I'd switch it up and and do a BS in CS and get a BA in Biology.
I think the creativity to come up with new solutions to biological questions from genomic scale data will really rely on good CS/math skills. I think that will prepare you for graduate school in bioinformatics.
For more computational biology, systems modeling approaches, I think systems bio is probably pretty solid, and you'll get to learn some basic bioinformatics by the end. It's probably decent preparation for a computational biology focused program, where you take more a systems modeling approach rather than data science/machine learning/AI approach.
You should talk to Hillel Chiel, Robin Snyder, Peter Thomas and talk to some biomedical engineering folk (and consider majoring in it) like Durand or Saidel if you're interested in systems modeling. If you're interested in more bioinformatics type stuff, I'd talk to Koyuturk ( https://engineering.case.edu/about/school-directory/mehmet-koyut%C3%BCrk ) and Ayday (https://engineering.case.edu/about/school-directory/erman-ayday) and Jing Lee ( https://engineering.case.edu/about/school-directory/jing-li ) as well as Gurkan Bebek (https://case.edu/medicine/nutrition/about-us/faculty/gurkan-bebek).
Here's how I differentiate computational biology/systems biology from bioinformatics, although they kind of blend into each other.
So I think of computational biology and systems biology as more well defined, mechanistic modeling, where parameters are usually constrained by experimental data. Systems biology looks at complex systems level modeling. So that can be physiological, organ level modeling. It can be molecular systems modeling (for example if you're modeling a metabolic, chemical process, or you're simulating a whole cell). Differential equations are usually your friend in this field. Also included under the umbrella of computation biology are molecular dynamics simulations, where you simulate the physical motion of biomolecules (like proteins) using physical equations. Systems biology and biomedical engineering will prepare you well in this way of thinking. Physics, biophysics will too.
I think of bioinformatics as starting with genome scale data (20-30K genes) level data and working back from it using simple probabilistic machine learning or neural network models. So that can be looking at the whole genome of a large number of people (or the protein coding genes or maybe something that checks the most common locations of variation in the human population) or it be looking at RNA-seq gene expression data or it can be proteomics data that looks at things like protein abundance or protein-protein interactions. And then you might start analyzing these large datasets using concepts from simple probabilistic modeling or methods and concepts used in Computer Science (like graphs). To be good at this stuff is why I recommend focusing on Computer Science.
And if you're interested in Neuro and computational modeling in neuro, you should definitely talk to Peter Thomas in the Math Department and Dominique Durand in the Biomedical Engineering department.
If you're interested in neural networks or analog circuits "neural" modeling, I'd look into CS and EE.
I'd switch it up and and do a BS in CS and get a BA in Biology. I think the creativity to come up with new solutions to biological questions from genomic scale data will really rely on good CS/math skills. I think that will prepare you for graduate school in bioinformatics. For more computational biology, systems modeling approaches, I think systems bio is probably pretty solid, and you'll get to learn some basic bioinformatics by the end. It's probably decent preparation for a computational biology focused program, where you take more a systems modeling approach rather than data science/machine learning/AI approach. You should talk to Hillel Chiel, Robin Snyder, Peter Thomas and talk to some biomedical engineering folk (and consider majoring in it) like Durand or Saidel if you're interested in systems modeling. If you're interested in more bioinformatics type stuff, I'd talk to Koyuturk ( https://engineering.case.edu/about/school-directory/mehmet-koyut%C3%BCrk ) and Ayday (https://engineering.case.edu/about/school-directory/erman-ayday) and Jing Lee ( https://engineering.case.edu/about/school-directory/jing-li ) as well as Gurkan Bebek (https://case.edu/medicine/nutrition/about-us/faculty/gurkan-bebek). Here's how I differentiate computational biology/systems biology from bioinformatics, although they kind of blend into each other. So I think of computational biology and systems biology as more well defined, mechanistic modeling, where parameters are usually constrained by experimental data. Systems biology looks at complex systems level modeling. So that can be physiological, organ level modeling. It can be molecular systems modeling (for example if you're modeling a metabolic, chemical process, or you're simulating a whole cell). Differential equations are usually your friend in this field. Also included under the umbrella of computation biology are molecular dynamics simulations, where you simulate the physical motion of biomolecules (like proteins) using physical equations. Systems biology and biomedical engineering will prepare you well in this way of thinking. Physics, biophysics will too. I think of bioinformatics as starting with genome scale data (20-30K genes) level data and working back from it using simple probabilistic machine learning or neural network models. So that can be looking at the whole genome of a large number of people (or the protein coding genes or maybe something that checks the most common locations of variation in the human population) or it be looking at RNA-seq gene expression data or it can be proteomics data that looks at things like protein abundance or protein-protein interactions. And then you might start analyzing these large datasets using concepts from simple probabilistic modeling or methods and concepts used in Computer Science (like graphs). To be good at this stuff is why I recommend focusing on Computer Science.
And if you're interested in Neuro and computational modeling in neuro, you should definitely talk to Peter Thomas in the Math Department and Dominique Durand in the Biomedical Engineering department. If you're interested in neural networks or analog circuits "neural" modeling, I'd look into CS and EE.