interpretation and provided edits, comments and suggestions to the paper. All authors reviewed the manuscript critically for important intellectual content and approved the final version of the manuscript for publication. The Chair of the PGC is P.F.S. The Bipolar Disorder Working Group of the PGC is led by O.A.A.
Bipolar disorder is a complex mental health condition that affects millions of people worldwide. While the exact causes of bipolar disorder are still not fully understood, recent advancements in genomics have provided valuable insights into the biological and phenotypic aspects of this disorder.Genomics, the study of an organism’s complete set of DNA, has allowed researchers to identify genetic variations that may play a role in the development of bipolar disorder. By analyzing the genomes of individuals with bipolar disorder, scientists have been able to pinpoint specific genes and genetic pathways that are associated with the disorder.
One of the key findings from genomic studies of bipolar disorder is the involvement of genes related to neurotransmitter signaling, synaptic function, and circadian rhythms. These genes are known to play a crucial role in regulating mood, behavior, and sleep patterns, all of which are disrupted in individuals with bipolar disorder.
In addition to identifying genetic factors, genomics has also shed light on the phenotypic aspects of bipolar disorder. By studying the patterns of gene expression and protein activity in individuals with bipolar disorder, researchers have been able to uncover the underlying biological mechanisms that contribute to the disorder’s symptoms.
Furthermore, genomics has enabled the development of personalized treatment approaches for individuals with bipolar disorder. By identifying genetic variations that may influence a person’s response to medications, doctors can tailor treatment plans to better suit each individual’s unique genetic makeup.
Overall, genomics has significantly advanced our understanding of bipolar disorder, providing valuable insights into its biological and phenotypic underpinnings. By continuing to study the genetic basis of bipolar disorder, researchers hope to uncover new therapeutic targets and improve treatment outcomes for individuals with this challenging condition.
Tags:
- Genomics
- Bipolar disorder
- Biological insights
- Phenotypic insights
- Mental health
- Genetic research
- Psychiatry
- Mood disorders
- Genetic factors
- Bipolar disorder research
#Genomics #yields #biological #phenotypic #insights #bipolar #disorder
SNPweb: Bridging the Gap Between Genomics and Clinical Applications
SNPweb: Bridging the Gap Between Genomics and Clinical ApplicationsIn recent years, advancements in genomics have revolutionized the field of medicine, offering unprecedented insights into the genetic basis of diseases and potential treatment options. However, translating these genomic discoveries into clinical practice has been a significant challenge. This is where SNPweb comes in, bridging the gap between genomics and clinical applications.
SNPweb is a cutting-edge platform that provides researchers and healthcare professionals with the tools and resources they need to harness the power of genomics in a clinical setting. By integrating genomic data with clinical information, SNPweb enables users to identify genetic variations that may be relevant to a patient’s health and tailor treatment plans accordingly.
One of the key features of SNPweb is its ability to analyze single nucleotide polymorphisms (SNPs), which are variations in a single base pair of DNA. These SNPs can impact an individual’s susceptibility to certain diseases, response to medications, and overall health outcomes. By analyzing a patient’s SNP profile, healthcare providers can better understand their unique genetic makeup and make more informed decisions about their care.
Another important aspect of SNPweb is its user-friendly interface, which allows researchers and healthcare professionals to easily access and interpret genomic data. The platform provides tools for data visualization, statistical analysis, and genetic variant annotation, making it easier to identify clinically relevant findings and integrate them into patient care.
Furthermore, SNPweb offers a comprehensive database of genetic variants and their associated clinical implications, allowing users to stay up-to-date on the latest research findings and medical guidelines. This database is continuously updated with new data from genomic studies, ensuring that users have access to the most current information available.
Overall, SNPweb is a valuable resource for healthcare providers looking to incorporate genomics into their practice. By leveraging the platform’s tools and resources, researchers and clinicians can better understand the genetic basis of diseases, personalize treatment plans, and improve patient outcomes. With SNPweb, the gap between genomics and clinical applications is being bridged, paving the way for a more personalized and effective approach to healthcare.
#SNPweb #Bridging #Gap #Genomics #Clinical #Applications,snpweb
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microsc
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microsc
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opy, and Drug DiscoveryDeep Learning has revolutionized many industries, including the life sciences. In the field of genomics, deep learning algorithms are being used to analyze vast amounts of genetic data to identify patterns and make predictions about disease risk, treatment outcomes, and more. By training deep neural networks on large genomic datasets, researchers are able to uncover hidden relationships and gain new insights into the complex interactions between genes and proteins.
In microscopy, deep learning is being used to enhance the resolution and quality of images, allowing scientists to study biological specimens in greater detail than ever before. By training deep convolutional neural networks on microscopy images, researchers can automatically identify and analyze cellular structures, track the movement of cells, and even predict the behavior of biological systems.
