Download or read book Interpretable Machine Learning for Scientific Discovery in Regulatory Genomics written by Avanti Shrikumar and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: All cells in our body have approximately the same DNA sequence, yet different cell-types have distinct behavior due to differential expression of genes. This cell-type specific control of gene expression is governed by regulatory proteins that bind to DNA. Over 90% of disease-associated mutations do not disrupt the DNA sequences of genes, but rather disrupt functions involved in the regulation of gene expression. Unfortunately, conventional computational models can fail to distinguish between mutations that are benign and mutations that are likely to affect regulatory activity. Machine learning poses a solution to this dilemma: by training complex models, including deep learning models, to predict regulatory activity from DNA sequence, we implicitly force the models to learn which sequence features are relevant for regulation. However, our difficulty in interpreting and trusting these models limits our ability to extract novel scientific insights from them. In this thesis, I will present techniques I have developed to address some of these limitations. I will begin by discussing DeepLIFT, a fast algorithm for calculating example-specific importance scores to explain the predictions of a deep learning model, as well as GkmExplain, an algorithm for efficiently computing importance scores for gapped k-mer support vector machines. I will then describe TF-MoDISco, an algorithm that leverages importance scores produced by an algorithm such as DeepLIFT or GkmExplain to discover recurring patterns learned by the model. Next, I discuss two projects on leveraging domain-specific knowledge to improve the performance and interpretability of deep learning models trained on regulatory genomic data. The first project, on reverse-complement parameter sharing, introduces architectures that can account for symmetries inherent in the double-stranded nature of regulatory DNA. The second project, on separable fully-connected layers, introduces a novel parameterization to exploit the fact that positional patterns in DNA binding sites are often shared across different regulatory proteins. Finally, I will discuss three projects centered on improving the reliability of predictions derived from these models. The first project deals with the situation where a deep learning model trained on regulatory genomic data is leveraged to identify pairs of proteins that have non-additive interaction effects; we demonstrate that looking at change in the model's prediction loss, rather than simply looking at the change in the predictions, is a far more robust indicator of whether the model's learned interaction effect is likely to be an artifact. The second project presents a state-of-the-art algorithm for improving the model predictions under a type of data distribution shift known as ``label shift'', where the class proportions in the held-out testing set differ from the class proportions that the model was trained on (this can occur, for example, if a model that is trained to predict diseases given symptoms is deployed in a situation where the prevalence of the disease is far higher than in the data distribution it was trained on). The third project explores the scenario where a model can abstain from making predictions on a subset of examples that it is uncertain of, in order to improve user trust in the predictions on remaining examples; in the project, we devise a novel and flexible strategy for choosing which examples to abstain on when the goal is to optimize metrics other than simple prediction accuracy, such as the area under the ROC curve or the sensitivity at a target specificity level (such metrics are commonly used in genomics and medicine). Taken together, I hope these methods help pave the way for successful application of advanced machine learning techniques to derive novel scientific insights from regulatory genomic data.

Download or read book Improving and Leveraging the Interpretability of Deep Neural Networks for Genomics written by Alex Michael Tseng and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, the field of genomics has been characterized by an extraordinary influx of novel high-throughput technologies and techniques. These methods produce enormous datasets which quantify cellular state across several axes of measurement. Given their large size and complexity, machine-learning algorithms (particularly deep neural networks) have increasingly been relied upon to ingest and model these datasets. In fitting to the data, these neural networks demonstrably learn the overarching patterns and subtle nuances in the underpinning biology. In order to be most useful for scientific discovery, however, these models need to be able to convert their learned scientific principles into a form that human scientists can then understand and use. Unfortunately, this transfer of knowledge has been limited due to the overall uninterpretability of deep neural networks, and the difficulty of distilling a model's individual decisions across a dataset into a set of human-understandable rules. In this thesis, I will present my work on addressing these challenges. First, I will discuss my development of the Fourier-transform-based attribution prior, which trains deep neural networks to be more stable and interpretable, thereby allowing them to more consistently and reliably reveal the biological patterns driving various genome-regulatory events. Subsequently, I will present my work on a computational framework which distills and summarizes a neural network's individual decisions into a small set of global protein-binding rules that can then be understood by a human scientist. Through several case studies, I will show that these methods can be applied in a real-world setting for scientific discovery. Although the methods presented in this thesis are primarily applied to the problem of genomics, many ideas discussed here are easily (if not directly) applicable to many other domains of machine learning. Together, these developments represent a major advancement in how humans extract learned information from deep neural networks. This can have resounding improvements and impacts in not only how neural networks can be used for science, but also in our understanding of how they internally learn in general.

