Data Science and Machine Learning are intricately connected, particularly in computational biology. In a time when biological data is being produced on an unprecedented scale — encompassing genomic sequences, protein interactions, and metabolic pathways- meeting the demand has never been more crucial.

Data visualization plays a crucial role in biological data sciences since it allows the transformation of complex, often incomprehensible raw data into visual formats that are easier to understand and interpret. This allows biologists to recognize patterns, anomalies, and correlations that would otherwise be lost in the sheer volume of data. In addition, machine learning (ML) has brought about a revolution in the analysis of biological data. Exploiting extensive datasets, ML provides tools to model complex systems and generate predictions. Indeed, ML algorithms excel at uncovering subtle patterns in data, contributing to tasks like predicting protein structures, comprehending genetic variations and their implications for diseases, and even facilitating drug discovery by predicting molecular interactions.

The integration of data visualization and machine learning is particularly powerful. In particular, visualization may aid in interpreting machine learning models, allowing biologists to understand and trust their predictions. It could also help fine-tune these models by identifying outliers or anomalies in the data.

Due to its remarkable capability, there has been a surge in the development and application of tools that combine data visualization and machine learning in biology. Platforms that integrate these technologies enable biologists to conduct comprehensive analyses without needing deep expertise in computer science. Assuredly, this democratization of data science and ML has empowered more and more biologists to engage in sophisticated, data-driven research.

Tissues and organs are complex and highly-organized systems composed of diverse cells that work together to maintain homeostasis, drive development and mediate complex disease progression as Myocardial Infarction (Kuppe et al. 2022). A key focus of modern biology is understanding how heterogeneous populations of cells coexist and communicate with each other (intercellular signaling), how they properly respond (intracellular signaling) within a tissue and organ system and how these processes vary across different experimental conditions (comparative analysis). Recently, a rapid expansion of computational tools exploring the expression of ligand and receptor has enabled the systematic inference of cell-cell communication from single-cell transcriptomics and spatial transcriptomics data (Armingol et al. 2021; Armingol, Baghdassarian, and Lewis 2024). These are crucial in unravelling the complex landscape of biological systems.

This tutorial aims to provide a comprehensive introduction to computational approaches for cell-cell communication inference using high throughput transcriptomics data. It covers the fundamental concepts of cellular communication and assumptions underlying analysis focusing on the main computational methods used in the field. This includes computational approaches for inter-cellular communication inference (CellphoneDB (Efremova et al. 2020); LIANA (Dimitrov et al. 2022, 2024)) and for intra-cellular signals communication (scSeqComm (Baruzzo, Cesaro, and Di Camillo 2022); NicheNet (Browaeys, Saelens, and Saeys 2019)). Next, we will describe approaches for comparative analysis of cell-cell networks in distinct biological conditions (CrossTalkeR (Nagai et al. 2021)) and methods for spatially resolved cell-cell communications (Ischia (Regan-KomitoDaniel 2024); DeepCOLOR (Kojima et al. 2024)).

In the first part of the tutorial, participants will be introduced to the theoretical basis of state-of-the-art computational approaches and will learn how to use representative tools for inferring intercellular signaling and intracellular signaling pathways. In the second part, we will focus on the comparative analyses, i.e. changes of cell-cell communication in two conditions, and subsequently highlighting the unique insights spatial transcriptomics data can provide for understanding tissue architecture and cellular communication. Both sections will be followed by a hands-on component based on the analysis of single cell and spatial transcriptomics data from the myocardial infarction atlas (Kuppe et al. 2022). To promote transparency, all the codes, software tools and the datasets used throughout the tutorial will be available and accessible through open-access repositories (e.g. GitHub repositories or Zenodo platforms).

Modern bioinformatics analyses rely heavily on the existing knowledge of the role of genes and mutations in different diseases, as well as the complex interactions between genes, proteins and drugs. However, access to this information is often limited for many biomedical problems, especially niche areas, due to lack of knowledge bases and large curation costs. The information is often locked in the text of the original research papers. Machine learning methods, particular natural language processing techniques, offer an automated approach to extracting knowledge from the research literature to build bespoke knowledge bases for scientists’ needs. This tutorial will provide a hands-on introduction to the core tasks in biomedical natural language processing (BioNLP). These include identifying mentions of important concepts (e.g. phenotypes, cell-lines, etc) and extracting nuanced relationships between them. Finally, it will show how large language models have changed how information can be quickly extracted, but also highlight their challenges.

The urgent need for crop improvement is hindered by the lack of precision in crop breeding. Although significant progress has been made in genomics, many causal genes of important agronomic and nutritional traits remain unknown. This is due to the inefficient identification of causal genetic features in candidate genes. With the growing body of sequencing data and phenotype information, current advances in genomics and the development of bioinformatics tools offer improvements in candidate gene selection. In this workshop, SoyHUB, the suite of tools, strategies, and solutions for soybean applied genomics, will be presented along with its extension to other crops at KBCommons. Our methodology for data integration, curation, reuse, and leveraging will be highlighted. Practical utilization of integrated data with the tools will be demonstrated. We will focus on the selection of best-performing markers, identification of causal genes, and exploration of alleles and genomic variation. We will cover simple Mendelian traits, showing how to analyze variation in protein-coding regions and promoters and touch on copy number variation. Solutions for complex cases, such as multiple independent alleles in a single gene or quantitative traits, will also be introduced. This workshop will highlight recent achievements in leveraging big data to improve precision in GWAS-driven discoveries and, therefore, accelerate the breeding of soybean and other crops.

As computational biologists, we produce biological datasets, visualizations, and computational tools. Our shared goal is to make our data and tools widely usable and accessible. However, we often fail to meet the needs of certain groups of people. Our recent comprehensive evaluation [1] (https://inscidar.org) shows that biological data resources are largely inaccessible to people with disabilities, with severe accessibility issues in almost 75% of all 3,112 data portals included in the study. To address the critical accessibility barriers, it is important to increase awareness of accessibility in the community and teach the workforce practical ways to enhance the accessibility of biological data resources. While there are existing resources and training opportunities that focus on content that includes no or little data, there is a lack of solutions and resources that provide insights into how to make data-intensive content accessible.

Our tutorial is designed to help participants understand the importance of digital accessibility in computational biology and practice various approaches to test and implement digital accessibility of biological data and visualizations. We will demonstrate our evaluation results [1] to help participants understand the critical barriers in biological research and education for people with disabilities, such as those involving vision, cognitive, and physical function. We will use hands-on examples that are familiar to and widely used by computational biologists, such as computational notebooks, genome browsers, and other visualizations. Our tutorial will conclude by introducing open problems and recent innovations, such as the accessibility of interactive genomics data visualizations [2]. We will ensure that our tutorial and all the materials are accessible.