In drug discovery, deep learning is being used to predict the effectiveness and safety of potential drug candidates, reducing the time and cost of bringing new therapies to market. By training deep learning models on chemical structures and biological data, researchers can identify promising drug candidates, optimize their properties, and predict how they will interact with the human body.
Overall, deep learning is transforming the life sciences by enabling researchers to analyze complex biological data more effectively, make more accurate predictions, and ultimately accelerate the pace of scientific discovery. As the field continues to advance, we can expect even more groundbreaking applications of deep learning in genomics, microscopy, drug discovery, and beyond.
#Deep #Learning #Life #Sciences #Applying #Deep #Learning #Genomics #Microsc, deep learning
Handbook of Machine Learning Applications for Genomics (Studies in Big Data, 103)
Price: $249.99
(as of Dec 27,2024 05:13:07 UTC – Details)
Publisher : Springer; 1st ed. 2022 edition (June 24, 2022)
Language : English
Hardcover : 228 pages
ISBN-10 : 9811691576
ISBN-13 : 978-9811691577
Item Weight : 1.13 pounds
Dimensions : 6.14 x 0.56 x 9.21 inches
Are you interested in learning more about the intersection of machine learning and genomics? Look no further than the Handbook of Machine Learning Applications for Genomics (Studies in Big Data, 103). This comprehensive guide delves into the cutting-edge applications of machine learning in genomics, offering insights into how these technologies are revolutionizing the field.From predicting disease risk to analyzing large-scale genomic datasets, this handbook covers a wide range of topics that are essential for anyone working in the field of genomics. Whether you’re a seasoned researcher or a newcomer to the field, this book is sure to provide valuable information and resources to help you stay current with the latest advancements in the field.
Don’t miss out on this invaluable resource for researchers, students, and professionals interested in the exciting intersection of machine learning and genomics. Order your copy of the Handbook of Machine Learning Applications for Genomics today!
#Handbook #Machine #Learning #Applications #Genomics #Studies #Big #Data
Deep Learning for the Life Sciences : Applying Deep Learning to Genomics,…
Deep Learning for the Life Sciences : Applying Deep Learning to Genomics,…
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Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Drug Discovery, and Precision MedicineDeep learning, a subset of artificial intelligence, has revolutionized many industries, including the life sciences. In recent years, deep learning algorithms have been applied to various areas within the life sciences, such as genomics, drug discovery, and precision medicine, with promising results.
Genomics is one area where deep learning has shown great potential. By utilizing deep learning algorithms, researchers have been able to analyze large-scale genomic data more efficiently and accurately than ever before. These algorithms can identify patterns in genetic sequences that may be linked to certain diseases or traits, leading to new insights into the causes and mechanisms of various genetic disorders.
In drug discovery, deep learning has also played a crucial role. By training deep learning models on large datasets of chemical compounds and their biological effects, researchers can predict the efficacy and safety of potential drug candidates before conducting expensive and time-consuming clinical trials. This has the potential to accelerate the drug discovery process and reduce the cost of bringing new drugs to market.
Precision medicine, which aims to customize medical treatment based on an individual’s genetic makeup, is another area where deep learning is making an impact. By analyzing a patient’s genetic data using deep learning algorithms, healthcare providers can identify personalized treatment options that are tailored to the patient’s unique genetic profile. This can lead to more effective treatments with fewer side effects, ultimately improving patient outcomes.
Overall, deep learning is transforming the life sciences by enabling researchers and healthcare providers to analyze and interpret vast amounts of data more efficiently and accurately than ever before. As the field continues to advance, we can expect to see even more groundbreaking applications of deep learning in genomics, drug discovery, and precision medicine.
#Deep #Learning #Life #Sciences #Applying #Deep #Learning #Genomics..
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, – GOOD
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, – GOOD
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Deep learning has revolutionized many industries, and the life sciences are no exception. In recent years, researchers have been applying deep learning techniques to genomics in order to gain new insights into the complex world of genetics.One of the key benefits of deep learning in genomics is its ability to uncover patterns and relationships in large datasets that would be impossible for humans to discern on their own. By training deep learning models on massive amounts of genomic data, researchers can identify genetic variants associated with diseases, predict the effects of mutations, and even uncover new drug targets.
Deep learning has also been used to improve our understanding of gene regulation, gene expression, and protein structure. By analyzing vast amounts of genomic data, deep learning models can predict how genes are regulated, which genes are turned on or off in different conditions, and how proteins fold and interact with each other.
Overall, the application of deep learning to genomics has the potential to revolutionize our understanding of genetics and lead to new breakthroughs in personalized medicine, drug discovery, and disease prevention. As researchers continue to refine and expand their deep learning models, we can expect even more exciting discoveries in the field of genomics.
