Decision trees (DTs) and their random forest (RF) extensions are workhorses of classification and regression in Euclidean spaces. However, algorithms for learning in non-Euclidean spaces are still limited. We extend DT and RF algorithms to product manifolds: Cartesian products of several hyperbolic, hyperspherical, or Euclidean components. Such manifolds handle heterogeneous curvature while still factorizing neatly into simpler components, making them compelling embedding spaces for complex datasets. Our novel angular reformulation of DTs respects the geometry of the product manifold, yielding splits that are geodesically convex, maximum-margin, and composable. In the special cases of single-component manifolds, our method simplifies to its Euclidean or hyperbolic counterparts, or introduces hyperspherical DT algorithms, depending on the curvature. We benchmark our method on various classification, regression, and link prediction tasks on synthetic data, graph embeddings, mixed-curvature variational autoencoder latent spaces, and empirical data. Compared to six other classifiers, product DTs and RFs ranked first on 21 of 22 single-manifold benchmarks and 18 of 35 product manifold benchmarks, and placed in the top 2 on 53 of 57 benchmarks overall. This highlights the value of product DTs and RFs as straightforward yet powerful new tools for data analysis in product manifolds. Code for our paper is available at https://github.com/pchlenski/embedders.

Black box deep learning models trained on genomic sequences excel at predicting the outcomes of different gene regulatory mechanisms. Therefore, interpreting these models may provide novel insights into the underlying biology, supporting downstream biomedical applications. Due to their complexity, interpretable surrogate models can only be built for local explanations (e.g., a single instance). However, accomplishing this requires generating a dataset in the neighborhood of the input, which must maintain syntactic similarity to the original data while introducing semantic variability in the model's predictions. This task is challenging due to the complex sequence-to-function relationship of DNA. We propose using Genetic Programming to generate datasets by evolving perturbations in sequences that contribute to their semantic diversity. Our custom, domain-guided individual representation effectively constrains syntactic similarity, and we provide two alternative fitness functions that promote diversity with no computational effort. Applied to the RNA splicing domain, our approach quickly achieves good diversity and significantly outperforms a random baseline in exploring the search space, as shown by our proof-of-concept, short RNA sequence. Furthermore, we assess its generalizability and demonstrate scalability to larger sequences, resulting in a ~30% improvement over the baseline.

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.

Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis. The state of the art is currently represented by neural network models such as variational autoencoders (VAEs) which use a variational approximation of the likelihood for inference. We here present the Deep Generative Decoder (DGD), a simple generative model that computes model parameters and representations directly via maximum a posteriori (MAP) estimation. The DGD handles complex parameterized latent distributions naturally unlike VAEs which typically use a fixed Gaussian distribution, because of the complexity of adding other types. We first show its general functionality on a commonly used benchmark set, Fashion-MNIST. Secondly, we apply the model to multiple single-cell data sets. Here the DGD learns low-dimensional, meaningful and well-structured latent representations with sub-clustering beyond the provided labels. The advantages of this approach are its simplicity and its capability to provide representations of much smaller dimensionality than a comparable VAE.

Living beings, especially human bodies, are full of mysteries, where constant researches & developments bring new theories upon us in just so many ways. What else is a fact is humans are vulnerable to diseases & often-times procure infections that do not necessarily show their origin, like cancer. We know the influx of patients suffering from cancer is large throughout the world, making lives miserable for people. Hence, different countries are investing substantially in conducting numerous R&D activities to find a cure, therapeutics, and innovations for such health issues. Since such troublesome diseases are found in the cells & DNAs, researchers are showing an increasing focus on studying genomics and analyzing the functioning of illness.

The project represents a quantum leap in advanced personalised medicine, pooling genomic/ '-omics' health data through a secure and trustworthy Federated Learning platform. Our disruptive AI models, scaled up by High-Performance Computing, will boost the processing capacity of data repositories from 10 clinical sites across Europe, empowering forward-thinking research of common and rare Haematological Diseases. Don't miss out on our jouney, stay connected! The AI4Gov Master's Program in Artificial Intelligence for Public Services offers to future leaders in digital transformation a full preparation in using AI and digital technology in the public sector. Our challenges: Most HDs have a genetic background They are a growing public health challenge EU repositories are unconnected https://genomed4all.eu/ Don't miss out on our jouney, stay connected!

Artificial intelligence is defined as the engineering and science of making intelligent computer systems that can exhibit intelligence like humans (natural intelligence) and perform tasks directly without any human intervention. AI in healthcare uses software and algorithms to approximate human perception in order to analyze complex medical data and to analyze the relationship between treatment or prevention techniques and patient outcomes. Reasons for Doing the Study: AI has established itself as an important component of the life science industry.There is enormous potential for future applications, but there are also many near-term commercial opportunities, and the list of new products and applications is continuously growing. This report will help companies to better evaluate the many potential applications and to identify commercial opportunities for product development and competitive strategy. The information contained here will assist companies in prioritizing product opportunities and establishing solid frameworks for their strategic planning.

Artificial intelligence is defined as the engineering and science of making intelligent computer systems that can exhibit intelligence like humans (natural intelligence) and perform tasks directly without any human intervention. AI in healthcare uses software and algorithms to approximate human perception in order to analyze complex medical data and to analyze the relationship between treatment or prevention techniques and patient outcomes. Reasons for Doing the Study: AI has established itself as an important component of the life science industry.There is enormous potential for future applications, but there are also many near-term commercial opportunities, and the list of new products and applications is continuously growing. This report will help companies to better evaluate the many potential applications and to identify commercial opportunities for product development and competitive strategy. The information contained here will assist companies in prioritizing product opportunities and establishing solid frameworks for their strategic planning.

WellAI Data Scientists are presenting events about the latest research on AI, Machine Learning, Genomics, Blockchain in Healthcare and Finance. Can AI, Genomics, Blockchain make you and your family healthier? To find all answers, you can take Health & Tech 2021 Challenge by WellAI. You can register for free here. This online event is jointly organized with the society of quantitative analysts.

Genomic is Python software that evolves sound treatments and produce novel sounds. It offers features that have the potential to serve sound designers and composers, aiding them in their search for new and interesting sounds. This paper lays out the rationale and some design decisions made for Genomic, and proposes several intuitive ways of both using the software and thinking about the techniques that it enables for the modification and design of sound.