Other, more direct, large-scale ways of identifying gene functions and associations (for example yeast two-hybrid methods) will grow in significance and with them the accompanying bioinformatics of functional genomics.
There will be a general shift in emphasis (of sequence analysis especially) from genes themselves to gene products.
This will lead to:
attempts to catalog the activities and characterize interactions between all gene products (in humans): proteomics );
attempts to crystallography and or predict the structures of all proteins (in humans): structural genomics.
What some people refer to as research or medical informatics, the management of all biomedical experimental data associated with particular molecules or patients---from mass spectroscopy, to in vitro assays to clinical side-effects---will move from the concern of those working in drug company and hospital I.T. (information technology) into the mainstream of cell and molecular biology and migrate from the commercial and clinical to academic sectors.
It is worth noting that all of the above post-genomic areas of research depend upon established, pre-genomic sequence analysis techniques.
此外该网站还特别提到了生物学与计算机科学之间奇妙的关系：生物大分子通常由结构简单的单体聚合而成（这点与计算机中用一些简单的语法编写一个具有独立功能的软件非常相似）；以及生物学对计算机科学的启发，例如遗传算法、（人工）神经网络的结构等。
It is a mathematically interesting property of most large biological molecules that they are polymers; ordered chains of simpler molecular modules called monomers. Think of the monomers as beads or building blocks which, despite having different colors and shapes, all have the same thickness and the same way of connecting to one another. Monomers that can combine in a chain are of the same general class, but each kind of monomer in that class has its own well-defined set of characteristics. And many monomer molecules can be joined together to form a single, far larger, macromolecule. Macromolecules can have exquisitely specific informational content and/or chemical properties. According to this scheme, the monomers in a given macromolecule of DNA or protein can be treated computationally as letters of an alphabet, put together in pre-programmed arrangements to carry messages or do work in a cell.
There are also whole other disciplines of biologically-inspired computation, e.g. genetic algorithms, AI, and neural networks. Often these areas interact in strange ways. Neural networks, inspired by crude models of the functioning of nerve cells in the brain, are used in a program called PHD to predict, surprisingly accurately, the secondary structures of proteins from their primary sequences.
2013年
【定义9】阿肯色大学小石城分校（University of Arkansas at Little Rock, UALR）在BIOINFORMATICS PROGRAM中对生物信息的解释：
As a discipline that builds upon the fields of computer and information science, bioinformatics relies heavily upon strategies to acquire, store, organize, archive, analyze, and visualize data.
As a discipline that builds upon computational biology, bioinformatics encompasses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems.
As a discipline that builds upon the life, health, and medical sciences, bioinformatics supports medical informatics; gene mapping in pedigrees and population studies; functional-, structural-, and pharmaco-genomics; proteomics, and dozens of other evolving “-omics.”
As a discipline that builds upon the basic sciences, bioinformatics depends on a strong foundation of chemistry, biochemistry, biophysics, biology, genetics, and molecular biology which allows interpretation of biological data in a meaningful context.
As a discipline whose core is mathematics and statistics, bioinformatics applies these fields in ways that provide insight to make the vast, diverse, and complex life sciences data more understandable and useful, to uncover new biological insights, and to provide new perspectives to discern unifying principles.
In short, bioinformaticians (or bioinformaticists) bring a multidisciplinary perspective to many of the critical problems facing the health-science profession today.
该定义从5个不同的方面，对生物信息学进行了解释：
建立在计算机和信息学科之上的生物信息学，侧重于数据的采集、存取、分析及可视化；
建立在计算生物学之上的生物信息学，侧重于数据分析和理论方法的开发，以及数学模型和计算机模拟技术在生物学研究中的应用；
建立在生命科学和医学之上的生物信息学，侧重于医学信息数据和各种不同的组学数据的分析；
建立在基础科学之上的生物信息学，侧重于在更基础的层面（化学结构、生化过程等）对生物学数据进行解释；
建立在数学和统计学之上的生物信息学，侧重于对大量、不同类型的复杂数据（例如高维数据或高度异质性的数据）进行分析；
从上面的定义来看，更加凸显了生物信息学的交叉学科属性。
2017年
【定义10】生物信息学家Dr. Maria Nattestad用下面的话向非科学家介绍自己的工作：
I use computers to analyze biological data.
在一篇博客中，她将生物信息学与数据科学进行了比较，发现它们非常相似：