#Deep #Learning #Life #Sciences #Applying #Deep #Learning #Genomics #GOOD
R Bioinformatics Cookbook: Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis
Price: $49.15
(as of Dec 17,2024 18:54:22 UTC – Details)
Publisher : Packt Publishing (October 11, 2019)
Language : English
Paperback : 316 pages
ISBN-10 : 1789950694
ISBN-13 : 978-1789950694
Item Weight : 1.23 pounds
Dimensions : 9.25 x 7.52 x 0.66 inches
Are you looking to dive into the world of bioinformatics using R and Bioconductor? Look no further than the R Bioinformatics Cookbook! This comprehensive guide will take you through the process of using R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis.Whether you’re a beginner looking to learn the basics or an experienced bioinformatician looking to expand your skills, this cookbook has something for everyone. With step-by-step instructions, real-world examples, and hands-on exercises, you’ll be able to quickly and easily navigate the complex world of bioinformatics.
From importing and cleaning data to performing differential gene expression analysis and creating stunning visualizations, the R Bioinformatics Cookbook has everything you need to succeed in the field of bioinformatics. So why wait? Start your journey today and unlock the power of R and Bioconductor in bioinformatic analysis!
#Bioinformatics #Cookbook #Bioconductor #perform #RNAseq #genomics #data #visualization #bioinformatic #analysis
Deep Learning for the Life Sciences : Applying Deep Learning to Genomics …
Deep Learning for the Life Sciences : Applying Deep Learning to Genomics …
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Deep Learning for the Life Sciences: Applying Deep Learning to GenomicsIn recent years, deep learning has emerged as a powerful tool for analyzing and interpreting complex biological data. One area where deep learning has shown great promise is in the field of genomics. Genomics is the study of an organism’s complete set of DNA, including all of its genes.
By applying deep learning techniques to genomics data, researchers are able to uncover valuable insights into the genetic basis of diseases, identify potential drug targets, and even predict an individual’s risk of developing certain conditions.
One of the key advantages of deep learning in genomics is its ability to handle large and complex datasets. Genomic data is often high-dimensional and noisy, making it challenging to analyze using traditional statistical methods. Deep learning models, on the other hand, are able to extract meaningful patterns from these data, leading to more accurate predictions and discoveries.
Some popular deep learning architectures used in genomics include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are particularly well-suited for analyzing genomic sequences, as they can learn hierarchical patterns in the data, such as motifs and regulatory elements. RNNs, on the other hand, are useful for modeling sequential data, such as gene expression profiles over time.
Overall, the application of deep learning to genomics has the potential to revolutionize our understanding of the genetic basis of disease and pave the way for personalized medicine. As researchers continue to develop and refine deep learning models for genomics, we can expect to see even more exciting breakthroughs in the field.
#Deep #Learning #Life #Sciences #Applying #Deep #Learning #Genomics
Deep Learning for Genomics: Data-driven approaches for genomics applications in life sciences and biotechnology
Price:$44.99– $39.99
(as of Dec 17,2024 09:21:52 UTC – Details)
Publisher : Packt Publishing (November 11, 2022)
Language : English
Paperback : 270 pages
ISBN-10 : 1804615447
ISBN-13 : 978-1804615447
Item Weight : 1.06 pounds
Dimensions : 9.25 x 7.52 x 0.57 inches
Deep learning has revolutionized many industries, including the field of genomics. By using data-driven approaches, researchers in the life sciences and biotechnology sectors are able to extract valuable insights from vast amounts of genomic data.Genomics is the study of an organism’s complete set of DNA, including all of its genes. This field has immense potential for applications in medicine, agriculture, and environmental science. With the advancements in high-throughput sequencing technologies, researchers can now generate massive amounts of genomic data in a short amount of time.
Deep learning algorithms, such as convolutional neural networks and recurrent neural networks, have shown great promise in analyzing and interpreting this complex genomic data. These algorithms are able to learn patterns and relationships within the data, allowing researchers to make predictions and identify potential targets for further study.
In the field of medicine, deep learning for genomics has the potential to revolutionize personalized medicine. By analyzing an individual’s genetic makeup, researchers can predict disease risk, tailor treatments to specific genetic profiles, and develop new therapies targeted at specific genetic mutations.
In agriculture, deep learning for genomics can help breeders develop crops with improved yield, disease resistance, and nutritional content. By analyzing the genetic diversity of different plant species, researchers can identify genes responsible for desirable traits and use this information to breed new varieties with enhanced characteristics.
In environmental science, deep learning for genomics can help researchers understand the impact of climate change on biodiversity, track the spread of invasive species, and monitor the health of ecosystems. By analyzing the genetic diversity of different species, researchers can assess the resilience of ecosystems and develop strategies for conservation and restoration.
Overall, deep learning for genomics holds tremendous potential for advancing research in the life sciences and biotechnology sectors. By harnessing the power of data-driven approaches, researchers can unlock new insights into the genetic basis of life and develop innovative solutions to complex challenges in health, agriculture, and the environment.
#Deep #Learning #Genomics #Datadriven #approaches #genomics #applications #life #sciences #biotechnology
