Improved Sampling Based Motion Planning Through Local Learning

Every motion made by a moving object is either planned implicitly, e.g., human natural movement from one point to another, or explicitly, e.g., pre-planned information about where a robot should move in a room to effectively avoid colliding with obstacles. Motion planning is a well-studied concept in robotics and it involves moving an object from a start to goal configuration. Motion planning arises in many application domains such as robotics, computer animation (digital actors), intelligent CAD (virtual prototyping and training) and even computational biology (protein folding and drug design). Interestingly, a single class of planners, sampling-based planners have proven effective in all these domains. Probabilistic Roadmap Methods (PRMs) are one type of sampling-based planners that sample robot configurations (nodes) and connect them via viable local paths (edges) to form a roadmap containing representative feasible trajectories. The roadmap is then queried to find solution paths between start and goal configurations. Different PRM strategies perform differently given different input parameters, e.g., workspace environments and robot definitions. Motion planning, however, is computationally hard – it requires geometric path planning which has been shown to be PSPACE hard, complex representational issues for robots with known physical, geometric and temporal constraints, and challenging mapping/representing requirements for the workspace environment. Many important environments, e.g., houses, factories and airports, are heterogeneous, i.e., contain free, cluttered and narrow spaces. Heterogeneous environments, however, introduce a new set of problems for motion planning and PRM strategies because there is no ideal method suitable for all regions in the environment. In this work we introduce a technique that can adapt and apply PRM methods suitable for local regions in an environment. The basic strategy is to first identify a local region of the environment suitable for the current action based on identified neighbors. Next, based on past performance of methods in this region, adapt and pick a method to use at this time. This selection and adaptation is done by applying machine learning. By performing the local region creation in this dynamic fashion, we remove the need to explicitly partition the environment as was done in previous methods and which is difficult to do, slows down performance and includes the difficult process of determining what strategy to use even after making an explicit partitioning. Our method handles and removes these overheads. We show benefits of this approach in both planning robot motions and in protein folding simulations. We perform experiments on robots in simulation with different degrees of freedom and varying levels of heterogeneity in the environment and show an improvement in performance when our local learning method is applied. Protein folding simulations were performed on 23 proteins and we note an improvement in the quality of pathways produced with comparable performance in terms of time needed to build the roadmap

Adaptive local learning in sampling based motion planning for protein folding

BACKGROUND: Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods and variants contain several phases (i.e., sampling, connection, and path extraction). Most of the time is spent in the connection phase and selecting which variant to employ is a difficult task. Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes. RESULTS: We develop a local learning algorithm that exploits the past performance of methods within the neighborhood of the current connection attempts as a basis for learning. It is sensitive not only to different types of landscapes but also to differing regions in the landscape itself, removing the need to explicitly partition the landscape. We perform experiments on 23 proteins of varying secondary structure makeup with 52–114 residues. We compare the success rate when using our methods and other methods. We demonstrate a clear need for learning (i.e., only learning methods were able to validate against all available experimental data) and show that local learning is superior to global learning producing, in many cases, significantly higher quality results than the other methods. CONCLUSIONS: We present an algorithm that uses local learning to select appropriate connection methods in the context of roadmap construction for protein folding. Our method removes the burden of deciding which method to use, leverages the strengths of the individual input methods, and it is extendable to include other future connection methods

The Nigerian Bioinformatics and Genomics Network (NBGN): a collaborative platform to advance bioinformatics and genomics in Nigeria

Africa plays a central importance role in the human origins, and disease susceptibility, agriculture and biodiversity conservation. Nigeria as the most populous and most diverse country in Africa, owing to its 250 ethnic groups and over 500 different native languages is imperative to any global genomic initiative. The newly inaugurated Nigerian Bioinformatics and Genomics Network (NBGN) becomes necessary to facilitate research collaborative activ�ities and foster opportunities for skills’ development amongst Nigerian bioinformatics and genomics investigators. NBGN aims to advance and sustain the fields of genomics and bio�informatics in Nigeria by serving as a vehicle to foster collaboration, provision of new oppor�tunities for interactions between various interdisciplinary subfields of genomics, computational biology and bioinformatics as this will provide opportunities for early career researchers. To provide the foundation for sustainable collaborations, the network organises conferences, workshops, trainings and create opportunities for collaborative research studies and internships, recognise excellence, openly share information and create opportunities for more Nigerians to develop the necessary skills to exceed in genomics and bioinformatics. NBGN currently has attracted more than 650 members around the world. Research collabora�tions between Nigeria, Africa and the West will grow and all stakeholders, including funding partners, African scientists, researchers across the globe, physicians and patients will be the eventual winners. The exponential membership growth and diversity of research interests of NBGN just within weeks of its establishment and the unanticipated attendance of its activ�ities suggest the significant importance of the network to bioinformatics and genomics research in Nigeria

Strengthening Bioinformatics and Genomics Analysis Skills in Africa for Attainment of the Sustainable Development Goals Report of the 2nd Conference of the Nigerian Bioinformatics and Genomics Network

The second conference of the Nigerian Bioinformatics and Genomics Network (NBGN21) was held from October 11 to October 13, 2021. The event was organized by the Nigerian Bioinformatics and Genomics Network. A 1-day genomic analysis workshop on genome-wide association study and polygenic risk score analysis was organized as part of the conference. It was organized primarily as a research capacity building initiative to empower Nigerian researchers to take a leading role in this cutting-edge field of genomic data science. The theme of the conference was “Leveraging Bioinformatics and Genomics for the attainments of the Sustainable Development Goals.” The conference used a hybrid approach—virtual and in-person. It served as a platform to bring together 235 registered participants mainly from Nigeria and virtually, from all over the world. NBGN21 had four keynote speakers and four leading Nigerian scientists received awards for their contributions to genomics and bioinformatics development in Nigeria. A total of 100 travel fellowships were awarded to delegates within Nigeria. A major topic of discussion was the application of bioinformatics and genomics in the achievement of the Sustainable Development Goals (SDG3—Good Health and Well-Being, SDG4—Quality Education, and SDG 15—Life on Land [Biodiversity]). In closing, most of the NBGN21 conference participants were interviewed and interestingly they agreed that bioinformatics and genomic analysis of African genomes are vital in identifying population-specific genetic variants that confer susceptibility to different diseases that are endemic in Africa. The knowledge of this can empower African healthcare systems and governments for timely intervention, thereby enhancing good health and well-bein

Predicting Human–pathogen Protein–protein Interactions Using Natural Language Processing Methods

In this paper, we predict the interaction of proteins between Humans and Yersinia pestis via amino acid sequences. We utilize multiple Natural Language Processing (NLP) methods available in deep learning in a unique format and produce promising results. Our developed model gives a cross-validation AUC score of 0.92 and is comparable with other work that utilizes extensive biochemical properties i.e, network and sequence in conjunction. We achieve this by combining advanced tools in neural machine translation into an integrated end-to-end deep learning framework as well as methods of preprocessing that are novel to the field of bioinformatics. We show that our proposed approach is robust to different protein–protein interactions between host and pathogen data