Download or read book Interpretable Machine Learning Methods for Regulatory and Disease Genomics written by Peyton Greis Greenside and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: It is an incredible feat of nature that the same genome contains the code to every cell in each living organism. From this same genome, each unique cell type gains a different program of gene expression that enables the development and function of an organism throughout its lifespan. The non-coding genome - the ~98 of the genome that does not code directly for proteins - serves an important role in generating the diverse programs of gene expression turned on in each unique cell state. A complex network of proteins bind specific regulatory elements in the non-coding genome to regulate the expression of nearby genes. While basic principles of gene regulation are understood, the regulatory code of which factors bind together at which genomic elements to turn on which genes remains to be revealed. Further, we do not understand how disruptions in gene regulation, such as from mutations that fall in non-coding regions, ultimately lead to disease or other changes in cell state. In this work we present several methods developed and applied to learn the regulatory code or the rules that govern non-coding regions of the genome and how they regulate nearby genes. We first formulate the problem as one of learning pairs of sequence motifs and expressed regulator proteins that jointly predict the state of the cell, such as the cell type specific gene expression or chromatin accessibility. Using pre-engineered sequence features and known expression, we use a paired-feature boosting approach to build an interpretable model of how the non-coding genome contributes to cell state. We also demonstrate a novel improvement to this method that takes into account similarities between closely related cell types by using a hierarchy imposed on all of the predicted cell states. We apply this method to discover validated regulators of tadpole tail regeneration and to predict protein-ligand binding interactions. Recognizing the need for improved sequence features and stronger predictive performance, we then move to a deep learning modeling framework to predict epigenomic phenotypes such as chromatin accessibility from just underlying DNA sequence. We use deep learning models, specifically multi-task convolutional neural networks, to learn a featurization of sequences over several kilobases long and their mapping to a functional phenotype. We develop novel architectures that encode principles of genomics in models typically designed for computer vision, such as incorporating reverse complementation and the 3D structure of the genome. We also develop methods to interpret traditionally ``black box" neural networks by 1) assigning importance scores to each input sequence to the model, 2) summarizing non-redundant patterns learned by the model that are predictive in each cell type, and 3) discovering interactions learned by the model that provide indications as to how different non-coding sequence features depend on each other. We apply these methods in the system of hematopoiesis to interpret chromatin dynamics across differentiation of blood cell types, to understand immune stimulation, and to interpret immune disease-associated variants that fall in non-coding regions. We demonstrate strong performance of our boosting and deep learning models and demonstrate improved performance of these machine learning frameworks when taking into account existing knowledge about the biological system being modeled. We benchmark our interpretation methods using gold standard systems and existing experimental data where available. We confirm existing knowledge surrounding essential factors in hematopoiesis, and also generate novel hypotheses surrounding how factors interact to regulate differentiation. Ultimately our work provides a set of tools for researchers to probe and understand the non-coding genome and its role in controlling gene expression as well as a set of novel insights surrounding how hematopoiesis is controlled on many scales from global quantification of regulatory sequence to interpretation of individual variants.

Download or read book Machine Learning and Deep Learning in Computational Toxicology written by Huixiao Hong and published by Springer Nature. This book was released on 2023-03-11 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning and deep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology.

Download or read book Handbook of Machine Learning Applications for Genomics written by Sanjiban Sekhar Roy and published by Springer Nature. This book was released on 2022-06-23 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.

Download or read book Interpretable Machine Learning in Plant Genomes written by Christina Brady Azodi and published by . This book was released on 2019 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex systems are ubiquitous in genetics and genomics. From the regulation of gene expression to the genetic basis of complex traits, we see that complex networks of diverse cellular molecules underpin the natural world. Driven by technological advances, today's researchers have access to large amounts of omics data from diverse species. At the same time, improvements in computer processing and algorithms have produced more powerful computational tools. Taken together, these advances mean that those working at the interface of data science and biology are poised to better model and understand complex biological systems. The research in this dissertation demonstrates how a data-driven approach can be used to better understand three complex systems: (1) transcriptional response to single and combined heat and drought stress in Arabidopsis thaliana, (2) the genetic basis of flowering time, a complex trait, in Zea mays, and (3) the social basis for opinions and beliefs about biotechnology products.To study the first system, we generated models of the cis-regulatory code from information about DNA sequence and additional omics levels using both classic machine learning and deep learning algorithms. We identified 1,061 putative cis-regulatory elements associated with different patterns of response to single and combined heat and drought stress and found that information about additional levels of regulation, especially chromatin accessibility and known transcription factor binding, improved our models of the cis-regulatory code. To study the second system, we generated phenotype prediction models for flowering time, height, and yield based on either genetic markers or transcript levels at the seedling stage. We found that, while genetic marker-based models performed better than transcript level-based models, models that integrated both types of data performed best. Furthermore, transcript-based models were more useful for finding genes known to be associated with flowering time, highlighting how using additional levels of omics data can improve our ability to understand the genetic basis of complex traits. Finally, to study the third system, we integrated 29 characteristics about a person (e.g. age, political ideology, education, values, environmental beliefs) into a machine learning model that would predict an individual's beliefs and opinions about five different types of biotechnology products (e.g. biofortification, biopharmaceuticals). While this approach was particularly usefully for identifying individuals that were broadly supportive of biotechnology, finding characteristics of individuals with negative or conditional (i.e. support product A, but not B) opinions was more challenging, highlighting the complexity of public opinions about biotechnology.

Download or read book Machine Learning and Network Driven Integrative Genomics written by Mehdi Pirooznia and published by Frontiers Media SA. This book was released on 2021-04-29 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Download or read book Machine Learning Methods for Multi Omics Data Integration written by Abedalrhman Alkhateeb and published by Springer Nature. This book was released on 2023-12-15 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Download or read book Biologically Interpretable Machine Learning Methods to Understand Gene Regulation for Disease Phenotypes written by Ting Jin and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene expression and regulation is a key molecular mechanism driving the development of human diseases, particularly at the cell type level, but it remains elusive. For example in many brain diseases, such as Alzheimer's disease (AD), understanding how cell-type gene expression and regulation change across multiple stages of AD progression is still challenging. Moreover, interindividual variability of gene expression and regulation is a known characteristic of the human brain and brain diseases. However, it is still unclear how interindividual variability affects personalized gene regulation in brain diseases including AD, thereby contributing to their heterogeneity. Recent technological advances have enabled the detection of gene regulation activities through multi-omics (i.e., genomics, transcriptomics, epigenomics, proteomics). In particular, emerging single-cell sequencing technologies (e.g., scRNA-seq, scATAC-seq) allow us to study functional genomics and gene regulation at the cell-type level. Moreover, these multi-omics data of populations (e.g., human individuals) provide a unique opportunity to study the underlying regulatory mechanisms occurring in brain disease progression and clinical phenotypes. For instance, PsychAD is a large project generating single-cell multi-omics data including many neuronal and glial cell types, aiming to understand the molecular mechanisms of neuropsychiatric symptoms of multiple brain diseases (e.g., AD, SCZ, ASD, Bipolar) from over 1,000 individuals. However, analyzing and integrating large-scale multi-omics data at the population level, as well as understanding the mechanisms of gene regulation, also remains a challenge. Machine learning is a powerful and emerging tool to decode the unique complexities and heterogeneity of human diseases. For instance, Beebe-Wang, Nicosia, et al. developed MD-AD, a multi-task neural network model to predict various disease phenotypes in AD patients using RNA-seq. Additionally, with advancements in graph neural networks, which possess enhanced capabilities to represent sophisticated gene network structures like gene regulation networks that control gene expression. Efforts have also been made to capture the gene regulation heterogeneity of brain diseases. For instance, Kim SY has applied graph convolutional networks to offer personalized diagnostic insights through population graphs that correspond with disease progression. However, many existing machine learning methods are often limited to constructing accurate models for disease phenotype prediction and frequently lack biological interpretability or personalized insights, especially in gene regulation. Therefore, to address these challenges, my Ph.D. works have developed three machine-learning methods designed to decode the gene regulation mechanisms of human diseases. First, in this dissertation, I will present scGRNom, a computational pipeline that integrates multi-omic data to construct cell-type gene regulatory networks (GRNs) linking non-coding regulatory elements. Next, I will introduce i-BrainMap an interpretable knowledge-guided graph neural network model to prioritize personalized cell type disease genes, regulatory linkages, and modules. Thirdly, I introduce ECMaker, a semi-restricted Boltzmann machine (semi-RBM) method for identifying gene networks to predict diseases and clinical phenotypes. Overall, all our interpretable machine learning models improve phenotype prediction, prioritize key genes and networks associated with disease phenotypes, and are further aimed at enhancing our understanding of gene regulatory mechanisms driving disease progression and clinical phenotypes.

Download or read book Data Science for Genomics written by Amit Kumar Tyagi and published by Academic Press. This book was released on 2022-11-27 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes. Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR. Provides a detailed explanation of data science concepts, methods and algorithms, all reinforced by practical examples that are applied to genomics Presents a roadmap of future trends suitable for innovative Data Science research and practice Includes topics such as Blockchain technology for securing data at end user/server side Presents real world case studies, open issues and challenges faced in Genomics, including future research directions and a separate chapter for Ethical Concerns

Download or read book Kernel Methods in Computational Biology written by Bernhard Schölkopf and published by MIT Press. This book was released on 2004 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed overview of current research in kernel methods and their application to computational biology.

Download or read book Machine Learning and Systems Biology in Genomics and Health written by Shailza Singh and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.

Download or read book Artificial Intelligence in Drug Discovery written by Nathan Brown and published by Royal Society of Chemistry. This book was released on 2020-11-04 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Download or read book Genomics Assisted Crop Improvement written by R.K. Varshney and published by Springer Science & Business Media. This book was released on 2007-12-12 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This superb volume provides a critical assessment of genomics tools and approaches for crop breeding. Volume 1 presents the status and availability of genomic resources and platforms, and also devises strategies and approaches for effectively exploiting genomics research. Volume 2 goes into detail on a number of case studies of several important crop and plant species that summarize both the achievements and limitations of genomics research for crop improvement.